Daily Tech Digest by Kannan Subbiah: business continuity

Showing posts with label business continuity. Show all posts

Daily Tech Digest - May 30, 2026

Quote for the day:

“Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” -- Martin Fowler

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Duration: 18 mins • Perfect for listening on the go.

AI-Driven Bug Tsunami Prompts Exploitability Questions

The article outlines how artificial intelligence has driven a massive increase in software bug reports, pushing the Common Vulnerabilities and Exposures system toward another record year. While major platforms like Chrome and GitHub have seen a large number of reported flaws, security researchers emphasize that most of these automated findings present very little real threat. Historically, fewer than two percent of all reported vulnerabilities are actually exploitable, and current telemetry indicates that only a tiny fraction are ever widely used by attackers. A primary issue is that automated tools often generate reports that lack necessary context regarding severity, practical reachability, and real world impact, creating an unnecessary administrative burden for software maintainers who must sort through low quality duplicates. In response, open source projects like the Linux kernel and platforms like GitHub have tightened their guidelines, now requiring functional proof of concept demonstrations before prioritizing a bug or issuing rewards. Furthermore, even advanced models like Anthropic’s Mythos, despite their ability to chain minor bugs into serious exploits, have not altered underlying risks significantly. Traditional security measures and defense in depth principles remain effective. By ensuring systems are built with multiple layers of security, organizations can ensure a single software flaw will not compromise an entire product.

AI and connected systems are forcing CIOs and COOs to rethink OT security

Historically, organizations kept operational technology, such as factory equipment and utility infrastructure, isolated from corporate IT networks to maintain security and safety. However, the search for efficiency has pushed companies to introduce connected sensors, cloud data, and artificial intelligence into these industrial spaces. While this change offers clear business advantages, it also creates significant cyber risks. Older operational equipment was never designed for internet connectivity, making standard software updates or sudden network shutdowns highly impractical. Furthermore, the integration of autonomous artificial intelligence systems complicates defense strategies because they constantly exchange data with outside networks while relying on legacy internal frameworks. To address these vulnerabilities, chief information officers and chief operating officers must move away from isolated management practices and embrace shared responsibility. This coordination is essential because typical corporate security tactics, like instantly isolating a compromised system, can disrupt manufacturing schedules or cause physical damage on the factory floor. Instead of trying to replace decades of old equipment immediately, leadership teams should focus on improving basic operational visibility, monitoring the network access of outside contractors, and deploying stricter identity verification checks. Taking a deliberate, phased approach to securing these blended environments allows companies to manage hidden threats much more effectively while keeping critical machinery running safely.

Accelerating Data Strategy and Governance with AI

According to a Dataversity article featuring insights from Peter Aiken, many organizations fail with their data strategies because they treat them as static documents to be completed and shelved rather than ongoing processes. Consequently, a vast amount of corporate data often remains redundant or obsolete. To fix this, an effective data strategy should serve as a continuous pattern of choices that aligns information assets directly with broader business goals. Aiken suggests utilizing a cyclical method focused on addressing constraints, where teams repeatedly isolate and resolve single bottlenecks to build small, incremental advantages. Data governance teams provide the necessary routine execution, though they frequently face common hurdles like cultural resistance, confusion, or competing technology priorities. Artificial intelligence serves as a practical tool to ease these operational burdens and expand human worker capabilities. Rather than replacing professionals, AI automates tedious administrative chores such as labeling data, mapping information lineage, checking security risks, and updating quality rules. This shift reduces internal friction and allows data stewards to spend their time on important strategic planning. Ultimately, combining cyclical improvements with automated support helps companies steadily improve their data quality, mitigate security risks proactively, and turn abstract strategy documents into practical business actions.

India has already witnessed increasing cyber targeting of critical infrastructure sectors

In this interview, Vaibhav Dutta of Tata Communications discusses the growing cybersecurity risks facing India’s critical infrastructure as industries embrace digital modernization. As sectors like energy, utilities, and manufacturing integrate isolated operational technology with enterprise IT, cloud networks, and automated systems, they inadvertently widen their exposure to external threats. This shift changes the nature of these threats from basic data breaches to complex physical disruptions capable of destabilizing essential public services. India has already seen an uptick in malware and remote access exploitation targeting its power grids and manufacturing setups. Dutta points out major vulnerabilities in current industrial upgrades, particularly a severe lack of visibility over legacy equipment, insecure remote access pathways, and unprotected application programming interfaces. Furthermore, many organizations mistakenly treat security as a compliance box to check rather than a core operational necessity. To mitigate these risks, the text advocates for building safety controls directly into systems during the initial planning stages of any digital expansion. Moving forward, safeguarding these interconnected environments will require a unified approach that blends traditional computer network security with physical operational safety, relying on continuous verification models and intelligent monitoring to detect anomalies and maintain continuity even during an active cyber attack.

The AI inventory is the EU AI Act artefact most teams underestimate

The Information Age article highlights why the AI inventory required by the EU AI Act is a critical component that corporate teams routinely underestimate. Rather than treating it as a superficial list or spreadsheet of active tools, organizations should view the inventory as a map that connects every artificial intelligence application to real business processes. A weak register merely names products like chatbots or analytics software. In contrast, a truly comprehensive inventory details business and technical owners, data inputs, intended outcomes, human review steps, and clear accountability trails. This deep level of clarity helps prevent the common issue of ownerless systems, where unmonitored technology leads to gradual shifts in purpose and completely untracked updates. While creating an inventory does not automatically ensure legal compliance or replace deeper security and privacy reviews, it establishes the necessary shared baseline record that different departments require to work together effectively. Technology executives play a central role here because standard legal or compliance teams rarely notice the automated features quietly embedded inside third-party corporate software platforms. Ultimately, maintaining a clear and current register enables legal, security, and operational units to understand exactly what they own, paving the way for structured risk management as new regulations phase in.

Kindness and Critical Infrastructure: Rethinking OT Security

In episode 52 of the Hack the Planet podcast, titled "Kindness and Critical Infrastructure," host Bryson Bort interviews Andrea Haddad, an infrastructure architect working at a pharmaceutical manufacturing organization. Haddad shares her transition from traditional IT network engineering to the world of operational technology, where safety and production take top priority. She highlights a common tension between maintaining strong security and ensuring daily workplace convenience. For example, forcing factory technicians to manage multiple complex passwords for remote access often leads to frustration and risky habits, like password reuse. Furthermore, external equipment suppliers frequently push back against corporate network rules, sometimes introducing unauthorized remote connections that create visibility blind spots. Haddad notes that while theoretical frameworks like the Purdue model offer helpful blueprints for layering networks and establishing equipment standards, strict solutions cannot be imposed instantly. Instead, she argues that lasting security relies heavily on mutual listening and empathy, choosing kindness over rigid enforcement. Because production downtime causes massive financial losses, security teams must understand the real-world constraints under which plant engineers operate. Ultimately, true system protection comes from a continuous process of learning, open communication, and building a practical middle ground that safeguards equipment without disrupting daily work.

