Daily Tech Digest by Kannan Subbiah: security architecture

Showing posts with label security architecture. Show all posts

Daily Tech Digest - August 29, 2026

Quote for the day:

“You may be disappointed if you fail, but you are doomed if you don’t try.” -- Beverly Sills

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Duration: 22 mins • Perfect for listening on the go.

Why digital twins need memory as much as AI agents

Digital twins are evolving from passive virtual mirrors into active decision environments, making their underlying data structures more critical. As artificial intelligence agents are introduced into these environments, they must evaluate complex layers of information such as sensor data, equipment dependencies, and historical records to make sound operational decisions. However, AI agents demand more than standard data access; they require durable, long term memory. Rather than forcing information into prompt windows or attaching separate storage systems, organizations should treat agent memory as primary data within the twin itself. This approach means accurately tracking the source of every fact, its historical context, and its validity over time. Crucially, when new information contradicts an older belief, the system should not simply overwrite the past. Instead, it must retain the original data and link it to the update. Preserving this chain of reasoning creates an essential audit trail that builds trust and supports proper governance. To handle this complexity at scale, unified data foundations are necessary to seamlessly link documents, temporal states, and structured records. Ultimately, the challenge is no longer just building the digital model, but constructing the comprehensive memory around it, ensuring that human operators and machines can act with complete confidence.

AI Slop in the Enterprise: What Happens When Engineers Stop Reviewing AI-Generated Code

AI slop in enterprise software engineering refers to low-quality, AI-generated code that appears functional on the surface but introduces hidden defects, security flaws, and severe maintenance burdens. This phenomenon occurs when developers use AI tools to generate code much faster than teams can responsibly review it. Consequently, pull requests accumulate, and code is frequently merged without thorough human oversight. Because AI-generated code lacks clear human intent, reviewing it requires significantly more effort to identify subtle architectural errors, ultimately doubling review times and placing a heavy burden on senior engineers. This growing review tax leads to burnout and a divide between responsible developers and those who submit AI output without understanding it. The business impact is substantial. Studies show that while AI increases coding volume, it also introduces security vulnerabilities at a vastly accelerated rate, with nearly half of AI-generated samples containing fundamental flaws. Furthermore, unmanaged AI code can quadruple technical debt by the second year, silently embedding architectural mistakes that slow down future development. To solve this problem, enterprises must shift their focus from raw coding speed to strict governance. Solutions involve implementing visible quality metrics, enforcing architectural fit, and applying automated rule sets to verify AI output before human review even begins.

The three layers of agentic AI security: A defense-in-depth architecture for autonomous agents

The VentureBeat article outlines a comprehensive security architecture designed to address the specific risks of autonomous AI agents. Traditional security measures fall short because these agents operate independently and can inadvertently cause data leaks or execute unintended commands. To manage these new risks, the piece proposes a security model built on three distinct layers. First, the infrastructure layer establishes a secure foundation by verifying the physical and digital environments where agents run. By using methods such as hardware level trust and secure isolation, this step ensures that only authorized workloads operate, which is especially important for regulated industries like finance. Second, the network layer manages how agents communicate with other systems and data sources. Because agents generate complex and dynamic traffic patterns, traditional static network rules no longer work. Instead, organizations must adopt dynamic, strict access policies that closely control internal movement and data retrieval. Finally, the control plane acts as the central management hub for permissions and resource allocation. This layer enforces rules consistently across the entire system, preventing agents from using unauthorized tools or consuming excessive computing power. Together, these three layers provide a structured approach to securing independent AI systems, allowing organizations to maintain effective control and continuous oversight.

