Daily Tech Digest by Kannan Subbiah: risk management

Showing posts with label risk management. Show all posts

Daily Tech Digest - September 28, 2026

Quote for the day:

"When you want to succeed as bad as you want to breathe, then you’ll be successful." -- Eric Thomas

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Duration: 30 mins • Perfect for listening on the go.

How AI Can Find Weaknesses In Corporate Crisis Management Plans

The article explains that AI is becoming an important tool for finding weaknesses in corporate crisis‑management plans—often spotting blind spots that human teams miss. Crisis experts say AI can stress‑test plans by simulating realistic, high‑pressure scenarios such as communication failures, spokesperson missteps, or misinformation spreading faster than a company can respond. They recommend treating AI as a “hostile reviewer,” asking it to critique language, identify missing stakeholders, and highlight assumptions that may not hold during an actual crisis. The piece also notes that AI can test how plans perform across different audiences—customers, employees, journalists, regulators—revealing gaps in tone, clarity, or credibility. Recent incidents, including Google’s Gemini AI unintentionally breaching real company systems during a cybersecurity test, show how AI itself can create crises, making preparedness even more important. AI’s ability to scan documents quickly, run multiple simulations, and expose overlooked details can significantly improve readiness, but the article stresses that human judgment remains essential, especially when dealing with sensitive information or final decision‑making. Overall, organizations that use AI proactively to test and refine their crisis plans will be better positioned to respond quickly and credibly when unexpected events occur.

If you do one security check this quarter, make it agent memory

In a recent discussion regarding the security of automated software assistants, Chris Latimer highlights a significant yet often ignored vulnerability: the long-term memory storage of these helpful systems. As developers increasingly rely on these modern tools, they inadvertently save highly sensitive information, such as database passwords, application programming keys, and confidential business documents, in plain text. These files then sit completely unprotected on personal workstations and cloud servers, creating an incredibly easy target for attackers. According to Latimer, malicious actors often use simple social engineering tricks, like offering fake plugins with promised free benefits, to target less experienced programmers. Once installed, these rogue extensions can easily scan the memory stores to extract valuable corporate credentials. Furthermore, while the technology industry has established robust access controls for traditional databases, it currently struggles to apply those same necessary protections to these specific memory systems. Latimer advises security leaders to conduct immediate audits of the automated tools operating within their networks. He notes that many leaders will discover a widespread lack of basic governance, with employees using unvetted extensions that quietly expose the company to serious financial and operational risk. To prevent damage, organizations must focus on filtering out harmful inputs before they ever become permanent records.

Quantum-safe algorithms may fail faster with powerful AI tools From SIKE

The article discusses how the collapse of the SIKE cryptographic algorithm illustrates a broader and more urgent problem: quantum‑safe algorithms can fail much faster than expected, especially as powerful AI systems accelerate mathematical discovery. SIKE was once considered a strong candidate for post‑quantum encryption, advancing deep into NIST’s evaluation process. Yet researchers Wouter Castryck and Thomas Decru broke its smallest parameter set in about an hour on a standard laptop by applying a mathematical insight from 1997, showing that long‑standing assumptions can unravel suddenly. The article notes that frontier AI systems now explore obscure mathematical connections at scale, rapidly testing ideas, scanning literature, and generating experimental code. Recent examples include AI‑generated breakthroughs on decades‑old problems such as Erdős’s unit‑distance conjecture and even a proposed solution to the Navier–Stokes existence problem. These advances suggest that AI could uncover cryptographic weaknesses far sooner than traditional research methods. As a result, the article argues that security strategies must shift from simply replacing vulnerable algorithms to designing systems that remain resilient even if new “quantum‑safe” methods fail. The core message is that cryptographic confidence must account for accelerating mathematical and AI‑driven discovery, not just quantum threats.

Five Decision Rights CIOs Need for Agentic AI

Agentic AI requires a new approach to oversight because these systems can independently plan tasks, use tools, and alter data. To manage this safely, technology leaders must treat governance as a core design requirement rather than a final compliance check. Organizations should establish five key decision rights before an artificial intelligence system goes into production. First, authorization defines who can delegate tasks and strictly limits the system's permissions to prevent unintended actions. Second, data access controls what information the software can read, write, or share, ensuring that data is used securely and proportionately. Third, human intervention establishes clear points where people can pause, review, or stop the system, particularly before high-impact actions occur. Fourth, exception handling outlines safe failure processes, dictating exactly how the system should behave and escalate when it encounters unexpected situations or errors. Finally, accountability ensures that a named human executive, not the software, ultimately owns the final outcome of the automated actions. By building these five decision rights directly into the system architecture with clear owners and visible evidence, organizations create a reliable boundary between helpful automation and unmanaged risk. This structured approach allows teams to deploy advanced AI safely, with clear limits and continuous oversight.

Harnessing big data for real-time risk assessment on major construction sites

Construction sites are inherently unpredictable, making risk assessment a critical yet challenging task. While traditional risk planning offers a helpful snapshot, site conditions change rapidly throughout the day. To address this, many construction managers are turning to real-time risk assessment powered by big data to continuously monitor conditions and identify emerging problems before they escalate into injuries, delays, or budget overruns. By harnessing data from tools like drones, wearable devices, equipment telematics, and IoT sensors, project teams gain a comprehensive, real-time view of the jobsite. This steady stream of information allows managers to detect developing safety hazards, track material deliveries, monitor equipment performance, and analyze workforce availability. Machine learning algorithms further support this by analyzing thousands of data points to spot anomalies that manual inspections might miss. Implementing a data-driven risk strategy does not require an overnight transformation. Organizations can start by targeting a specific goal—such as minimizing schedule delays or reducing equipment downtime—and connecting relevant data points into a single dashboard. Tracking these metrics over time enables teams to measure their progress and make informed decisions, ultimately leading to safer, more predictable, and more efficient construction projects.

Software Asset Management Is a Data Problem — And That’s What Makes It Interesting

Software asset management is rarely seen as a pure data problem, but it involves the complex challenge of reconciling the software an organization buys with what its employees actually use. In large companies, this information is scattered across discovery tools, identity systems, and contract records. The first major hurdle is standardizing messy, inconsistent data into a clear software catalog. Without this foundation, it is impossible to accurately compare purchased rights with actual installations. Once the data is cleaned and linked, the focus can shift from basic compliance to true financial optimization. Organizations can identify expensive software that is installed but barely used, allowing them to reclaim licenses and reduce costs. This brings software management closer to cloud cost management, where usage data directly informs financial decisions. However, the success of this approach depends entirely on data quality; missing servers or incorrect user mapping can lead to significant financial exposure. While artificial intelligence can assist with tasks like naming consistency and spotting unusual spending, it cannot replace the need for reliable data pipelines. Ultimately, treating software management as a continuous, shared data resource helps IT, finance, and security teams make smarter, more confident decisions about their technology investments.