How to Ideate in Design Thinking: What Works, What's Overhyped, and What's Changing

The Eleken article highlights that coming up with fresh product ideas is often misunderstood as a rigid, workshop-heavy process that smaller teams cannot afford. In reality, effective problem-solving is simply about pushing past the first few obvious choices, which are usually the same generic concepts your competitors have already considered. Traditional group brainstorming sessions frequently fall short because the loudest voices dominate the room, participants fear judgment, and early suggestions accidentally restrict everyone’s thinking. To bypass these social limitations, teams can use practical alternatives like the bad idea challenge, which removes performance pressure by asking people to deliberately invent terrible solutions that can later be flipped into useful features. Other effective approaches include studying solutions from completely unrelated industries or using imaginary scenarios to challenge basic assumptions. Furthermore, artificial intelligence is steadily changing how teams work by quickly producing hundreds of starting layouts and options. Instead of replacing human creativity, these software tools handle the heavy lifting of initial volume, allowing designers to dedicate their time to reviewing, editing, and perfecting the best directions. Ultimately, the article suggests treating design thinking as a flexible toolkit rather than a strict textbook rulebook, matching the core principles to actual product timelines and real-world project constraints.

Cloud spend is now a governance issue. Finance and IT need a new model

The article highlights the shifting nature of cloud and AI infrastructure costs, framing them not as a purely technical or financial problem, but as a critical governance challenge. Traditional static budgeting models and retroactive approvals fail to match the reality of modern cloud consumption, where expenses fluctuate dynamically based on daily engineering decisions and varying workload demands. Consequently, companies frequently deal with wasted spending, often due to overprovisioning or unutilized cloud resources. To solve this, finance and technology departments must work together more closely, adopting a shared framework commonly known as FinOps. This collaborative approach distributes financial accountability directly to product and business teams, linking cloud costs directly to performance and measurable business value. By establishing metrics like cost allocation coverage, forecasting accuracy, and unit economics, such as the cost per transaction or model inference, finance leaders gain deeper context into what their spending actually accomplishes. This visibility creates a shared understanding between engineering and corporate finance, helping teams make better everyday design choices. Ultimately, the text argues that companies focusing merely on reducing costs will struggle, whereas organizations that actively manage the business value of their cloud investments can turn structural volatility into a distinct operational advantage.

Stragglers, Not Failures: How Adaptive Hedged Requests Reduce p99 Latency by 74 Percent

This InfoQ article discusses how adaptive hedged requests can effectively manage extreme response delays in distributed computer networks. In large systems, overall performance is often slowed down not by outright errors, but by requests that eventually finish but take far longer than usual due to temporary glitches like background garbage collection or minor network bottlenecks. While software engineering teams often use retries to fix these issues, resending a slow request can accidentally overload an already struggling back-end server. Instead, a hedged request proactively sends a duplicate backup request if the initial attempt takes too long, accepting whichever response returns first and canceling the slower peer. To avoid the pitfalls of static timing limits, which require constant manual adjustments as traffic patterns shift throughout the day, the author introduces an automated system. By using an open-source statistical tracking tool called DDSketch, this setup continuously analyzes real-time response times to establish accurate thresholds naturally. Additionally, a built-in safety mechanism uses a token bucket budget to cap duplicate traffic, ensuring that the system handles problems gracefully rather than multiplying load during genuine outages. Ultimately, this approach works best for repeatable operations that do not change database state across multi-instance environments.

From resilience to survivability: How AI forces a rethink of business continuity

The article by Zeus Kerravala explains how artificial intelligence is changing corporate business continuity, pushing organizations to move past traditional recovery plans toward a model of continuous survivability. Historically, maintaining business operations during an unexpected network outage meant relying on simple secondary backups. However, these systems often share hidden technical dependencies, such as the same cloud providers or identity management tools. Because modern AI workloads are deeply interconnected and control real-time decision-making systems, any downtime creates severe immediate consequences and steep financial losses. To address these vulnerabilities, businesses are adopting architectural independence, which involves running separate, parallel environments with isolated data pathways and distinct operational teams. This approach ensures that a failure in the primary system does not spread to the backup. Furthermore, companies must view AI as both a major security risk and a helpful recovery asset. On one hand, automated models introduce supply chain risks and potential data corruption. On the other hand, they can predict infrastructure failures and trigger self-healing protocols. Ultimately, technology and enterprise leaders are advised to thoroughly map their complex system dependencies, test for total model failures, and transition from reactive troubleshooting to building autonomous safeguards that keep essential operations running smoothly during unexpected disruptions.

Daily Tech Digest - May 19, 2026.

Quote for the day:

“When you connect to the silence within you, that is when you can make sense of the disturbance going on around you.” -- Stephen Richards

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Duration: 21 mins • Perfect for listening on the go.

Why the best security investment a board can make in 2026 isn’t another tool

In this insightful opinion article, cybersecurity expert Jason Martin argues that the most valuable technological investment a corporate board can make is not purchasing another security tool, but rather achieving comprehensive environmental visibility. Traditionally, organizations respond to threats by adding specialized protection platforms, creating a heavily fragmented infrastructure where tools generate massive data but fail to provide unified context. Cybercriminals successfully exploit these operational seams, utilizing legitimate trust relationships or unmonitored human and machine credentials, including automated service accounts, API keys, and emerging AI agents, to bypass siloed defenses entirely without triggering network alerts. True visibility transcends raw logs and complex dashboards; it requires a complete, foundational map of all assets, user permissions, and systemic dependencies, enabling defense teams to reconstruct security incidents in minutes rather than weeks. This dangerous gap between overwhelming technical data and actual operational understanding is further exacerbated by rapid corporate AI adoption, which creates automated connections far faster than governance protocols can track. Therefore, Martin advises boards to shift away from merely asking if they are protected. Instead, corporate leadership must critically ask what their defense teams can actually see, establishing a complete inventory baseline before adding more top-tier detection layers. Drawing this definitive organizational blueprint builds the necessary foundation for absolute, long-term cyber resilience.

CI/CD Was Built for Deterministic Software — Agents Just Broke the Model

The article argues that traditional continuous integration and continuous delivery or CI/CD pipelines, which were built under the assumption of deterministic software repeatability where identical inputs yield identical results, are being disrupted by the rise of agentic artificial intelligence. Because AI agents introduce variance as a core feature by dynamically reasoning, selecting tools, and altering behaviors based on shifting contexts, the conventional binary testing framework of green or red dashboards is no longer sufficient. Instead, DevOps teams must shift to statistical testing methodologies involving comprehensive evaluation sets, scenario libraries, and drift detection. Furthermore, operational management becomes significantly more complex; rolling back systems shifts from reverting a stable binary to unraveling an unpredictable, interconnected chain of decisions and tool interactions. Provenance and observability must also evolve to track prompts, policy configurations, and behavioral intent rather than basic system error codes. Ultimately, traditional deployment models are not entirely obsolete, but they must expand through platform engineering to provide shared governance, simulation environments, and robust guardrails. This extension ensures that autonomous agents can be safely deployed, monitored, and kept within specified organizational boundaries, transforming the ultimate goal of modern DevOps pipelines from merely shipping software to definitively proving and verifying acceptable autonomous behavior.