The Board’s Role in Crisis Management and Scenario Planning

In an era of unpredictable disruptions, a board of directors must shift from merely reacting to crises to actively preparing for them. The core responsibility of the board in crisis management is oversight and strategic guidance, rather than day-to-day execution. While senior management is tasked with implementing response plans when an emergency strikes, the board ensures that robust frameworks, ethical standards, and clear communication channels are already established. A critical tool in this proactive approach is scenario planning. By anticipating potential threats, ranging from financial downturns and operational failures to reputational damage, boards can guide management in developing practical response strategies before a crisis occurs. This involves conducting regular risk assessments and participating in crisis simulations to build organizational resilience. Scenario planning helps uncover hidden vulnerabilities and tests the effectiveness of current policies, allowing companies to respond swiftly and confidently when real challenges arise. Furthermore, effective governance during a crisis requires clear decision-making processes and an unwavering commitment to the company's long-term stability. After a crisis, the board must also lead the review process to identify lessons learned and improve future readiness. Ultimately, strong board leadership transforms crisis management from a frantic scramble into a structured, reliable process that protects the organization and its stakeholders.

Most Organizations Declare Victory Over a Breach Too Early

When dealing with a security incident, business leaders often feel pressured to return to normal operations as quickly as possible. This pressure leads many organizations to declare victory over a breach long before the threat is fully removed. In their rush to restore services, response teams typically address the most obvious signs of an attack, such as isolating a compromised server or resetting user passwords. However, stopping the investigation at this early stage is a critical mistake. Intruders often establish hidden backdoors, create secondary accounts, or move laterally across the network well before the initial detection occurs. If responders fail to conduct a thorough forensic analysis, these hidden footholds remain active, allowing the attackers to quietly regain access days or weeks later. To effectively resolve a cyber incident, organizations must shift their focus from mere speed to complete threat eradication. This requires committing to extended monitoring and ensuring that all affected systems are deeply analyzed for residual threats. Teams should wait until they have clear evidence that the environment is genuinely secure before announcing that the crisis has passed. By taking a careful, methodical approach to recovery, companies can better protect themselves from falling victim to the exact same intruders twice.

Architects Will Define the Next Generation of IT

Artificial intelligence is fundamentally changing how enterprise software is built, shifting the industry away from large, specialized teams toward smaller, highly skilled groups. At the center of this shift is the IT architect. Rather than simply overseeing design, architects are returning to direct implementation. AI tools allow them to compress the traditional software process into a single, continuous loop that includes analysis, design, coding, testing, and deployment. To succeed today, these architects must combine a deep understanding of business operations with strong technical judgment. By using AI to close the gap between an initial idea and working software, small, architecture-led teams can deliver solid results in a fraction of the time. For instance, a recent legacy system update was finished in just five months instead of the usual two years, without sacrificing basic security, data integrity, or accuracy. This newfound efficiency completely changes the underlying economics of technology development. Traditional systems integrators and major software providers that rely on large staffs and lengthy timelines will face serious market pressure. Highly experienced professionals equipped with modern tools can now build complex systems much faster and more affordably. Consequently, business leaders must rethink their approach to building and buying technology before smaller, more capable competitors outpace them.

Offensive Security Investments Surge as AI Threats Increase

In a recent interview at Black Hat USA 2026, Omdia analyst Theresa Lanowitz shared findings on how artificial intelligence is shifting the landscape of cybersecurity. She notes that older methods like standard penetration testing and simulated attacks are no longer enough to keep up with the speed at which threats operate today. Because of this, organizations are rethinking their defense strategies and increasing their investments in offensive security. In fact, research shows that a vast majority of companies are willing to spend more to gain continuous visibility and better track devices across their networks. However, deploying automated tools for defense introduces its own set of challenges. Companies are rightly concerned about the risks of these systems behaving unpredictably, falling victim to manipulative inputs, or simply driving up costs. To manage these risks, experts recommend establishing strict boundaries to limit the potential damage if a system goes off track. Furthermore, securing the software supply chain has become incredibly critical. While nearly all organizations recognize its importance and are investing heavily in it, less than half are documenting their software components during the build process. Ultimately, business leaders are prioritizing overall resilience to ensure they can withstand and recover from unexpected incidents.