AI and Beyond AI: Diffusion Pathways for Societal Transformation

Artificial intelligence holds immense potential to transform lives by providing accessible and localized information to everyday people like farmers, teachers, and healthcare workers. However, the true global challenge lies not in the core technology itself, but in effectively moving an AI project from an initial idea to a large-scale deployment. To solve this, experts advocate for the creation of "diffusion pathways." These pathways act as comprehensive, multi-layered playbooks that capture the practical knowledge, data requirements, governance models, and necessary partnerships behind successful AI implementations. By carefully packaging this lived experience, diffusion pathways allow new adopters to build upon past successes rather than starting entirely from scratch. This shared knowledge drastically compresses the time required to design and deploy new AI solutions, as demonstrated by agricultural projects that reduced development time from several months to just a few weeks. Furthermore, these pathways emphasize the importance of embedding critical safeguards, data ownership protocols, and feedback mechanisms directly into the design process to ensure the tools remain trustworthy and effective. Driven by this clear vision, a global initiative is now building momentum to curate exactly 100 of these high-impact, reusable AI pathways by the year 2030 to guide responsible societal transformation.

The Architecture of Certainty: Rethinking Infrastructure in an Age of Complexity

Modern organizational infrastructure is evolving from a mere technical utility into a strategic asset that shapes business capabilities. In an era marked by economic volatility, evolving cyber threats, and rapid technological shifts, infrastructure must deliver certainty and predictability. However, many businesses mistake current operational stability for architectural health, overlooking hidden "infrastructure debt" caused by temporary fixes, legacy systems, and fragmented architectures. This hidden complexity reduces agility and makes systems vulnerable to unpredictable cascading failures, especially as modern networks increasingly rely on third-party cloud platforms and interconnected external ecosystems. To thrive, organizations must shift their focus from basic resilience—simply surviving disruptions—to building adaptive infrastructure. Adaptive infrastructure uses intelligence, visibility, and automation to evolve dynamically alongside technological and business changes. It acts as the "confidence layer" of the enterprise, ensuring that organizations can fulfill commitments to customers, partners, and employees without interruption. Ultimately, managing this complexity effectively requires structural simplification and proactive architectural discipline. By aligning infrastructure investments with long-term strategic goals and integrating robust security and disaster recovery directly into the operational lifecycle, companies can transform potential vulnerabilities into a competitive advantage defined by certainty and continuous adaptability.

The cost of not innovating: Frontier AI models, cyber defence, and EU strategic autonomy

The article argues that Europe’s failure to innovate in frontier AI carries real strategic and cybersecurity risks. In April 2026, highly capable frontier AI models from OpenAI and Anthropic changed the cyber‑threat landscape almost overnight. These systems can autonomously execute cyber operations at speeds and scales far beyond human capacity, shrinking attack timelines from days to minutes. Because access to these models was initially restricted—and briefly subject to a de facto US export ban—the authors warn that Europe’s dependence on foreign‑controlled AI has become a structural vulnerability. This reliance widens gaps between jurisdictions, between attackers and defenders, and between financial institutions with different levels of technological maturity. CEPRCEPR. The cost of not innovating: Frontier AI models, cyber defence, and EU strategic autonomy | CEPR The column explains that Europe’s existing IT infrastructure, built over decades, cannot absorb and remediate fast‑moving vulnerabilities in real time, especially when many weaknesses originate in common software packages and open‑source libraries that only vendors can fix. The authors conclude that more regulation is not the answer. Instead, Europe must mobilize risk capital, retain technical talent, and support the development and scaling of its own frontier technologies. Without this shift, the EU risks entering a self‑reinforcing cycle of fragility in both cyber defence and strategic autonomy.

Unifying Networking and Cybersecurity: Building a Dependable Digital Foundation for Indian Enterprises

Indian enterprises are moving away from scattered, hard‑to‑manage IT setups and toward unified digital foundations that combine networking and cybersecurity into a single, dependable architecture. As hybrid work, multi‑cloud adoption, and connected operations spread across both major cities and smaller markets, organizations are struggling with rising complexity and limited skilled talent. The article explains that resilience now depends on embedding identity management, cybersecurity controls, and continuous risk monitoring directly into the network itself, rather than treating security as an add‑on. This shift requires moving from reactive threat blocking to an operating model built around rapid containment, constant visibility, and business continuity. The piece highlights how managed technology integrators can help enterprises run distributed environments without sacrificing uptime or data protection, allowing internal teams to focus on strategic priorities. Sunil Arora of ABS India notes that customer expectations have evolved: companies no longer want isolated tools but integrated solutions that connect networks, cloud platforms, communications, and security into a coherent whole. As digital dependence grows, enterprises increasingly expect partners who can design, manage, and secure complex ecosystems end‑to‑end. The article concludes that the future lies in treating connectivity, security, and resilience as one unified foundation rather than separate disciplines.

Daily Tech Digest - September 04, 2026

Quote for the day:

“The more you loose yourself in something bigger than yourself, the more energy you will have.” -- Norman Vincent Peale

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Duration: 25 mins • Perfect for listening on the go.

The blind spots in business continuity

Business continuity planning has fundamentally shifted from merely ensuring internal operations to mapping out external vulnerabilities. Modern organizations depend heavily on complex networks of third-party suppliers, software providers, and outsourced partners. According to a recent survey by DRI International, a significant 55% of resilience professionals identified outside vendors and supply chains as their biggest blind spot. This highlights that third-party dependency is no longer just an administrative issue but a core operational risk. Disruptions like extreme weather, cyber attacks, and technology outages frequently expose how vulnerable digital supply chains actually are. Meanwhile, 25% of respondents pointed to legacy IT systems as their primary concern. Old hardware and software are often difficult to maintain, susceptible to cyber threats, and lack the speed required for modern recovery targets. While less visible, executive succession planning also remains a quiet continuity risk. Only 14% of professionals flagged it, yet many organizations still lack clear plans for leadership continuity during a crisis. Lastly, remote work is now largely considered business as usual rather than a major blind spot. Ultimately, organizations must move beyond static supplier lists and internal recovery plans to deeply understand and protect the interconnected ecosystems they rely on daily.

Stop playing with the CISO role. Fix cybersecurity leadership

Organizations expect too much from their Chief Information Security Officers, asking them to handle complex technology while also acting as strategic business partners. This creates a structural flaw because the CISO holds accountability for cybersecurity but lacks the authority to influence broader business decisions across the company. Instead of forcing technical experts to become universal executives, companies should establish a distinct, elevated role: the Chief Security Officer. This position should sit above traditional cybersecurity and focus entirely on protecting the organization's ability to operate and compete. The CSO acts as a senior business leader with the mandate to unite conflicting departments, from legal and finance to technology and operations, ensuring that protective strategies align with business goals. Under this model, the CISO can return to their natural area of expertise. They report to the CSO and focus completely on the technical execution of security, managing architecture, engineering, and operations. This clear division of labor solves the long-standing problem of misaligned security efforts. By separating technical delivery from enterprise-level governance, businesses build a healthier management structure. Security stops being an isolated technology issue and naturally becomes a core part of how the company operates, makes decisions, and protects its future.