Why blockchain will be vital for the next generation of biometrics

In this article, Thomas Berndorfer, the CEO of Connecting Software, discusses how blockchain technology will become vital for protecting next generation digital identity and biometric verification systems against sophisticated artificial intelligence driven document manipulation. This pressing cyber threat was underscored by a massive banking scandal in Australia, where sophisticated fraudsters leveraged advanced tools to subtly modify legitimate income records and fraudulently secure billions in loans. Berndorfer emphasizes that while modern biometric passports incorporate strong protections, secondary documentation used for identity verification, such as housing contracts and pay stubs, remains highly susceptible to subtle, undetectable alterations. To effectively mitigate this vulnerability, incorporating a decentralized public blockchain enables issuing organizations to lock digital files with an immutable cryptographic hash, known colloquially as a blockchain seal. Any subsequent modification to the original file yields a completely mismatched hash value, instantly exposing unauthorized tampering to third party verifiers while preserving user privacy by only exposing the hash rather than sensitive underlying personal data. However, the author cautions that blockchain is not a standalone solution; it requires initial issuer sealing at source, cannot identify precisely what information was changed, and fails to differentiate between harmless filename updates and dangerous fraudulent text alterations.

Expanding the Narrative of Business Continuity History

In the article "Expanding the Narrative of Business Continuity History" published in the Disaster Recovery Journal, Samuel McKnight argues that the business continuity and resilience profession possesses a much deeper historical foundation than standard narratives suggest. While traditional accounts trace the discipline’s origins to mainframe computing in the 1960s, followed by programmatic advancements surrounding IT disaster recovery, 9/11, and COVID-19, McKnight uncovers century-old roots through a personal investigation into his great-grandfather’s vintage steel desk. Manufactured by the General Fireproofing Company around 1930, the heirloom led him to a 1924 trade catalogue that passionately advocated for proactively protecting paper business records from devastating urban fires, such as the 1906 San Francisco conflagration. McKnight highlights how this early twentieth-century value proposition, which treated vital documents as the "very breath" of an enterprise's existence, closely mirrors contemporary business continuity management and operational resilience strategies. Ultimately, the author emphasizes that reconstructing this rich history provides modern practitioners with a profound sense of purpose and vocational grounding. It demonstrates that the core mandate of organizational preparedness is not a novel concept but a multi-generational legacy, which continually adapts its protective methods to mitigate systemic vulnerabilities as technology and corporate infrastructure evolve over time.

What is a data architect? Skills, salaries, and how to become a data framework master

The article provides a comprehensive overview contrasting virtual and physical firewalls within modern, dynamic network architectures. Virtual firewalls are software-based security solutions operating on shared compute infrastructure, such as hypervisors, public cloud platforms, and container environments. By decoupling security features from dedicated hardware, they offer programmatic deployment agility, horizontal scaling, and crucial east-west visibility to inspect lateral traffic moving within an environment. However, because they are CPU-bound, virtual instances can experience performance bottlenecks during compute-intensive tasks like high-volume TLS inspection. Conversely, physical firewalls are dedicated hardware appliances built with purpose-designed processors like ASICs. Installed at fixed perimeters, local data centers, or branch offices, they deliver highly predictable, hardware-accelerated throughput for north-south traffic. They remain indispensable for air-gapped systems or strict data sovereignty regulations, though their fixed capacity requires longer procurement and cannot natively follow workloads into public clouds. Ultimately, the article emphasizes that neither solution is universally superior. Instead, most organizations benefit by blending both into a unified hybrid mesh architecture managed through a centralized interface. This holistic approach utilizes physical appliances at high-bandwidth boundaries while deploying virtual firewalls inside cloud infrastructure, ensuring consistent security policies, preventing dangerous policy drift, and reducing management costs across the global network fabric.

Capabilities-Driven Application Modernization: Business Value at Every Step

The article by Melissa Roberts explores how organizations can transition application modernization from strategy to practice using a deliberate, data-driven framework. Rather than rebuilding every application blindly, which often leads to costly failures, companies should use a business capability model paired with a capability heatmap to assess the value, performance, and risk of their operations. Business capabilities are categorized into strategic, core, and supporting layers to help prioritize investments where technology genuinely differentiates the business. Furthermore, the framework requires aligning domains to these capabilities, creating a cross-functional structure that breaks down technical silos. Following Conway's Law, this alignment ensures technical architectures match internal communication patterns, promoting the use of bounded contexts to minimize accidental complexity and avoid monolithic coupling. A domain heatmap visually points executives toward critical, underperforming capabilities that need higher investment, while protecting adequately performing areas from unnecessary spending. Companies often fail when they neglect to connect distinctive capabilities with their corresponding problem domains and underlying technologies. Ultimately, establishing this capability-driven alignment ensures stakeholders realize clear business outcomes, maximizing return on investment while preventing organizations from hemorrhageing capital on redundant or non-essential application modernization initiatives.

Beyond Crisis Management: Why Scenario Planning Must Become a Regular Operating Discipline

The article argues that traditional scenario planning, once treated as a static, annual ritual dominated by hypothetical workshops, is no longer sufficient in an era marked by deep geopolitical fragmentation and supply chain shocks. Modern scenario planning must instead evolve into a continuous, data-driven operating rhythm deeply embedded across core functions like procurement, treasury, logistics, and technology. The strategic focus has shifted from trying to predict exact future outcomes to building collective agility that minimizes organizational paralysis during abrupt changes. To bridge the gap between boardroom discussions and execution, successful multinational enterprises now utilize trigger-based escalation frameworks. By anchoring abstract scenarios to specific, measurable indicators—such as freight thresholds, inventory buffer levels, or shipping delays—organizations can automatically execute predetermined actions before a crisis fully materializes. Furthermore, corporate leadership and investors are reframing resilience as a vital commercial asset, moving scenario mapping into capital allocation and strategic investment decisions. Ultimately, building a resilient enterprise requires cultivating an internal culture that normalizes uncomfortable conversations, encourages leaders to challenge deep-seated assumptions, and treats risk functions not as passive compliance units, but as strategic interpreters of systemic uncertainty.

Bridging Gaps in SOC Maturity Using Detection Engineering and Automation

The DZone article asserts that true Security Operations Center (SOC) maturity requires maintaining a stable, continuous feedback loop where threat detection and response are systematically governed, measured, and optimized. Organizations frequently suffer from uneven operational maturity, where a massive accumulation of raw logs outpaces data normalization capabilities and overwhelms analysts with alert noise. To close these gaps, the article advocates treating detection engineering as a robust control plane. Rather than relying on brittle, static alerts, teams should treat detections as portable, version-controlled software artifacts—such as Sigma rules—backed by explicit telemetry contracts. This systematic structure cleanly separates rule defects from underlying data quality failures. Automation further scales this cycle by introducing programmatic, pre-deployment quality gates and standardizing responses via frameworks like OpenC2, STIX, and TAXII. Instead of using automation to aggressively suppress noisy alerts—which frequently masks the root causes of risks—mature automation enforces behavioral consistency, quality thresholds, and precise telemetry validation before accelerating execution. Ultimately, shifting to an artifact-driven model protects system transparency, prevents operational debt, and alleviates downstream queue pressure. This structural evolution successfully transitions analyst workloads away from repetitive manual triage and allows them to focus on high-value, threat-informed threat hunting and investigation.