CTEM can give your security team a contextual edge

Traditional vulnerability management relies on periodic assessments and patching, but this approach is no longer enough to keep up with fast-moving cyber threats. Many security teams are now turning to continuous threat exposure management (CTEM) to stay ahead. Unlike standard scanners that only flag software flaws, CTEM takes a much broader view of an organization's actual risk. It actively monitors for misconfigurations, identity risks, and excessive permissions across cloud environments, applications, and networks. A major advantage of this continuous model is that it focuses on validation and action. Instead of simply generating long lists of potential issues, it helps teams determine whether a vulnerability is truly exploitable under their current defenses. It also ensures specific people are assigned to fix the most critical problems, shifting the goal from counting flaws to actually closing attack paths. To work well, this approach relies heavily on automation and contextual intelligence, combining technical data with business priorities. However, adopting this new model requires significant cultural shifts. Security leaders must overcome tool fatigue, break down departmental silos, and change their teams' mindsets. Rather than just hunting for every single technical error, the focus must shift toward steadily reducing the overall risk to the core business.

Cybersecurity has become an operational discipline

Cybersecurity in manufacturing is no longer just an IT concern; it is a fundamental operational discipline. When a cyber incident strikes a factory, it halts production, impacts product quality, and compromises worker safety. Because modern facilities connect legacy machinery with cloud services, robots, and artificial intelligence, the boundaries of the factory floor have expanded. This creates new vulnerabilities, yet many companies still rely on traditional IT security methods. Standard IT practices, like aggressive scanning and immediate patching, can actually disrupt continuous manufacturing processes. Instead, protecting operational technology requires a different approach focused on system availability, using passive monitoring and protective architecture around older equipment rather than replacing it. A major challenge is the division of responsibility between IT, engineering, and plant operations, which often leaves critical decisions unresolved during an attack. To build real resilience, plant leaders need clear ownership of cyber risks, treating them with the same importance as workplace safety and product quality. By developing specific response plans before an incident occurs, teams can drastically reduce recovery time. Ultimately, manufacturers must merge technical threat knowledge with practical engineering experience to ensure that their facilities run reliably and securely in an increasingly connected world.

Security Readiness Looks Good On Paper. Investigations Say Otherwise

Organizations frequently overestimate their cybersecurity readiness, assuming that purchasing an array of security tools makes them safe. In reality, the true strength of a security program is only revealed during an actual breach, which often exposes a gap between what leaders believe and what is actually happening. Many companies buy defenses like endpoint detection or backup systems but fail to fully implement or monitor them around the clock. Attackers capitalize on these cumulative, minor weaknesses, such as delayed updates or lingering credentials, rather than relying on a single sophisticated exploit. Furthermore, detecting threats has become increasingly difficult as attackers use stealthy methods and artificial intelligence to blend their movements with normal daily operations. Instead of waiting for a breach to happen to secure funding and buy the newest marketed tools, leaders should adopt a proactive mindset. This means asking what protective measures they would wish they had in place if an attack happened tomorrow. By relying on forensic evidence from actual incidents rather than theoretical product demonstrations, companies can focus on battle-tested solutions and practical fixes. Closing the gap between perceived readiness and actual defense capabilities allows organizations to address their vulnerabilities before attackers can exploit them.

Daily Tech Digest - July 19, 2026

Quote for the day:

“The best startups are the ones that take something that already works and improve it dramatically.” -- Peter Thiel

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Duration: 22 mins • Perfect for listening on the go.