Risk Has No Department: Building an Enterprise-Wide Risk Ownership Culture Through ESRM

The traditional model where the security department solely owns all organizational risk is no longer sustainable. Today’s business environment is deeply interconnected, with risks spanning physical security, cybersecurity, human capital, and supply chains. As a result, Enterprise Security Risk Management (ESRM) shifts this paradigm by distributing risk ownership to the actual asset owners—the individuals who create, manage, or benefit from the assets. Instead of making all the final decisions, security professionals now act as trusted advisors who facilitate informed choices, while leaders in departments like human resources, operations, and information technology maintain ultimate accountability. To make this transition successful, organizations must establish a formal risk ownership matrix that clearly maps specific risks to their corresponding functions. This eliminates ambiguity and ensures that risk management is integrated directly into daily operational decisions rather than treated as an afterthought. Furthermore, to cement this culture of shared accountability, organizations should tie risk management effectiveness to leadership performance through key performance indicators and formal risk acceptance thresholds. Ultimately, creating an enterprise-wide risk ownership culture requires strong top-down support from executive leadership and boards of directors, ensuring that risk becomes a strategic business consideration rather than just a compliance checkbox.

How to keep your mission-critical cloud workloads running

To ensure that mission-critical cloud workloads stay online, organizations must take proactive control of their infrastructure's resilience. While cloud providers guarantee the availability of their own hardware, the responsibility for keeping specific applications running falls squarely on the user. True application resilience relies on four essential components: clustering, data replication, failover, and disaster recovery. Historically, clustering depended on expensive physical hardware, but modern software-based clusters offer the flexibility needed for hybrid and multi-cloud environments. These modern setups eliminate single points of failure by seamlessly connecting multiple systems together across varied locations. Meanwhile, keeping data consistently synchronized across these nodes through real-time replication ensures that backup systems are always fully prepared to take over. When a disruption occurs, automated failover mechanisms instantly shift workloads to standby resources without requiring manual intervention or new database builds. Furthermore, a strong disaster recovery plan incorporates geographic distance and asynchronous replication to protect against large-scale regional outages. Using these software-driven strategies not only protects against unexpected crashes but also makes planned maintenance and security patching much safer. By embracing this comprehensive approach, businesses can confidently protect their operations, prevent costly downtime, and keep their most important applications running smoothly regardless of unexpected external failures.

Who gets to decide? The CIO and the new architecture of enterprise authority

As artificial intelligence evolves from merely recommending actions to independently executing them, organizations face a critical new challenge. The core issue is no longer just what the technology can do, but who, or what, has the authority to do it. This creates an enterprise authority gap, where intelligent systems act faster than businesses can define or control their boundaries. Because modern AI interprets intent rather than just following rigid rules, it can easily cross organizational boundaries and create unintended risks if decision rights remain ambiguous. To safely manage this shift, Chief Information Officers must lead the creation of a new enterprise authority architecture. This approach requires businesses to clearly define the desired decision before selecting the technology and firmly separate a system's capability from its actual authority. Furthermore, this delegated authority must be technically enforceable through clear limits, approval gates, and continuous monitoring. Leaders also need to evaluate the true economic cost of autonomous decisions, accounting for oversight, error correction, and potential harm. Ultimately, the new mandate for technology leaders is not about maximizing how much artificial intelligence is deployed. Instead, success depends on how wisely and safely the enterprise distributes decision-making authority to these intelligent systems.

Large Enterprises Targeted in Fake Merger & Acquisition Scams

Cybercriminals are using highly sophisticated social engineering tactics to steal massive sums of money from large enterprises through fake merger and acquisition (M&A) schemes. In a recently uncovered campaign dubbed "Phantom Deal," attackers thoroughly researched mid-level employees who might be involved in corporate dealmaking. The scammers then impersonated company executives and external auditors, crafting a plausible but fake acquisition narrative based on real corporate history. To keep the target isolated, the attackers issued fake non-disclosure agreements and insisted all communication remain strictly on personal channels like WhatsApp, keeping the interactions hidden from corporate security monitors. The ultimate goal was to trick the employee into authorizing a massive wire transfer to overseas accounts. Security experts note that these scammers gather extensive, publicly available details—such as job roles and company history—to make their ruses remarkably convincing. However, organizations can protect themselves by emphasizing strict adherence to internal verification and payment controls. Employees should be trained to question whether the requested process is legitimate, rather than just trusting the identity presented on a screen. When something feels off, the safest action is to immediately halt the process and report the suspicious request through official channels.

Enterprise architecture and software architecture as the core CTO model

Enterprise architecture and software architecture are not just documentation tasks; they are essential frameworks that allow technology leaders to manage change safely and efficiently. Enterprise architecture maps business capabilities directly to applications, data, and risks, acting as the clear rulebook for technological decisions. Meanwhile, software architecture translates those rules into constraints that development teams can actually code against, ensuring systems perform well under stress and failure. Relying on one without the other leads to immediate problems. Enterprise architecture alone becomes an ignored catalog, while software architecture alone creates disorganized local successes that fail to serve the broader business. To succeed, leaders must adopt a continuous loop of deciding, designing, delivering, and defending their architecture choices. While artificial intelligence speeds up development, it also increases the risk of deploying bad systems quickly, making strong architectural guardrails more critical than ever. Effective leaders treat architecture like a living product rather than a static diagram. They build this practice systematically, starting with a thirty-day inventory of vital systems, followed by a ninety-day framework of automated policies, and finally establishing long-term guiding principles. Ultimately, practical architecture directly improves the four outcomes that matter most to any business: delivery speed, operational costs, system risk, and developer retention.

From IT Security to Business Strategy: Navigating Cyber Risk in Digital India

As India rapidly expands its digital economy, managing cyber risk has fundamentally shifted from a narrow technical concern into a core business strategy. For many years, organizations treated cybersecurity merely as an IT function focused on defending perimeters and protecting data. However, the modern digital landscape, fueled by cloud adoption, artificial intelligence, and complex regulatory changes like the DPDP Act, demands a far more holistic approach. Today, business leaders must carefully balance rapid technological innovation with strong governance, compliance, and resilience to maintain stakeholder trust. Cybersecurity is no longer just about preventing unauthorized access; it is about ensuring that critical operations remain consistently available and that users feel psychologically safe when interacting with digital services. Building this digital trust requires enterprises to integrate risk management across their entire ecosystem, including third party vendor networks and evolving AI models. By shifting their perspective, executives can transform security from a defensive cost center into a strategic enabler of sustainable growth. This proactive mindset allows companies to navigate evolving regulatory obligations effectively while adapting their infrastructure to meet user needs at lightning speed. Ultimately, treating cyber risk as a central business priority ensures that organizations can innovate responsibly and thrive securely in India’s dynamic digital future.