Context architecture is replacing RAG as agentic AI pushes enterprise retrieval to its limits

The VentureBeat article outlines a structural transition in enterprise AI infrastructure, where traditional Retrieval-Augmented Generation (RAG) pipelines are being replaced by context architectures. Standard RAG frameworks, which pre-load data into pipelines before model execution, are failing because autonomous AI agents generate vastly larger, continuous data requests than human users. This scale mismatch leaves data scattered and stale. Enterprise buyers are shifting toward custom, hybrid retrieval stacks that flip the paradigm, enabling agents to dynamically pull live, governed, low-latency context at runtime using Model Context Protocol (MCP) tool calls. In response to these market demands, companies like Redis have introduced platforms like Redis Iris. This context and memory platform provides real-time data integration, short- and long-term state tracking, and semantic interfaces while utilizing highly cost-effective storage technologies like Redis Flex to run data on flash. Analyst and market data confirm that retrieval optimization has overtaken evaluation as the top enterprise investment priority. Ultimately, the successful scaling of agentic AI depends on implementing these unified context layers to ensure data is fresh, secure, and cost-efficient, allowing multiple specialized agents to interact simultaneously without causing backend system strain or governance risks.

Can EU AI Act actually regulate models like Mythos?

The Silicon Republic article explores the regulatory challenges surrounding frontier AI models, focusing on Anthropic's powerful "Mythos" system. Discovered as an unintentional byproduct of coding and autonomy improvements, Mythos has triggered global security discussions due to its defensive capabilities and potential systemic cyber risks. This disruption has heavily strained start-ups and SMEs, which face immense pressure to constantly patch digital products and services. Joseph Stephens, director of resilience at Ireland's National Cyber Security Centre (NCSC), emphasizes that individual states have limited power to block independent, US-based rollouts. Consequently, the EU and member nations are seeking a highly coordinated regulatory framework. While the EU AI Act includes provisions designed to mitigate systemic dangers and offensive cyber capabilities, its practical application remains restricted by geographical bounds. Legal expert Dr. TJ McIntyre notes that the extraterritorial regulation of models like Mythos is only possible if the systems or their outputs are directly sold within the European Union. If Anthropic uses geo-restricting measures to block availability inside the bloc, enforcement under the Act becomes deeply uncertain. Ultimately, while the AI Act represents a groundbreaking attempt to police advanced software marketplaces safely, officials acknowledge that governments cannot entirely regulate their way out of accelerating technological advancements.

Daily Tech Digest - April 30, 2026

Quote for the day:

"You've got to get up every morning with determination if you're going to go to bed with satisfaction." --George Lorimer

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Duration: 15 mins • Perfect for listening on the go.

The dreaded IT audit: How to get through it and what to avoid

The article "The dreaded IT audit: how to get through it and what to avoid" from IT Pro encourages organizations to reframe the auditing process as a strategic business asset rather than a burdensome cost center. Successfully navigating an audit requires maintaining a comprehensive, up-to-date inventory of all technology assets—including those used by remote workforces—to ensure security, safety, and insurance compliance. Even startups should establish structured auditing processes, as these evaluations proactively identify vulnerabilities and optimize operational efficiency. To streamline the experience, the article recommends prioritizing high-risk areas, such as software licensing, and utilizing customized spot checks instead of repetitive, standardized reviews that may fail to uncover meaningful insights. Crucially, leaders must adopt an open-minded approach to findings; the goal is to engage in transparent discussions about discovered issues rather than becoming defensive. Key pitfalls to avoid include treating the audit as a one-time administrative hurdle, relying on outdated manual tracking methods, and ignoring the gathered data. Instead, organizations should leverage audit results to inform staff training and drive practical improvements. By viewing the audit as a strategic opportunity for growth, companies can significantly strengthen their cybersecurity posture and ensure long-term sustainability in a digital economy.

Privacy in the AI era is possible, says Proton's CEO, but one thing keeps him up at night

In a wide-ranging interview at the Semafor World Economy Summit, Proton CEO Andy Yen addressed the critical tension between the rapid advancement of artificial intelligence and the fundamental right to digital privacy. Yen voiced significant concerns regarding the current AI trajectory, arguing that the industry's reliance on massive data harvesting inherently threatens individual security. He advocated for a paradigm shift toward "privacy-first AI," where processing occurs locally on user devices or through end-to-end encrypted frameworks to ensure that personal information remains inaccessible to service providers. Unlike the advertising-driven models of Silicon Valley giants, Yen highlighted Proton’s commitment to a subscription-based business model, which avoids the ethical pitfalls of monetizing user data. He also explored the "privacy paradox," observing that while users value their data, they often succumb to the convenience of free platforms. To counter this, Proton is expanding its ecosystem with tools like encrypted email and small language models designed specifically for security. Ultimately, Yen emphasized that the future of the digital economy hinges on stricter regulatory enforcement and the adoption of decentralized technologies that empower users with absolute control over their information, rather than treating them as products to be sold.

Outsourcing contracts weren't built for AI. CIOs are renegotiating now

The rapid advancement of generative artificial intelligence is necessitating a major overhaul of IT outsourcing agreements, as traditional contracts centered on headcount and billable hours prove incompatible with AI-driven efficiency. This InformationWeek article explains that while service providers promise productivity gains of up to 70%, legacy full-time equivalent (FTE) models fail to account for this increased output, leading CIOs to aggressively renegotiate for outcome-based pricing. This shift allows organizations to pay for specific results rather than human time, yet it introduces significant legal complexities. Key concerns include data sovereignty—where proprietary data might inadvertently train a provider's large language model—and intellectual property risks regarding the ownership of AI-generated code. Furthermore, the ability of AI to automate routine tasks is prompting some enterprises to bring previously outsourced functions back in-house, as smaller internal teams can now manage workloads that once required massive offshore cohorts. To navigate these challenges, technical leaders are implementing "gain-sharing" frameworks and rigorous governance standards to manage risks like AI hallucinations and liability. Ultimately, CIOs are assuming a more central role in procurement to ensure that vendor incentives align with genuine innovation and that the financial benefits of automation are captured by the enterprise.

Bad bots make up 40% of internet traffic

The "2026 Thales Bad Bot Report: Bad Bots in the Agentic Age" reveals a transformative shift in internet traffic, where automated activity now accounts for 53% of all web interactions, surpassing human traffic for the second consecutive year. Malicious "bad bots" alone comprise 40% of global traffic, highlighting a growing threat landscape. A critical finding is the 12.5x surge in AI-driven bot attacks, fueled by the rapid adoption of agentic AI which blurs the lines between legitimate and harmful automation. These advanced bots are increasingly targeting APIs, with 27% of attacks now bypassing traditional interfaces to exploit backend logic directly at machine speed. The financial services sector remains the most vulnerable, suffering 24% of all bot attacks and nearly half of all account takeover incidents. Thales experts, including Tim Chang, emphasize that the primary security challenge has evolved from simple bot identification to the complex analysis of behavioral intent. As AI agents emerge as a new traffic category, organizations must transition to proactive, intent-based defenses that can distinguish between helpful AI agents and malicious automation. This machine-driven era necessitates deeper visibility into API traffic and identity systems to maintain trust and security across modern digital infrastructures.

Incentive drift: Why transformation fails even when everything looks green

In the article "Incentive Drift: Why Transformation Fails Even When Everything Looks Green," Mehdi Kadaoui explores the paradoxical failure of IT transformations that appear successful on paper. The central challenge is "incentive drift"—the structural separation of authority from accountability that leads organizations to optimize for project delivery rather than business value. This drift manifests through several destructive patterns: the "ownership vacuum," where strategy and execution are disconnected; the "budgetary firewall," which isolates capital spending from operational costs; and "language capture," where success definitions are subtly redefined to ensure "green" status. Kadaoui argues that "collective amnesia" often follows, as organizations quietly lower their expectations to avoid acknowledging failure. To resolve this, he proposes making drift "structurally expensive" through three key mechanisms. First, a "value prenup" requires operational leaders to explicitly own and sign off on intended outcomes before development begins. Second, a "cost mirror" forces transparency across budget ledgers. Finally, a "semantic anchor" ensures original goals are read aloud in every governance meeting to prevent meaning erosion. By grounding digital transformation in rigid accountability and linguistic clarity, leadership can ensure that technological outputs translate into genuine, durable enterprise value.