The Refactoring You Keep Deferring Is Not Technical Debt — It’s Architecture Risk

The article argues that many engineering teams mislabel certain long‑postponed refactoring tasks as technical debt when they are actually signs of deeper architectural risk. Technical debt, the author explains, is about how code is written. It creates friction, slows development, and increases the cost of change, but the system still does what it was designed to do. Architecture risk is different: it reflects structural assumptions baked into the system—limits on throughput, data model constraints, or tightly coupled components—that only become visible when the business needs the system to do something new. The piece shows how teams often confuse the two because both appear as “cleanup” work and both get deferred for similar reasons. But the consequences diverge sharply. Technical debt can be addressed gradually, module by module. Architectural constraints often require redesigning entire parts of the system, which demands planning, ownership, and honest communication with stakeholders. The author offers a simple test: if rewriting the code cleanly using the same structure would not remove the limitation, the issue is architectural. The article encourages teams to identify structural assumptions early, map how they limit future directions, and treat high‑impact constraints as real risks rather than backlog chores.

Brain-Machine Interface Identifies, Amplifies Conversations Amid Noise

A new brain-computer system developed by researchers at Columbia University helps people follow specific conversations in noisy environments. Traditional hearing aids often struggle in crowded rooms because they amplify all sounds equally. To solve this, scientists created a device that constantly monitors a person's brain activity alongside surrounding audio to figure out which voice the listener wants to hear. Once it identifies the target, the program automatically turns up the volume on that specific conversation while turning down competing background noise. Researchers tested the technology using four patients who already had electrodes temporarily placed in their brains for other medical reasons. During the trials, the equipment successfully adjusted the audio in real time, even when listeners intentionally shifted their attention from one speaker to another. Participants reported that understanding speech became much easier, and measurements of their pupils confirmed they expended less effort to listen. When the recorded audio was played for people with hearing loss, they also experienced significant improvements in speech clarity. While this early version relies on invasive electrodes to gather high-quality brain signals, the results offer a clear foundation for future hearing devices that might adapt to an individual's focus using less invasive technology and methods.

SABSA framework for risk-driven security architecture: a practical guide for UK SMEs

The SABSA framework helps organizations build a security architecture that directly connects business risks to technical solutions. Unlike a rigid checklist or a product guide, SABSA ensures every security measure has a clear, explainable purpose. It asks fundamental questions about what needs protection, potential threats, and required security properties. This framework is particularly valuable for small and medium-sized enterprises because it encourages pragmatic decision-making, helping to avoid duplicated tools or neglected controls. SABSA utilizes a layered approach that progresses from broad business attributes to specific technical implementations. It starts by defining necessary business qualities, such as availability or confidentiality, and then determines the required security objectives. From there, it outlines logical mechanisms and finally maps them to actual technologies and configurations. This layered method ensures strong traceability, making it easy to justify why a specific control exists. When applying SABSA, businesses should identify their most critical services, analyze potential threats, and define control objectives based on their specific risk appetite. By focusing on proportionate controls that balance protection, usability, and operational cost, small teams can effectively implement SABSA one critical service at a time, resulting in a coherent and practical security design.

The AI coding rollout worked. Now CIOs have a bigger problem

Although artificial intelligence tools are widely used by developers today, the expected massive boost in productivity has yet to materialize. Instead of simply speeding up how fast code is written, these tools are fundamentally changing what developers do every day. Writing code is no longer the primary bottleneck or the most crucial skill. Developers are shifting away from manual programming and spending more of their time designing systems, validating outcomes, and reviewing work generated by the machine. While raw coding speed has improved, companies are discovering that artificial intelligence code often takes much longer to review and contains more security vulnerabilities. This shift also introduces a serious long-term problem for the industry. Routine tasks like bug fixes and writing tests—the exact work that junior developers traditionally used to learn their craft—are now handled by software. If companies stop hiring entry-level engineers because machines can do their work, they will face a severe shortage of experienced senior staff in the coming years. To succeed, organizations must stop focusing solely on how much code is generated. Instead, they need to redesign their development processes around strong governance, clear business outcomes, and new ways to mentor the next generation of engineers.

The Pulse: What can we learn from Bun’s rapid Rust rewrite with AI?