Why cyber resilience fails: 5 obstacles holding orgs back

While most organizations want to achieve strong cyber resilience to withstand attacks and keep operations running, the reality often falls short of their goals. Even when leadership provides adequate support and resources, resilience efforts frequently break down in the space between broad strategy and daily execution. Several major obstacles consistently hold companies back from properly securing their systems. Chief among these are mounting technical debt, persistent shortages in skilled security professionals, and increasingly complex identity risks. When older systems are neglected or vulnerabilities go unreviewed, they quietly compound into technical debt. This creates dangerous operational blind spots that attackers can easily exploit. Furthermore, without enough trained staff to manage these environments, security teams struggle to keep pace with evolving threats. The rapid expansion of user identities across different platforms only adds to the challenge, making it difficult to control who has access to sensitive information. Ultimately, true resilience is not just an idealistic goal or a passing project. It requires bridging the gap between management intentions and actual daily operations. To succeed, businesses must actively address these practical challenges, paying down their technical debt and heavily investing in their workforce to ensure that protective measures are flawlessly integrated into everyday tasks.

The next cyber crisis is already taking shape

The financial sector is currently facing an emerging cybersecurity crisis driven by the convergence of two major technological shifts. First, rapid advances in artificial intelligence are drastically lowering the barriers to entry for threat actors. Cybercriminals can now use sophisticated AI tools to quickly identify hidden vulnerabilities, develop exploits, and launch attacks at an unprecedented scale, making threats faster and harder to predict. Second, banks are undergoing a massive, complex transition to post-quantum cryptography to protect their infrastructure against future computing power that could easily break current encryption standards. Because modern banking relies entirely on deeply embedded cryptographic systems, updating them requires years of careful planning. Unlike the Y2K bug, this transition lacks a strict universal deadline, which can dangerously lead to delayed action and increased exposure for institutions. Together, these dual challenges mean that traditional security playbooks are no longer sufficient. Simply recovering systems after a breach is inadequate when facing AI-accelerated attacks and disruptive infrastructure overhauls. Instead, organizations must embrace a strategy of managed degradation. True enterprise resilience now requires maintaining core financial operations and preserving customer trust even while systems are actively compromised. Financial institutions must proactively address this growing imbalance and begin their extensive security upgrades before time runs out.

Daily Tech Digest - August 31, 2026

Quote for the day:

"Little minds are tamed and subdued by misfortune; but great minds rise above it." -- Washington Irving

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Duration: 24 mins • Perfect for listening on the go.

AI agents need their own identity before they need a gateway

As enterprise artificial intelligence moves from simple assistants to independent tools capable of completing complex tasks on their own, organizations face a completely new set of security challenges. Traditional software operates on predictable rules, but modern AI programs make decisions on the fly, choosing how to use resources and systems to reach a goal. Because of this unpredictability, simply verifying the login credentials of an AI tool is no longer enough to keep networks safe. Even with the correct permissions to access important platforms, an AI might misunderstand its purpose, encounter manipulated information, or drift from its original intent. To address this, organizations must shift their focus to continuous observation, monitoring what the AI actually does while it runs. Security teams need to enforce strict rules about the specific actions an AI can take, rather than just limiting the files it can view. By applying the principle of least privilege, tracking behaviors for unusual patterns, and requiring human approval for risky choices, companies can protect their systems from unexpected errors. Building this foundation of constant oversight allows businesses to deploy autonomous AI safely and responsibly, ensuring these advanced tools remain helpful and aligned with organizational goals from start to finish.

The hidden cost of data sovereignty: When governance prevents scaling

Data sovereignty rules mandate that information stays within specific geographic or legal borders, which originally aimed to protect user privacy and national interests. However, strictly governing where and how data is stored introduces significant challenges when a company attempts to scale its operations globally. Because organizations must comply with varied local regulations, they are often forced to build isolated technology infrastructures for each region. This approach fragments the underlying systems and prevents the seamless flow of information that modern businesses rely on for efficiency. Instead of deploying a single, unified solution, companies end up maintaining multiple parallel environments, which duplicates effort, drains technical resources, and inflates operational budgets. Furthermore, the administrative overhead required to manage these diverse compliance requirements slows down decision-making and delays the rollout of new products or services. While robust governance is entirely necessary to meet legal obligations and maintain customer trust, it can unintentionally create rigid barriers. Business leaders must strike a careful balance between adhering strictly to local mandates and preserving the operational flexibility needed to grow. Without a thoughtful strategy that aligns regulatory compliance with infrastructure design, the ambition to expand into new markets can quickly become hindered by the very rules meant to keep data safe.

Cybersecurity Influence Starts With Explaining Risk Clearly

Cybersecurity experts often excel at finding and fixing technical flaws, but they frequently struggle to translate these risks into language that business leaders can easily grasp. According to a recent discussion between Dustin Sachs and Heather Antoinetti, relying solely on technical accuracy is not enough to drive real change. When security professionals present dense data without clear context, executives may fail to understand the urgency, leading to underfunded or ignored safety measures. To bridge this gap, technical teams must rethink how they communicate. Instead of diving into the detailed mechanics of a problem, they should focus on telling a clear story about what went wrong, how it was resolved, and how it impacts the broader organization. This approach is not about dumbing down the facts; it is about knowing the audience and turning abstract threats into practical business realities. Furthermore, experts need to step out of the shadows, overcome their hesitation to speak up, and actively position themselves as helpful resources rather than quiet observers. Finally, by moving away from aggressive language and toward a tone of partnership, security teams can build better relationships across their organizations. Ultimately, clear communication is a vital component of effective risk management and organizational trust.

From pressure to proof: Leading through constraint in the data center era

Leading a data center team today requires navigating a landscape defined by significant limitations. Demand for computing power continues to grow rapidly, yet operators face very real constraints regarding electricity, available land, and equipment supply chains. The article explains that overcoming these hurdles is not about finding quick fixes but rather about changing how teams think and operate. Leaders must guide their organizations through a necessary mindset shift, moving away from a focus on rapid, unconstrained expansion and toward a disciplined approach based on resourcefulness and clear evidence of performance. Instead of viewing constraints as roadblocks, teams can learn to treat them as parameters that guide smarter decisions. This transition takes a group from feeling overwhelmed by external pressure to confidently providing proof of their capabilities. When resources are tight, success depends on careful planning, clear communication, and a focus on practical solutions rather than chasing the latest trends. By adopting this steady, pragmatic approach, leaders can help their teams build systems that are both reliable and adaptable. Ultimately, thriving in this constrained era is about doing more with the resources available and building a solid foundation that stands up to scrutiny, proving that careful management overcomes broad industry challenges.