How to Be a Great Data Steward: 6 Core Skills to Build

The article "Core Data Stewardship Skills to Build" emphasizes that effective data stewardship requires a unique blend of technical proficiency, business acumen, and interpersonal skills. High-performing stewards act as "purple people," bridging the gap between IT and business by translating complex technical standards into actionable business practices. Key operational activities include identifying and documenting Critical Data Elements (CDEs), aligning them with precise business terms, and performing data profiling to identify quality issues. Beyond basic documentation, stewards must master data classification to ensure regulatory compliance with frameworks like GDPR or HIPAA. Analytical thinking is essential for interpreting patterns and uncovering root causes of data inconsistencies, while strong communication skills enable stewards to foster a collaborative, data-driven culture. Furthermore, literacy in adjacent domains such as metadata management, master data management (MDM), and the use of modern data catalogs is vital. Ultimately, the role is outcome-driven; stewards do not just manage data for its own sake but focus on ensuring data health to drive measurable organizational value. By combining attention to detail with strategic consistency, data stewards serve as the essential operational guardians who transform raw data into a reliable, high-quality strategic asset for their organizations.

Researchers unearth industrial sabotage malware that predated Stuxnet by 5 years

Researchers from SentinelOne recently uncovered a sophisticated malware framework, dubbed "Fast16," that predates the infamous Stuxnet worm by five years. Active as early as 2005, this discovery shifts the timeline of state-sponsored industrial sabotage, proving that nation-states were deploying cyberweapons against physical infrastructure much earlier than previously understood. Unlike typical espionage tools designed for data theft, Fast16 was engineered for strategic sabotage by targeting high-precision floating-point arithmetic operations within engineering modeling software. By corrupting the logic of the Floating Point Unit (FPU), the malware produced subtly altered outputs in complex simulations, potentially leading to catastrophic real-world failures. The researchers identified three specific targeted engineering programs, including one previously associated with Iran’s AMAD nuclear program and another widely used in Chinese structural design. The modular nature of Fast16, which utilizes encrypted Lua bytecode, underscores its advanced design and national importance. This finding highlights a historical precedent for cyberattacks on critical workloads in fields such as advanced physics and nuclear research. Ultimately, Fast16 serves as a significant harbinger for modern industrial sabotage, demonstrating that the transition from strategic espionage to physical disruption in cyberspace was already in full swing two decades ago, long before Stuxnet gained global notoriety.

How AI Is Transforming Business Continuity and Crisis Response

Charlie Burgess’s article, "How AI Is Transforming Business Continuity and Crisis Response," explores the pivotal role of artificial intelligence in navigating the complexities of modern digital and physical risks. As businesses face increasingly non-linear threats, from supply chain disruptions to cyber incidents, the abundance of generated data often leads to information overload. AI addresses this by acting as a sophisticated data analysis tool that parses vast information streams to identify hidden patterns and suppress low-priority noise. This allows crisis teams to focus on critical alerts and early warning signs. Furthermore, AI enhances situational awareness and coordination by correlating disparate system inputs and surfacing standardized playbook responses. During active incidents, technologies like AI-powered cameras provide real-time visibility, aiding in personnel safety and evacuation efforts. Beyond immediate response, AI suggests optimized recovery paths and strategic resource allocation, fostering long-term operational resilience. Ultimately, the integration of AI is not intended to replace human judgment but to empower decision-makers with actionable insights and agility. By bridging the gap between data collection and decisive action, AI transforms business continuity from a reactive necessity into a proactive, evidence-based strategic asset that safeguards both personnel and organizational stability in an unpredictable global landscape.

Europe Gliding Toward Mandatory Online Age Verification

The European Commission is accelerating its push toward mandatory online age verification, driven by the Digital Services Act's requirements to protect minors from harmful content. Central to this initiative is a new age assurance framework and a "technically ready" open-source mobile app designed to allow users to prove they are over a certain age using national identity documents without disclosing their full identity. However, this transition faces intense scrutiny. Security researchers recently identified significant vulnerabilities in the commission's prototype app, labeling it "easily hackable." Furthermore, privacy advocates, such as representatives from Tuta, warn that centralized age verification creates a lucrative "gold mine" for hackers, potentially exacerbating risks like phishing and identity theft. Despite these concerns, European officials like Henna Virkkunen emphasize that the DSA demands concrete action over mere terms of service, particularly following allegations that platforms like Meta have failed to adequately exclude children under thirteen. As several European nations consider raising minimum age requirements for social media, the commission continues to advocate for "robust and non-discriminatory" verification tools that can be integrated into national digital wallets, insisting that ongoing security testing will eventually yield a reliable solution for safeguarding the digital environment for children.

CodeGuardian: A Model Context Protocol Server for AI-Assisted Code Quality Analysis and Security Scanning

"CodeGuardian: A Model Context Protocol Server for AI-Assisted Code Quality Analysis and Security Scanning" introduces a breakthrough tool designed to integrate enterprise-grade security and quality checks directly into AI-powered development environments. Authored by Madhvesh Kumar and Deepika Singh, the article details how CodeGuardian leverages the Model Context Protocol (MCP) to extend coding assistants with eleven specialized analysis tools. This integration eliminates the friction of context-switching by allowing developers to execute security scans, identify hardcoded secrets across multiple layers, and generate compliant Software Bill of Materials (SBOM) using simple natural language prompts. Unlike traditional static analysis tools that merely flag issues, CodeGuardian provides context-aware, "drop-in" code remediations tailored to a project's specific framework and style. A core feature is its cross-layer security reporting, which aggregates findings into a single risk score, exposing systemic vulnerabilities that isolated scanners often miss. By shifting security "left" into the immediate coding workflow, the tool empowers developers to build more resilient software while maintaining high delivery velocity. Ultimately, CodeGuardian represents a pivot toward "agentic" security, where AI assistants act as proactive guardians of code integrity throughout the development lifecycle, effectively bridging the gap between rapid feature delivery and robust organizational compliance.

Daily Tech Digest - February 01, 2026

Quote for the day:

"Successful leadership requires positive self-regard fused with optimism about a desired outcome." -- Warren Bennis

Forget the chief AI officer - why your business needs this 'magician

There's a lot of debate about who should be responsible for ensuring the business makes the most out of generative AI. Some experts suggest the CIO should oversee this crucial role, while others believe the responsibility should lie with a chief data officer. Beyond these existing roles, other experts champion the chief AI officer (CAIO), a newcomer to the C-suite who oversees key considerations, including governance, security, and identification of potential use cases. ... Many people across other business units are confused about the different roles of technology and data teams. When Panayi joined Howden in August last year, he decided to head off that issue at the pass. ... "I think companies are missing a trick if they've not got someone ensuring that people are using things like Copilot and so on. These tools are new enough that we do need people to help with adoption," he said. "And at the moment, I don't think we can assume the narrative is correct that people using AI at home to help them book holidays is the same as how it can help them be more productive at work." ... "It's like he's a magician, showing people who have to deal with thousands of pages of stuff, how to get the answers they need quickly," he said, outlining how the director of productivity highlights the benefits of gen AI to the firm's brokers. "These people are not at the computer all day. They are out in the market, talking and making decisions."