The creator of the Bun software project recently completed a massive code rewrite from the Zig programming language to Rust in just eleven days using artificial intelligence. Originally, Bun relied on Zig, which caused persistent memory errors and system crashes. Rust promised to solve these stability problems by handling computer memory more safely. However, manually rewriting over half a million lines of code would have taken a team of developers at least a year, severely delaying new features and updates. Instead, the team used an advanced artificial intelligence model named Fable to automate the heavy lifting. The process started with strict guidelines, followed by dividing the workload across sixty four independent artificial agents. These agents translated the code, reviewed their work, and resolved thousands of compilation errors while the human developers slept. After a few days of getting the automated tests to pass, the project was finished. Although the computing cost reached one hundred sixty five thousand dollars, it remains significantly cheaper and faster than paying a team of engineers for a year of manual labor. This achievement demonstrates that large software migrations are now highly practical, provided a team maintains strong testing practices and a clear technical strategy.

The vertically integrated neocloud

Iren, once known for Bitcoin mining, has reinvented itself as a builder of very large data centers aimed at supporting AI workloads. The company believes its vertically integrated approach—owning the land, the power infrastructure, and the data centers themselves—lets it move faster and avoid the delays that come from relying on outside colocation providers. After converting its Canadian sites to support AI, Iren is now focused on the US, where it is developing several massive campuses. Its Texas footprint already includes 750MW in Childress, with two Sweetwater sites planned to reach 2GW. Another 1.6GW site is scheduled for Oklahoma in 2028. Keeping these projects geographically close helps the company maintain a stable workforce and contractor base during a period of intense competition for skilled labor. Iren builds and procures equipment ahead of customer commitments, which carries risk but has paid off—most notably through a large cloud contract with Microsoft. Early procurement also helps the company secure scarce components like high‑voltage gear and GPUs. Iren argues that some customers are rethinking their redundancy requirements, especially for AI training, where occasional interruptions are manageable. The company sees its track record of delivering capacity on time as a key advantage in a rapidly expanding and often over‑promising neocloud market.

Sovereign AI: Building AI Where Data, Infrastructure, and Control Stay Aligned

The article explains why many organizations are rethinking how they build and run AI systems, especially when sensitive data and strict regulations are involved. As AI moves from experiments into everyday operations, companies need more control over where data is stored, how models are run, and who can access the underlying infrastructure. The authors describe “sovereign AI” as an approach that keeps data, operations, and governance within clear boundaries rather than relying solely on contractual promises. They outline the kinds of information AI systems generate—such as prompts, embeddings, logs, and model artifacts—and note that these can be just as sensitive as primary business data. The piece argues that sovereignty is not only about compliance; it can help organizations gain trust, reach regulated markets, and scale AI safely. It also lays out architectural principles for maintaining control, including isolation of environments, strict rules for AI‑related data, and choosing an operating model that fits local requirements. The article then shows how Oracle’s cloud offerings support different sovereignty needs, using SoftBank’s Japan‑based deployment as an example of keeping AI infrastructure and operations within national boundaries. Overall, it presents sovereign AI as a practical way to align technology, regulation, and organizational responsibility.

Why Cyber Resilience Is Becoming Critical in AI-Led Enterprise Transformation

As businesses increasingly rely on artificial intelligence to manage everything from customer service to financial forecasting, the approach to digital security must fundamentally change. While these intelligent systems offer significant advantages, they also expose vast amounts of sensitive data and create new vulnerabilities. Traditional security measures designed merely to keep attackers out are no longer sufficient, especially since hostile actors are now using the same advanced tools to launch sophisticated, adaptable attacks. Instead of assuming every threat can be blocked, companies must shift their focus toward complete resilience. This means accepting that breaches will eventually occur and building robust systems that can quickly detect issues, limit the damage, and recover operations without major interruptions. Ensuring the integrity of the data that feeds these systems is critical, as flawed information easily leads to bad decisions and reputational damage. Furthermore, security can no longer be treated as an optional feature added at the end of a project. It must be woven directly into the core design of every network. Because these risks directly impact overall revenue and regulatory compliance, protecting the organization is no longer just a technical issue for the technology department; it has become a central responsibility for the entire leadership team. entire executive. central responsibility for the entire leadership team.