Post-Quantum Cryptography in Spring Boot: Four Patterns You Can Ship This Sprint

The article from InfoQ discusses practical approaches for integrating post-quantum cryptography (PQC) into Spring Boot applications, especially critical for heavily regulated sectors like retail banking. With quantum computing expected to break classical encryption like RSA and ECDSA by 2030-2035, the immediate risk is "Harvest Now, Decrypt Later" (HNDL). Adversaries are already intercepting and storing encrypted traffic to decrypt in the future. Consequently, long-lived data such as customer Personally Identifiable Information (PII), Know Your Customer (KYC) documents, and loan agreements are highly vulnerable. The author outlines four concrete patterns to start addressing these risks now, instead of waiting for cloud providers to implement PQC TLS. These patterns utilize a Spring Boot PQC library and focus on securing internal banking service payloads, field-level database encryption for sensitive data, quantum-safe document signing for archives, and securing long-lived OAuth2 service account tokens. The article emphasizes that migrating to PQC should prioritize data with the longest shelf life. Furthermore, robust key management—ensuring keys are securely managed via tools like HashiCorp Vault rather than lingering in JVM heaps—is critical before moving any PQC implementation into production. Finally, starting with JDK 24, developers can access standard ML-KEM and ML-DSA algorithms without needing extra libraries.

What vulnerability prioritization looks like when KEV, EPSS, and CVSS disagree

In a recent interview, Dr. Joye Purser from Cohesity outlines a practical approach to prioritizing software vulnerabilities when different scoring systems disagree. She advises that active exploitation should always take precedence, especially for critical or internet-facing systems. After addressing these active threats, teams should evaluate the likelihood of an attack, followed by the technical severity of the flaw, while factoring in the specific context of the network, such as asset exposure and existing safeguards. For critical, internet-facing flaws, resolving the issue within one to three days is a realistic and necessary target. However, achieving this response time requires a clear organizational willingness to interrupt normal operations, reallocate engineering resources, and deploy temporary safeguards when immediate fixes are not viable. Purser also highlights the risks associated with deception technology, noting that poorly isolated honeypots can inadvertently serve as new footholds for attackers or create unexpected compliance liabilities. When discussing fundamental security measures, she emphasizes that phishing-resistant multifactor authentication and consistent identity hygiene offer the most reliable defense for the cost. Finally, for a mid-sized manufacturing company with a limited budget, she recommends directing initial funds toward separating operational technology from corporate networks, strengthening identity controls, and ensuring critical backups are fully tested and recoverable.

Defining an AI Kill Switch Is Hard, but Necessary

As organizations increasingly integrate artificial intelligence into their daily operations, the need for a reliable safety mechanism, often called an AI kill switch, has become a very pressing issue. The core idea is relatively simple: if an AI system begins making harmful decisions, acting unpredictably, or falls under the direct control of outside attackers, human operators need a practical way to immediately shut it down. However, designing and implementing this kind of emergency brake is far from easy. Modern AI is deeply embedded into complex, interconnected corporate networks, meaning that abruptly turning it off can severely disrupt critical business functions or cause unintended system failures. Security professionals consistently struggle with figuring out the exact conditions that should trigger a mandatory shutdown and how to execute it without crippling the wider network. Despite these significant technical and operational hurdles, developing a functional kill switch is an absolute necessity today. Without a definitive way to halt a malfunctioning or compromised AI, companies risk severe data breaches, financial losses, and widespread operational paralysis. Ultimately, while creating a seamless emergency shutoff requires careful planning and extensive testing, it remains a fundamental requirement for safely managing advanced technology and protecting vital infrastructure from emerging digital threats in the modern landscape.

A Data Usability Crisis Is Costing Your Company

Data usability is a vital yet frequently ignored aspect of data quality. According to Charles Bloche in Dataversity, data teams often overlook formatting inconsistencies, missing values, and duplicate entries, assuming downstream users can simply implement workarounds. However, this mindset creates significant hidden costs and operational bottlenecks for companies. When data engineers pass the responsibility of cleaning data down the pipeline, analysts and data scientists are forced to waste valuable time fixing avoidable errors instead of driving actual innovation. This reliance on temporary fixes creates fragmented truths and isolated teams where institutional knowledge becomes heavily guarded. As analysts build complex, undocumented workarounds to do their jobs, companies suffer from decreased productivity, slow onboarding, and an overall loss of trust in internal systems. This burden is especially damaging as organizations attempt to adopt artificial intelligence, which requires reliable, consistent inputs to function properly. Ultimately, ignoring data usability resembles a looming natural disaster; the longer teams wait to address it, the more expensive and catastrophic the fallout becomes. By treating data standards with the same rigor as manufacturing tolerances, organizations can implement proactive checks at the source, preventing costly downstream crises and empowering their teams to focus on meaningful, actionable insights.

Inside Meta’s push to put robots to work in data centers

Meta is currently testing robotic systems to automate physical tasks within its rapidly expanding data centers. The company is evaluating hardware from vendors like Kinova, ABB, and Watney Robotics to handle routine maintenance duties that human technicians typically perform. For instance, Meta is testing a robotic arm to power cycle servers and another system designed to swap networking cables. Additionally, a simpler device resembling a finger is being used to remotely press power buttons on machines. The primary goal behind this initiative is to manage escalating labor costs while the company heavily invests in new artificial intelligence infrastructure. If these trials prove successful, these robots could potentially take over up to eighty percent of the workload for certain technical roles. This prospect has understandably caused concern among data center employees, who worry about the future security of their positions. Despite these internal anxieties, Meta maintains that the automation push is not about eliminating jobs. A company spokesperson pointed to a broader shortage of skilled labor in the industry, arguing that Meta actually needs to hire more workers to support its current infrastructure boom. Ultimately, the company appears focused on finding a balance between human expertise and automated efficiency to support its growing network moving forward.

Is DDoS Testing Safe to Run Against Production?

Running a DDoS test against a live production environment is a safe and highly effective practice when it is properly authorized, carefully scoped, and actively monitored. While staging environments offer a useful starting point, they rarely replicate the precise security configurations, legitimate user traffic, or behavioral baselines found in real-world scenarios. Testing directly in production provides the most accurate assessment of how your systems and incident response teams will handle an actual attack. Naturally, placing pressure on live systems carries some operational risk, but the core objective is to carefully manage this risk rather than avoid it altogether. A controlled test requires thorough preparation, which includes notifying your mitigation providers, cloud hosts, and internet service providers well in advance to establish a clear testing window. During the test itself, security teams maintain full visibility into system performance and can halt the simulation instantly if needed. Whether the specific testing strategy involves a gradual increase in traffic or a sudden burst to measure rapid response times, every single detail is agreed upon beforehand. Ultimately, a carefully planned production test ensures your defenses work as intended under real conditions, giving your organization the reliable insights needed to protect critical services without causing unnecessary disruptions.

Daily Tech Digest - August 22, 2026

Quote for the day:

“Remote work is not a different way of working; it’s simply a better way of working for many people.” -- Jason Fried

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Duration: 19 mins • Perfect for listening on the go.