Just Relying on Data Doesn’t Make You Data-driven — Advantage Solutions CDO

O’Hazo then draws a line between measurement and transformation. Success in data programs, she explains, is not only about performance indicators; it is also about whether the organization is starting to internalize the mindset behind them. “Success for me in this data and AI space is all about, ‘Are my stakeholders starting to actually speak some of my language?’” When stakeholders begin to “believe” and “trust,” she says, the shift becomes visible not only in outcomes but also in demand. The moment data starts becoming embedded in the business is the moment the need for the CDO office outgrows its capacity. ... She ties true data-driven maturity to operational efficiency and responsiveness: Accurate, timely information; Faster decision-making cycles; Quicker reactions to market conditions; and Lower effort to extract value from data. In her view, strong data foundations should reduce friction instead of creating new burdens. Speed, however, is not just about moving fast, it’s about winning the race to insight. “Once you have that foundation built, to get to the answer quickly, you have to be the first one there. If you’re not the first one there, you’ve lost.” ... As the conversation returns to the governance part of transformation, O’Hazo underscores that governance becomes sustainable only when people are comfortable using data and confident enough to surface risks early. For her, the true differentiator is not policy; it is talent and environment.

The Three Mindsets That Shape Your Life, Work And Fulfillment

Mission Mindset is goal-oriented but not outcome-obsessed. It begins with clarity about a specific, measurable and time-bound goal. Decades of research on goal-setting, including the work of Stanford psychologist Carol Dweck, shows that how we interpret challenges influences how we engage with them—and that mindset creates very different psychological worlds for people facing the same obstacles. Here's where most people go wrong. ... If mission provides direction, identity provides stability. Identity Mindset is rooted in a healthy, coherent self-image that does not rise and fall with every outcome. It answers a deeper question: Who am I when the going gets tough or disappointment abounds? Many people identify with their performance. Success feels like validation, and failure feels personal. That volatility makes progress emotionally expensive because every result threatens their self-worth. In contrast, PsychCentral broadly defines resilience as adapting well to adversity; individuals who are stable in how they see themselves are better able to regulate emotions, process setbacks and continue forward without losing themselves in the struggle. ... Agency Mindset is where actual momentum lives. It is the lived belief that you are the author of your life, not a character reacting to circumstances. Agency does not deny reality or minimize hardship. It refuses to play the victim, make excuses or place blame.

Why We Can’t Let AI Take the Wheel of Cyber Defense

When we talk about fully autonomous systems, we are talking about a loop: the AI takes in data, makes a decision, generates an output, and then immediately consumes that output to make the next decision. The entire chain relies heavily on the quality and integrity of that initial data. The problem is that very few organizations can guarantee their data is perfect from start to finish. Supply chains are messy and chaotic. We lose track of where data originated. Models drift away from accuracy over time. If you take human oversight out of that loop, you aren’t building a better system; you are creating a single point of systemic failure and disguising it as sophistication. ... There is no magical self-healing feature that puts everything back together elegantly. When a breach happens, it is people who rebuild. Engineers are the ones trying to deal with the damage and restoring services. Incident commanders are the ones making the tough calls based on imperfect information. AI can and absolutely should support those teams—it’s great at surfacing weak signals, prioritizing the flood of alerts, or suggesting possible actions. But the idea that AI will independently put the pieces back together after a major attack is a fantasy. ... So, how do we actually do this? First, make “human-in-the-loop” the default setting for any AI that can act on your systems or data. Automated containment can save your skin in the first few seconds of an attack, but every autonomous process needs guardrails.

Connecting the dots on the ‘attachment economy’

In the attention economy paradigm, human attention is a currency with monetary value that people “spend.” The more a company like Meta can get people to “spend” their attention on Instagram or Facebook, the more successful that company will be. ... Tristan Harris at the Center for Humane Technology coined the phrase “attachment economy,” which he criticizes as the “next evolution” of the extractive-tech model; that’s where companies use advanced technologies to commodify the human capacity to form attached bonds with other people and pets. In August, the idea began to gain traction in business and academic circles with a London School of Economics and Political Science blog post entitled, “Humans emotionally dependent on AI? Welcome to the attachment economy” by Dr. Aurélie Jean and Dr. Mark Esposito. ... The rise of attachment-forming tech is similar to the rise in subscriptions. While posting an article or YouTube video may get attention, getting people to subscribe to a channel or newsletter is better. It’s “sticky,” assuring not only attention now, but attention in the future as well. Likewise, the attachment economy is the “sticky” version of the attention economy. Unlike content subscription models, the attachment idea causes real harm. It threatens genuine human connection by providing an easier alternative, fostering addictive emotional dependencies on AI, and exploiting the vulnerabilities of people with mental health issues.

From monitoring blind spots to autonomous action: Rethinking observability in an Agentic AI world

AI-supported observability tools help teams not only understand system performance but also uncover the reasons behind issues. By linking signals across interconnected parts, these tools provide actionable insights and usually resolve problems automatically, reducing Mean Time to Resolution (MTTR) and cutting the risk of outages. ... AI-driven observability can trace service dependencies from start to finish, connect signals across third-party platforms, and spot early signs of unusual behavior. By examining traffic patterns, error rates, and configuration changes in real-time, observability helps teams identify emerging issues sooner, understand the potential impact quickly, and respond before full disruptions occur. While observability cannot prevent every third-party outage, it can greatly reduce uncertainty and response time, allowing solutions to be introduced sooner and helping rebuild customer trust. ... When AI-driven applications fail, teams often lack clear visibility into what went wrong, putting significant AI investments at risk. Slow or incorrect responses turn troubleshooting into guesswork, as teams struggle to understand agent interactions, find delays, or identify the responsible agent or tool. This lack of clarity slows down root-cause analysis, extends downtime, diverts engineering efforts from innovation, and can ultimately lead to lost revenue and customer trust. Observability addresses this challenge by providing complete visibility into AI application behavior.

Architecture Testing in the Age of Agentic AI: Why It Matters Now More Than Ever

Historically, architecture testing functioned as a safeguard against emergent complexity in distributed systems. Whenever an organization deployed a network of interdependent services, message buses, caches, and APIs, the potential for unforeseen interactions grew. Even before AI entered the picture, architects confronted the reality that large systems behave in ways no single engineer fully anticipates. ... Agentic systems challenge traditional testing practices in several fundamental ways. First, these systems are inherently non‑deterministic. A test that succeeds at 9:00 might fail just minutes later simply because the agent followed a different reasoning path. This creates a widening ‘verification gap,’ where deterministic enterprise systems and probabilistic, adaptive agents operate according to fundamentally different reliability expectations. Second, these agents operate within environments that are constantly shifting—APIs, user interfaces, databases, and document stores all evolve independently of the agent itself. Because agents are expected to detect these changes and adapt their behavior, long‑held architectural assumptions about stability and interface contracts become far more fragile. ... Third, agentic AI introduces a new level of emergent behavior. Operating through multi‑step reasoning loops and tool interactions, agents can develop strategies or intermediate actions that were never explicitly designed or anticipated. While emergence has always existed in complex distributed systems, with agents it becomes the rule rather than the exception.