The Future of Age Verification: Your Face Never Leaves Your Device

As governments worldwide enact strict age verification laws for online platforms, facial age estimation has become a popular compliance tool. However, this method traditionally requires sending user photos to external servers, which creates significant privacy risks and attractive targets for data breaches. To solve this problem, a company named Incode has developed a new age verification system that processes facial data entirely on the user's device. By shrinking their artificial intelligence models, they enable everyday devices like smartphones and computers to estimate a user's age locally without ever transmitting or storing the actual image of the face. Only the final age verification result and basic session data are sent to the platform, ensuring privacy through system architecture rather than just written policies. This session data helps block sophisticated fraud attempts, such as deepfakes or camera tampering, without compromising personal biometrics. Alongside this technology, Incode recently invested one hundred million dollars into privacy infrastructure, including the acquisition of Identiq. This partnership allows organizations to share critical fraud intelligence without pooling raw customer data into vulnerable centralized databases. Ultimately, these advancements allow platforms to meet growing legal requirements for age assurance while keeping sensitive biometric data strictly in the hands of the user.

Restoration of a 20-year-old Java “Big Ball of Mud” using AI and Docker

When tasked with modernizing a legacy codebase—in this case, a twenty-year-old Java repository—developers often fall into the "tourist trap." They ask generative artificial intelligence for a quick fix or a modern starter kit. The machine eagerly obliges, offering modern build files and updated dependencies that look pristine but are fundamentally disconnected from the actual architecture. This optimistic approach masks deep structural rot, such as outdated APIs, non-standard directory layouts, and hidden concurrency issues, leading developers down a frustrating path of debugging code that was never meant to be modernized in one step. To succeed, engineers must adopt an "archaeologist" mindset, using artificial intelligence not to generate new code, but to perform a forensic audit. By prompting the tool to analyze the era of the code, structural integrity, data flow, and error handling, developers can accurately assess the system's true health. In this project, the audit revealed a fragile system masquerading as Java, riddled with string-based typing and deceptive test coverage. Rather than immediately refactoring, the correct strategy was complete containment: wrapping the untouched legacy code in a stable Docker environment mimicking its original era. This creates a reliable baseline, proving that artificial intelligence is most effective when constrained by evidence and strict modernization phases.

Daily Tech Digest - June 29, 2026

Quote for the day:

"People don't need leaders who protect them from every challenge. They need leaders who help them believe they can handle the challenge." -- Gordon Tredgold

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

The Hidden Currency of AI: Why Tokens Make or Break Enterprise Systems

Tokens are the hidden but fundamental currency of modern artificial intelligence systems, acting as the basic units of text that determine both the cost and performance of enterprise AI deployments. Every interaction with a language model consumes tokens, which are pulled from a finite context window. While large context windows exist, models often struggle to process information buried in the middle of long prompts. Because AI providers charge for every token sent to and generated by a model, unchecked usage can quickly lead to massive budget overruns. Organizations frequently make three main mistakes: allowing chat histories to grow indefinitely, feeding too many unnecessary documents into the system, and failing to restrict the length of AI-generated responses. To control these costs without sacrificing quality, technical leaders should adopt basic financial hygiene measures. This includes caching repetitive instructions and taking a tiered approach to model selection, using smaller, cheaper models for routine tasks and reserving the most expensive, highly capable models for complex analysis. Ultimately, managing tokens effectively is not just an operational detail; it is a critical requirement for building scalable, secure, and financially responsible AI systems.