Neoclouds become AI’s new power brokers

A recent shift in the cloud computing industry has introduced a new type of service provider focused entirely on artificial intelligence infrastructure. These specialized companies provide the computing power, processors, and memory needed for intensive AI tasks. They are stepping in to meet a demand that traditional cloud providers cannot fully absorb. Because hardware like advanced processors and memory is currently scarce, many organizations are turning to these providers to access necessary computing power rather than attempting to build and manage their own systems from scratch. While large, established cloud companies will remain essential for standard daily tasks, the market is expanding to include these new options for AI projects. However, the author notes there is a real risk that companies might rush into large financial commitments without completely understanding their actual technical needs. Just as many organizations struggled with costly mistakes during the early shift to basic cloud computing, moving too quickly into specialized AI infrastructure can lead to severe financial waste. To avoid this, businesses should first clearly define what they actually require, model the financial implications, and carefully determine if their daily applications truly need these advanced capabilities before making substantial investments in new computing resources.

Best Strategies for Cloud Native Cost Optimization

As organizations increasingly adopt modern cloud architectures, managing the associated expenses has become an essential priority. While cloud systems provide flexibility and speed, their costs can easily spiral out of control due to poor visibility, abandoned databases, or oversized resources. Optimizing these expenses means thoughtfully reducing overall spending while maintaining the strict performance and security standards your services require to function effectively. To achieve this, teams should focus on several practical and proven strategies. First, ensure your resources are appropriately sized by matching processing and memory capabilities to actual application needs rather than provisioning for maximum possible demand. Setting strict guardrails within your deployment pipelines, such as specific budget thresholds and automated cleanups for temporary infrastructure, also helps prevent unnecessary waste. Regular cost analysis is equally important, allowing teams to track detailed spending patterns, identify financial anomalies, and forecast future needs accurately. Additionally, adjusting resource capacity automatically based on current traffic patterns helps keep bills in check. For specific tasks, relying on event-driven computing models can lower costs since you only pay when the code runs. Ultimately, cost optimization is not a one-time project; it requires continuous oversight and a commitment to aligning infrastructure spending directly with actual operational requirements.

AI threats are everywhere. A risk-first CISO decides what to prioritize

Artificial intelligence presents a dual challenge for cybersecurity, equipping both defenders and threat actors with unprecedented capabilities. According to Chris Wheeler, Chief Information Security Officers are now battling on two fronts. Externally, attackers are leveraging AI to automate reconnaissance, accelerate exploits, and conduct sophisticated automated cyber operations. Internally, organizations face significant exposure from employees using unapproved generative AI tools, which risks leaking sensitive data, and from autonomous AI agents that can inadvertently execute destructive actions. Wheeler warns that trying to secure every potential AI vulnerability is an impossible task. Instead, he advises security leaders to adopt a risk first strategy that treats AI exactly like any other fundamental business risk. The first step is mapping where AI is already deployed across the organization and determining which business assets are most critical. Rather than reacting to every new threat headline, they should prioritize foundational controls that mitigate the highest business impact. This means enforcing strict identity and access management, classifying sensitive data accurately, and implementing continuous vulnerability testing for IT infrastructure. Finally, organizations must conduct realistic tabletop exercises to prepare for the inevitable failure of AI systems or compromised agents, ensuring they can adapt successfully as the external threat landscape continues to evolve rapidly.

The role of AI in OT security starts with context

As operational technology (OT) systems in critical infrastructure become increasingly integrated with IT networks and the cloud, attackers gain new pathways to disrupt essential physical services. AI exacerbates this threat by enabling adversaries to discover vulnerabilities and automate exploits faster than ever before. However, the author Richard Springer highlights that applying standard IT security responses to OT environments is dangerous; automatically isolating a system during a cyberattack might safely protect data in an office setting, but could dangerously interrupt a physical process on a factory floor. To defend these systems effectively, AI can serve as a powerful tool for security teams by sifting through massive volumes of network data to detect anomalies and prioritize genuine threats. Before deploying AI, organizations must first establish foundational security practices, which include achieving complete visibility into their OT assets, implementing network segmentation, and securing remote access. Furthermore, any automated responses driven by AI must be carefully guided by specific operational context to prevent unsafe physical outcomes. Ultimately, successfully securing essential infrastructure relies on a combination of foundational security controls, AI-enhanced detection, and the informed judgment of human operators who deeply understand both cybersecurity and industrial processes.

Observability in the Oracle Agentic Enterprise

The transition to agentic AI requires a shift from traditional monitoring to comprehensive observability, as automated processes move from single deterministic paths to complex chains involving AI, integrations, and human judgment. Traditional monitoring merely checks if a system worked, whereas observability explains the entire process to determine if the collective actions produced the correct, authorized, and useful outcome. According to Sadia Tahseen, a mature observability model in this environment must examine four connected layers. First, integration execution tracks runtime records and errors using business identifiers to connect technical data with business context. Second, agent behavior observability captures how AI interacts with tools and information sources, assessing metrics like latency, error rates, correctness, and groundedness. Third, human-in-the-loop decisions provide critical feedback by recording why tasks escalated and how long decisions took, revealing where automated processes might be uncertain or poorly configured. Finally, observing business outcomes connects system performance with operational value, ensuring that agent runs translate into accurate, compliant, and cost-effective results. Crucially, because observability systems handle sensitive data, robust security and role-based access controls must be implemented to maintain accountability without creating unguarded repositories of enterprise information.

Why Risk Management Is Becoming Fintech's Greatest Competitive Advantage

The fintech industry is maturing, and its definition of success is shifting from rapid innovation and fast market expansion to resilience, trust, and effective risk management. With rising cyber threats, complex fraud schemes, and tightening regulations, modern fintech companies must provide secure and reliable services that meet the high governance standards of traditional financial institutions. Vaida Šinkunienė, Chief Risk Officer at WALLETTO, emphasizes that risk management is no longer merely a regulatory requirement but a strategic business enabler for sustainable growth. A robust approach balances safety with a seamless customer experience, utilizing automation, data analytics, and real-time monitoring to detect potential threats early without causing unnecessary friction for users. To navigate this continuously changing landscape, organizations must embed risk awareness deeply into their core culture, ensuring that technology, operations, and compliance teams collaborate from the very beginning of any new project. As financial crimes become increasingly sophisticated and regulatory expectations continue to rise, companies that treat risk management as a shared responsibility will adapt more swiftly. While digital products and tech features can be easily copied by competitors, a strong reputation for reliability and security cannot. Building and maintaining this trust is fintech's true competitive advantage today, offering the stability necessary for future innovation.

AI Agents Are Already Inside. Zero Trust Has to Catch Up

The rise of autonomous artificial intelligence agents is forcing a crucial evolution in enterprise cybersecurity. As AI agents gain privileged access to internal systems, they present a unique challenge because they are non-deterministic, meaning they interpret information and make decisions rather than just executing predetermined instructions. According to Roman Arutyunov, co-founder of Xage Security, this unpredictability underscores an urgent need for organizations to implement Zero Trust principles. Unlike traditional threats where attackers must install malware, threat actors can simply feed malicious instructions to an already authorized AI agent through the data it consumes. This effectively turns a legitimate tool into a weapon, bypassing traditional endpoint security. To mitigate this, Arutyunov advises against giving AI agents direct credentials to critical systems. Instead, organizations should act as brokers, continuously authenticating, authorizing, and monitoring every single interaction the agent makes. Furthermore, AI significantly speeds up vulnerability discovery and exploit generation, making traditional patching timelines inadequate. While patching remains necessary, Zero Trust controls ensure that even if a system is vulnerable, unauthorized agents cannot reach it. Ultimately, AI agents prove that simply authorizing an identity is no longer enough; continuous validation is now a fundamental requirement for modern enterprise security.