Data Privacy Day warns AI, cloud outpacing governance

Kornfeld commented, "Data Privacy Day is a reminder that protecting sensitive information requires consistent discipline, not just policies. This discipline starts with infrastructure choices. As organizations continue to evaluate cloud-first strategies, many are also reassessing where their most critical data should live. For workloads that demand predictable performance, strong governance and clear ownership, on-site infrastructure continues to play an essential role in a sound privacy strategy." ... Russel said, "Data Privacy Day often prompts the usual reminders: update policies, refresh consent language, and train staff on security and resilience strategies. These are important steps, but increasingly they are simply the baseline. In 2026, the board-level question leaders should also be asking is: can we demonstrate control of personal data and sustain trust through disruption, whether it stems from a compromise, misconfiguration, insider error, or a supplier incident?" ... Russell commented that identity controls and response processes sit at the core of this shift as attackers continue to exploit account compromise to reach sensitive information in cloud environments. "Identity is a privacy fault line. In cloud environments, compromised identities are often the fastest route to sensitive data. Resilience means detecting abnormal access early, limiting blast radius, and recovering confidently when identity controls are bypassed."

Security teams are carrying more tools with less confidence

Security leaders express mixed views about the performance of their SIEM platforms. Most say their SIEM contributes to faster detection and response, yet only half describe that contribution as strong. Confidence in long-term scalability follows a similar pattern, with many teams expressing partial confidence as data volumes and monitoring demands continue to grow. Satisfaction with log management and security analytics tools mirrors this split. Teams that express higher satisfaction also report stronger alignment between their tooling and application environments. ... Threat detection represents the most common use of AI and machine learning within security operations. Fewer teams apply AI to incident triage, automated response, or anomaly detection. Despite this limited scope, security leaders consistently associate AI with reduced alert fatigue and improved signal quality. Many also prioritize AI capabilities when evaluating SIEM platforms, alongside real-time analytics. ... Security leaders frequently describe operational cost as a top pain point. Multiple point solutions contribute to overlapping capabilities, siloed data, and increased alert noise. Data that remains isolated across tools complicates threat analysis and slows investigations, particularly when teams attempt to reconstruct activity across cloud, identity, and application layers.

Integrating Financial Counterparty Risk into Your Business Continuity Plan

Vendor defaults and liquidity issues can disrupt operations in ways that ripple across departments and delay recovery. If a key financial partner fails, access to working capital, credit or critical services can disappear overnight. For example, if your leasing company collapses, essential equipment could be repossessed, or service agreements could lapse. ... Financial counterparties show up across many areas of your business. You depend on banks for credit facilities and insurers for risk transfer. Payment processors, brokers and pension custodians handle everything from daily cash flow to long-term employee benefits. Clearinghouses are also vital in structured markets, such as stocks and futures. They sit between buyers and sellers to ensure both sides honor their contracts, which reduces your exposure to failure during high-volume or high-volatility periods. ... Not all financial counterparties pose the same level of risk, but the warning signs often follow familiar patterns. Monitoring a few high-impact indicators can help you identify problems and take action before disruptions escalate. ... Industry standards are raising the bar on how you manage financial counterparties. Frameworks like ISO 22301 stress the need to include financial dependencies in your continuity and risk programs. These standards define how regulators and stakeholders expect you to identify, assess and respond to financial exposure. If you treat financial partners like background support, you risk missing vulnerabilities that could surface under pressure.

Daily Tech Digest - January 15, 2026

Quote for the day:

"You have to have your heart in the business and the business in your heart." -- An Wang

AI agents can talk — orchestration is what makes them work together

“Agent-to-agent communications is emerging as a really big deal,” ’s chief innovation officer told . “Because if you don't orchestrate it, you get misunderstandings, like people speaking foreign languages to each other. Those misunderstandings reduce the quality of actions and raise the specter of hallucinations, which could be security incidents or data leakage.” ... In another critical evolution in the agentic era, human evaluators will become designers, moving from human-in-the-loop to human-on-the-loop, according to Sanders. That is: They will begin designing agents to automate workflows. Agent builder platforms continue to innovate their , Sanders said, meaning nearly anyone can now stand up an agent using . “This will democratize , and the super skill will be the ability to express a goal, provide context and envision pitfalls, very similar to a good people manager today.” ... Organizations should begin “expeditious programs” to infuse agents across workflows, especially with highly repetitive work that poses bottlenecks. Likely at first, there will be a strong human-in-the-loop element to ensure quality and promote change management. “Serving as an evaluator will strengthen the understanding of how these systems work,” Sanders said, “and eventually enable all of us to operate upstream in agentic workflows instead of downstream.”

Integrating AI-Enhanced Microservices in SAFe 5.0 Framework

can be a game-changer for within SAFe. By optimizing decision analytics and enhancing value stream performance, AI simplifies, rather than complicates. I know what you’re thinking: AI tools can add complexity. One client put this to the test, and we found AI helped reduce the noise. It sliced through the data smog to identify hidden and automate mundane tasks like financial forecasting and risk management. ... Integrating decentralized AI models into SAFe’s can significantly enhance their autonomy. During a high-stakes project, we shifted from a centralized to a decentralized model, which allowed ARTs to self-optimize and adapt to shifting priorities seamlessly. It was like giving ARTs a brain of their own. Decentralized AI models reduce the bottlenecks you'd typically encounter in centralized systems. Think of the ARTs as small startups within the larger enterprise ecosystem, each capable of making swift, informed decisions. ... This isn’t just a tech enthusiast's dream—it's an emerging reality. The maturity of AI technologies spells a future where enterprises aren’t just keeping up; they’re setting the pace. So, if there’s a single, actionable insight to glean from my journey, it’s this: enterprises need to actively pursue cross-industry collaborations, invest in AI-powered microservices, and hone their Agile professionals’ skill sets.

Incorporating Geopolitical Risk Into Your IT Strategy

IT organizations know how to plan for unexpected outages, but even the most rigorously designed strategy is vulnerable to the shifting winds of geopolitics. and technology leaders need to know how their organizations will respond to geopolitical disruptions, and scenario planning needs to be a priority. ... "The IT department can treat geopolitical disruption as an expected operational variable rather than an unforeseen catastrophe. Good and tested enterprise risk management frameworks, investment in government affairs partnerships and ongoing board engagement should start to manage and prepare for this," Dixon said. CIOs need to do around the risks facing their enterprise, and evaluate how IT is teaming with business units, security teams and the CISO on a cohesive tech strategy that builds security, including artificial intelligence security, in from the ground up, said Sean Joyce ... "You're as strong as your weakest link," Joyce said. "As geopolitical risk becomes more prominent, you're going to see tools like cyber being leveraged by countries, particularly those that don't have stronger military or other capabilities. For some, it may be the only tool they can leverage." Physical infrastructure, geography and power supplies are also now areas of risk CIOs need to consider, and infrastructure strategy must align with sustainability, energy realities and geopolitical stability.