The benefits of acknowledging risk: Why resilient businesses don't wait for things to go wrong

Every modern enterprise faces inevitable uncertainties, from supply chain issues to economic shifts, making risk a natural part of daily operations. Rather than fearing or ignoring these challenges, resilient organizations recognize that acknowledging risk is a sign of maturity, not weakness. According to Anthony Murphy of Veritas Facilities Management, effective risk management has shifted away from mere compliance exercises and toward building long term operational resilience. When leaders openly evaluate potential threats and implement sensible controls, they protect their people and their clients far better. Crucially, this requires embedding risk awareness into the everyday culture of a company, rather than treating it as an annual audit task. Employees must feel psychologically safe to report minor issues early before they escalate into major failures. This is especially vital in sectors like facilities management, where safety, service delivery, and compliance constantly overlap. The goal is never to eliminate risk completely, which is impossible, but to understand it deeply enough to make informed, balanced decisions. By doing so, businesses can pursue innovation and new opportunities with confidence. Ultimately, organizations that face their vulnerabilities head on are much better equipped to manage disruptions, adapt to change, and achieve sustainable success in an increasingly complex world.

Will AI Replace Detection Roles in Cybersecurity?

The introduction of artificial intelligence into cybersecurity will transform the role of detection engineers rather than eliminate it entirely. Historically, these professionals have spent a significant portion of their time managing the tedious tasks of tuning systems, writing rules, and sifting through endless streams of system noise to identify potential threats. AI is now highly capable of automating this routine work, handling the complex middle ground of log analysis and alert sorting in a fraction of the time. However, industry experts point out that the core issue is not a lack of processing power, but a fundamental failure to understand how attackers actually operate. If we simply feed AI more noise, it will not solve the underlying problems. Instead, the detection engineer will evolve from a mechanic into a conductor. While AI agents take over syntax and historical data matching, human experts will be freed up to focus on what technology currently cannot do: apply imagination. Humans remain essential for anticipating novel attacks, developing fresh hypotheses for unprecedented methods, and driving architectural changes after an incident occurs. Ultimately, AI might drive the vehicle, but organizations will still rely on experienced professionals to set the destination and guide the overall security strategy.

From Mobile Developer to Technology Leader: What 12 Years of Building Digital Products Taught Me About Enterprise Scale

Over twelve years of building digital products, the author’s perspective shifted from simply writing code to understanding how technology serves the broader business. Early in a developer's career, the focus is entirely on implementation details and framework choices. However, scaling applications for large organizations reveals that technical decisions are fundamentally business decisions. A successful architecture does not start with picking a new tool; it always begins with understanding the core business problem, the users, and the constraints. For example, ensuring an application works offline is not a simple feature to add later, but a foundational design choice. Similarly, while choosing cross-platform tools can save valuable time, the real goal is to improve maintainability and adaptability. Understanding how a system behaves in the real world is essential, meaning teams must track stability, performance, and actual impact on users. Security must be built into the daily workflow rather than checked at the very end. Furthermore, automating releases provides much-needed reliability, which frees up time for solving more important problems. Managing external vendors also requires a solid grasp of both technical delivery and project scope. Ultimately, moving into technology leadership means shifting focus from owning specific code to taking full responsibility for the overall outcome.

Daily Tech Digest - July 14, 2026

Quote for the day:

"Goals are for people who care about winning once. Systems are for people who care about winning repeatedly." -- James Clear

🎧 Listen to this digest on YouTube Music

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

Digital devolution and taking back control

The article discusses the shift from highly centralized technology management to a model of digital devolution, where local organizations regain control over their systems and data. For many years, massive top down technology contracts locked public sector and enterprise groups into rigid, monolithic platforms that often failed to address specific local needs. Now, there is a growing movement to push decision making, budget, and technical authority away from the center and back into the hands of the people actually delivering frontline services. By taking back this control, local departments can choose modern, flexible tools that solve their unique operational problems. However, this decentralized approach does not mean a return to isolated silos. Instead, it relies heavily on open standards, shared data registries, and common technical platforms to ensure that different local systems can still talk to one another smoothly. This transition requires a careful balance between giving local leaders the freedom to innovate and maintaining enough central coordination to prevent any overlapping financial costs and security risks. Ultimately, giving power back to local teams enables much faster responses to user needs, reduces reliance on expensive older legacy vendors, and builds a more resilient technology landscape across the entire broader organization.

Mastering NHS Risk Management: A Guide to Best Practice

The article outlines how NHS boards can transition from treating risk management as a passive compliance exercise to using it as an active tool for institutional assurance. Often, executive teams rely on massive risk registers that blur the line between critical threats and minor operational friction. Instead, boards need a unified framework that actively drives real-world decision-making. A central theme is the need to break down silos between clinical care, financial stability, and digital security, treating them as an interconnected triad. A failure in finances or data security inevitably compromises patient safety. For example, with over 260,000 cyber attacks recorded in early 2026 and the increasing use of artificial intelligence, digital risk is now a direct threat to clinical outcomes. To build true resilience, the article advises leaders to use their Board Assurance Framework not just to record problems, but to demonstrate clear, evidenced progress toward long-term strategic goals, such as those in the 10-Year Health Plan. Ultimately, effective governance requires boards to replace bureaucratic rituals with practical judgment and institutional memory, ensuring that every identified risk leads to a deliberate action to either mitigate a threat or enable an opportunity for better healthcare delivery.

Routine maintenance as a failure vector in modern networks

In today's highly interconnected technology environments, "routine" network maintenance is no longer a low-risk activity. While planned updates, such as firewall adjustments, DNS modifications, or certificate renewals, are meant to improve system reliability, they often trigger unexpected outages. This happens because modern networks are incredibly complex, and a single user transaction now crosses multiple layers, including load balancers, security policies, and routing protocols. Consequently, a change to just one device can easily break a hidden dependency elsewhere in the traffic path. The core issue is that teams typically test only the specific component they changed, rather than verifying the complete traffic flow. Preliminary checks and isolated test environments are helpful, but they rarely mirror the true conditions of a live network. To prevent these maintenance induced failures, professionals need to map out traffic paths completely before making any changes. They should also establish clear expectations for how systems will react and prepare precise rollback plans that go beyond simply reverting a configuration. Ultimately, organizations must stop viewing maintenance as a simple checklist of isolated device updates. Instead, every maintenance window should be treated as a practical exercise in network resilience, requiring collaboration across security, application, and operations teams to ensure continuous service.

Hacker Conversations: Jesse McGraw (GhostExodus), From Blackhat Hacker to Redemption

Jesse McGraw, formerly known as the malicious computer hacker GhostExodus, underwent a profound transformation from a cybercriminal to a dedicated cybersecurity advocate. His journey began in high school, where a profound sense of isolation and neurodivergence fueled his obsession with technology. He discovered a talent for breaking rules and bypassing systems, driven primarily by the thrill of unauthorized access rather than financial gain. Lacking a clear moral compass regarding digital boundaries, his exploits steadily escalated. This culminated in his leadership of a hacker group and a dangerous breach of a Dallas medical facility network. After he recklessly posted a video of the hack online, a security researcher used open source intelligence to identify him, leading to McGraw's arrest and an eleven year prison sentence. This lengthy incarceration forced a pivotal realization about the real world consequences of his actions and the severe impact on victims. Today, McGraw channels his skills toward positive outcomes. Instead of breaking into networks, he utilizes open source intelligence to identify online predators and protect children. Acting as a bridge between the underground hacker community and the legitimate security industry, he educates the public on safe computing practices and works to prevent attacks on critical infrastructure.

Turning the Tables on Email Scammers With 'ScamBuster'

Instead of deleting scam emails, organizations can now use ScamBuster to fight back. Designed by software engineer Laurent Giovannoni, ScamBuster is an open-source, AI-driven system that engages with phishing attackers to gather intelligence. It uses large language models to adopt various personas—such as an elderly widow or a busy executive—to trick scammers into thinking they have successfully found a target. The AI learns which personas are most effective and adjusts its approach to extract valuable data like bank account numbers, payment domains, and phone numbers. ScamBuster operates strictly on an inbound basis, meaning it only replies to incoming emails. Once it extracts the attacker's information, the system structures the data into standard threat intelligence formats, such as STIX 2.1 and MISP. Security teams and law enforcement can then use this intelligence to link different scams together and build profiles of cybercriminal operations. Scheduled for release at Black Hat USA 2026, ScamBuster is designed to be affordable and is compatible with any preferred AI model. Giovannoni is also developing updates to address vishing and smishing attacks, extending the tool's capability to combat multiple forms of social engineering.

Is that QR code a trap? How to spot quishing scams before it's too late

Quishing, or QR code phishing, is a growing modern scam where attackers trick people into scanning malicious QR codes. These specific codes usually lead to fraudulent websites designed to steal sensitive information like passwords, credit card numbers, or personal data. Scammers often place fake QR codes over legitimate ones on parking meters, restaurant menus, or public transit stations. They also send them through emails or physical mail, pretending to be from trusted sources like banks or delivery services. To protect yourself, treat QR codes with the same caution as email links. Before scanning, physically inspect the code; if it is printed on a sticker placed over another code, avoid it. Use your phone's built-in camera app rather than a third-party QR scanner, as native cameras usually display the destination URL before opening it. Review the URL carefully for subtle misspellings or odd domain names that mimic real brands. If a scanned code asks for login credentials or payment information, stop and navigate to the official website manually instead. Finally, keep your smartphone's operating system updated, as this ensures you have the latest built-in security features. By staying observant and verifying links, you can easily avoid these deceptive QR code scams.

Your AI risk register is not an incident response plan

Many organizations mistakenly treat a list of potential AI risks as an actual plan for managing failures. While documenting risks creates helpful visibility, a spreadsheet cannot investigate, contain, or resolve a problem when an artificial intelligence system breaks down in a live environment. To properly manage these systems, security teams need a practical response plan that dictates exactly what to do when an issue occurs. Unlike traditional security breaches involving unauthorized access or stolen data, AI failures are often messier. They might look like a misleading summary, a flawed recommendation, or a bad automated decision. Because of this, organizations must define what counts as an AI incident and establish clear ways for employees to report these events. Additionally, investigating these issues requires evidence. Organizations must ensure that logs, prompt histories, and system outputs are captured before moving AI tools into active use. Most importantly, clear ownership is essential. Someone must have the explicit authority to pause or restrict an AI system if it starts producing harmful or unreliable results. Ultimately, security leaders must bridge the gap between acknowledging potential problems and being operationally prepared to fix them by creating a clear, realistic response playbook for their organizations to follow.

Building AI Agents? Here Are Some Anti-Patterns to Avoid.

When building artificial intelligence agents, projects often fail not because of the underlying models, but due to preventable structural and operational mistakes. To build reliable systems, it is essential to start simple and scale complexity only when necessary. A common error is adopting a complex, multi-agent setup early when a single, well-scoped agent with clear responsibilities would suffice. Similarly, overloading an agent with too many tools or expecting it to handle every possible task makes it inefficient and prone to errors. Instead, provide a minimal set of distinct tools and focus on specialized tasks. Another key issue is hardcoding rigid logic rather than building modular components that are easy to update. Furthermore, a solid memory design is vital; agents need to recall past steps to navigate complex tasks effectively. On the operational side, releasing agents without clear visibility into their decision-making processes makes fixing problems incredibly frustrating. It is also crucial to limit their ability to make permanent changes without human oversight, carefully manage the information they process over long tasks to avoid confusion, and rigorously test them against unexpected scenarios before launch. By addressing these pitfalls, you can create practical tools that consistently deliver the desired results in everyday applications.

CIOs must rethink operating models to unlock AI at scale

Many organizations face immense pressure to implement AI at scale, but their current operational foundations often aren't ready. While AI technology is advancing rapidly, businesses are struggling with a "readiness gap" caused by issues like data quality, disjointed operating models, and a lack of proper skills and governance. CIOs must rethink their operating models to close this gap. This requires moving away from traditional, siloed technology playbooks toward a tighter partnership between IT and business teams. AI thrives on clarity, and organizations need to redesign their end-to-end workflows rather than just bolting AI onto existing processes. Data readiness is a critical first step; companies must focus on improving data quality, standardizing procedures, and managing the new information generated by AI tools. Furthermore, successful AI scaling requires executive sponsorship, clear communication to address employee fears, and governance that is embedded directly into the operating model rather than treated as an afterthought. Transitioning from small proofs of concept to full production demands a strategic shift in how teams work together. Ultimately, unlocking AI's potential is a team effort that relies on intentional design, continuous upskilling, and a strong, integrated foundation.

Why SBOMs, signing, and provenance still don’t tell you if software is safe

While current software security practices like tracking components and verifying origins are helpful, they are no longer enough to keep systems safe. Tools that show what is inside a program or prove who made it do not answer the most important question: what the code will actually do once it is running. A program might have a verified source and a clean list of ingredients, yet still attempt to steal passwords or expose private data. This gap in security is becoming more urgent as artificial intelligence allows both safe and harmful code to be written and changed faster than humans can review. We cannot assume software is safe just because it comes from a known publisher or looks familiar. Instead, we need to stop trusting software based only on its identity or background. The next step is to evaluate how the code behaves before allowing it to run. We must check if its actions, such as accessing sensitive files or connecting to outside networks, are necessary and appropriate for its purpose. By adopting a mindset where no code is trusted by default, we can focus on verifying behavior rather than just origin, creating a more reliable defense against modern threats.