Six Architecture Challenges for Startups

The risk is not that the first version is imperfect; that is inevitable. The risk is that the team keeps layering new functionality on top of an accidental architecture. At some point, the cost of change becomes so high that every small modification feels dangerous. The architectural challenge is to intentionally decide where to accept debt and where to invest in structure. Startups need a minimal set of principles – for example, clear domain boundaries, basic , and a simple deployment model – that allow speed without locking the product into a dead end. ... If the product team is still validating pricing models, redefining the customer journey, or experimenting with different verticals, any rigid decomposition can turn into friction. Yet avoiding boundaries altogether leads to a “big ball of mud” that is equally hard to evolve. A practical approach is to use provisional boundaries based on current value streams – onboarding, transaction processing, analytics, etc. – and treat them as hypotheses. The challenge is not to find the perfect structure from day one, but to keep those boundaries explicit and adjustable as the evolves. ... Startups must make conscious decisions about where they are comfortable being tightly coupled to a provider and where they need portability. That requires viewing cloud services through a business lens: What is strategic IP, what is replaceable, and what is pure commodity? Aligning these categories with architectural choices is a non-trivial design challenge, not just a procurement decision.

Platform-as-a-Product: Declarative Infrastructure for Developer Velocity

Without centralized guardrails, teams often compensate by over-allocating resources "to be safe", leading to inconsistent environments and unnecessary cloud spend that is only discovered after deployment. ... What is missing is a developer-friendly abstraction that brings these related concerns together. Developers need a way to express intent (not only what infrastructure is required, but also how the application should be built, deployed, configured across environments, secured, and sized) without having to implement the mechanics of each underlying system. From a platform engineering perspective, this abstraction represents the core of an and can be implemented as a lightweight . ... The platform comprises several interconnected components. coordinate everything, pulling code from repositories, building and unit testing applications (with tests written by developers), checking security, creating cloud infrastructure with , and deploying to clusters with . The configuration controls all of this, telling each component what to do. The architecture clearly separates concerns: the CI pipeline handles code building, testing, and vulnerability scanning. CD pipeline handles deployment: creating cloud resources, updating Kubernetes, and configuring environments.

(Re)introducing Adaptive Business Continuity

is designed to provide a framework that delivers better outcomes when organizations deal with losses. The result may be a reduction in documentation (something I greatly favor) but that is not a stated goal. ... My experience over the years has led me to conclude that trying to define priorities for the resumption of services is wasted effort. Many activities can take place in parallel, and priorities will change when disasters occur. A perfect example is the governmental lockdowns and health authority mandates that followed the emergence of . The result is that demand for products and services changed drastically, upending previous priorities. Priorities may be defined following adaptive principles, but it is not at all a stated component of the Adaptive framework. ... For a number of reasons, I would like to see the word “plan” used a lot less within our profession. Seeing the word “strategy” in its place would be a step in the right direction. Strategy improvement is not, however, a key outcome of Adaptive BC efforts. There is some benefit to having clearly defined recovery strategies, but strategies only provide benefit to competent and empowered teams armed with the resources they need to carry out the mission. For this reason, I always emphasize the importance of focusing efforts on capabilities and consider plans and strategies as little more than supporting tools for any business continuity program. The improvement of strategies and/or plans is simply not an expected outcome of Adaptive BC work.

Exactly What To Automate With AI In 2026 For Faster Business Growth

Most founders automate the wrong things. They start with the flashy stuff, the complicated tools and fancy dashboards, while ignoring the repetitive tasks quietly draining their hours. But you need faster, cleaner growth by removing friction from the activities that actually grow your business. ... You shouldn't embark on a day's worth of admin tasks every time a new client says yes. It will only slow you down. Make it easy for them to pay, get a receipt, complete an onboarding form, and submit the required information. On your end, have the folders, follow-up emails, and team briefings set up without you lifting a finger. Question everything you currently do manually. There is no reason it couldn't be an AI agent handling the sequence. All the tools you pay for already have integrations with each other; You're just not using them. The goal is that you could sign client after client because onboarding takes minutes, not hours. ... AI-generated content is awful when you use it wrong. But that doesn't mean you shouldn't involve AI in your content production process. Content still matters in marketing, whether long-form articles, videos, or social media visuals. You need to be part of the conversation, but only with relevant, authentic material. You cannot outproduce everyone manually, so use automations and retain your human genius for the finishing touches. ... The more your life admin runs on autopilot, the more you free up time and energy for your business.

What is AI fuzzing? And what tools, threats and challenges generative AI brings

The way traditional fuzzing works is you generate a lot of different inputs to an application in an attempt to crash it. Since every application accepts inputs in different ways, that requires a lot of manual setups. Security testers would then run these tests against their companies’ software and systems to see where they might fail. ... Today, generative artificial intelligence has the potential to automate this previously manual process, coming up with more intelligent tests, and allowing more companies to do more testing of their systems. ... But there’s a third angle involved here. What if, instead of trying to break traditional software, the target was an AI-powered system? This creates unique challenges because are not predictable and can respond differently to the same input at different times. ... AI fuzzing can also help speed up the discovery of vulnerabilities, Roy says. “Traditionally, testing was always a function of how many days and weeks you had to test the system, and how many testers you could throw at the testing,” he says. “With AI, we can expand the scale of the testing.” ... Another use of AI in fuzzing is that it takes more than a set of test cases to fully test an application — you also need a mechanism, a harness, to feed the test cases into the app, and in all the nooks and crannies of the application. “If the fuzzing harness does not have good coverage, then you may not uncover vulnerabilities through your fuzzing,” says , staff innovations architect for emerging technologies at .

CISOs flag gaps in third-party risk management

CISOs rank among their highest-impact threats. Vendor relationships touch nearly every core business function, from cloud infrastructure and software development to data processing and AI services. Each added dependency expands the attack surface and increases the number of organizations involved in protecting sensitive systems and data. ... Only a small portion of organizations report visibility across third-, fourth-, and nth-party relationships. Most operate with partial insight limited to direct vendors or a narrow segment of the extended supply chain. CISOs say limited visibility complicates incident response, risk prioritization, and compliance planning. When a breach emerges several layers removed from a known vendor, security teams may struggle to understand exposure, timelines, and downstream impact. ... CISOs report rising regulatory scrutiny tied to third-party cyber risk. Regulatory frameworks place greater expectations on organizations to demonstrate oversight across vendor ecosystems, including indirect relationships. Only a minority of organizations feel ready to meet upcoming requirements without major changes. Most report progress underway, with further work needed to align processes, tooling, and internal coordination. Third-party risk management involves legal, procurement, compliance, and executive leadership alongside security teams. ... At the same time, AI adoption accelerates within vendor risk management itself.

Anti-fragility – what is it and why should it be the goal for your organisation?

That ability to thrive in the face of disruption must become the basis for improved resilience. Modern organisations shouldn’t strive for survival, but for continual improvement. In the , that is crucial. Threat actors are constantly changing tack, targeting new , and executing increasingly complicated . Resilience must therefore move in tandem as an ongoing process of learning and adapting. That is the crux of . It defines systems that thrive and improve from stress, volatility, disorder and shocks, rather than just resisting them. If a security model is only designed to recover, it remains just as vulnerable as before. But an anti-fragile approach actively benefits from each attack, identifying weaknesses, addressing them, and adapting as needed. ... Increasingly, organisations are recognising the value in anti-fragility as a strategy and more will adopt it next year. However, getting there means going beyond regulatory compliance. Compliance lays the foundations from which successful cybersecurity can be built, yet many currently see it as the finished structure. There are several problems with that. Security legislation frequently lags behind the threat landscape, and so the gap between a new threat emerging and a new law coming in to address it can stretch over the course of years. Organisations must therefore understand that compliance doesn’t equal protection.