Forget AGI. The real prize is enterprise AGI

The artificial intelligence industry is largely chasing the wrong goal by focusing on general intelligence or superintelligence. Instead, the true economic prize is "Enterprise AGI," which is a tailored intelligence unique to each company. While many model vendors are building smarter, generalized models that offer the same baseline intelligence to everyone—a concept the authors call "data communism"—the real competitive advantage lies in "data capitalism." This approach allows businesses to turn their proprietary data, internal processes, corporate policies, and tacit human knowledge into governed, compounding assets. To achieve Enterprise AGI, companies need a system of intelligence that captures exactly how they operate on a daily basis. Databricks is highlighting this shift by moving beyond a traditional data platform to an enterprise intelligence platform. Through practical tools like Genie One—a digital assistant for business users—and the Genie Ontology, Databricks helps organizations harmonize their data and map real business meaning. By grounding artificial intelligence in authoritative, verified data assets, companies can ensure their tools reason and act within specific operational contexts. Ultimately, the winners will be those who help businesses convert their unique institutional knowledge into an actionable, differentiated intelligence system.

The New Insider Threat Isn't Human: Securing AI Agents Before They Secure Themselves

As AI agents become a central part of how we manage software and infrastructure, they are silently introducing significant new security risks. For decades, security teams have focused on protecting against human threats, like careless employees or compromised contractors. Today, however, automated machine identities vastly outnumber human ones. Rather than building tailored security protocols, many organizations take the easy route by giving these AI agents long-lasting human API keys or broad system access. This approach creates a dangerous vulnerability. If an attacker compromises an agent or manipulates its behavior through prompt injection, they gain the same extensive access the agent holds. Recent incidents highlight how easily malicious actors can hijack chatbot credentials to infiltrate interconnected networks or use compromised agents for automated espionage. Furthermore, connection frameworks meant to link agents to databases can be exploited if they rely entirely on implicit trust. The solution requires moving away from shared credentials and adopting strict authorization boundaries for software. Each AI agent needs a unique, short-lived identity restricted strictly to its specific task. By placing a clear policy enforcement checkpoint between the agent and your systems, you ensure that autonomous actions remain securely contained and properly audited.

Companies keep bolting AI onto their products, and the security bill is coming due

As companies rush to integrate artificial intelligence into their products, they are encountering significant security challenges. According to recent data from Cobalt, AI applications not only retain traditional software flaws but also introduce unique vulnerabilities. This combination results in high-risk issues occurring at nearly three times the rate of conventional systems. Unfortunately, fixing these problems is proving difficult. With the lowest resolution rate of any asset class, roughly two out of three serious AI vulnerabilities remain unfixed due to a shortage of specialized staff, immature security processes, and reliance on external vendors. Furthermore, unauthorized employee use of unapproved AI tools is now the leading cause of AI-related security incidents, as these applications easily bypass traditional corporate network scanners. Recognizing these complexities, organizations are shifting their approaches. The initial excitement for fully automated security testing has declined sharply, as teams notice that automated scanners frequently miss critical flaws. Instead, companies are increasingly relying on human experts to evaluate their most important systems. Ultimately, organizations that prioritize fixing verified, exploitable vulnerabilities rather than chasing theoretical alerts are seeing much better success in securing their environments and meeting their internal security goals.

Products That Are Not “Quantum-Safe” May Soon Be Ineligible for Cybersecurity Certification in France

Starting in 2027, developers seeking certification from France’s lead cybersecurity agency, ANSSI, may need to prove their security products are resistant to quantum computing attacks. This requirement is expected to become a universal standard by 2030. While this certification remains optional for general consumer products, it is strictly required for any technology used by the French government or critical infrastructure operators. This policy establishes France as an early leader in European cybersecurity regulation, complementing broader European Union directives. The initiative is driven by the looming threat of advanced quantum computers breaking traditional encryption methods. Although experts previously estimated this capability would arrive by 2035, recent assessments by major technology companies suggest it could happen as early as 2029. This accelerated timeline is concerning because malicious actors are already stealing encrypted data to decode it once powerful quantum computers become available. Despite these growing risks, adoption of new resistant standards has been slow. Organizations face complex challenges in upgrading existing systems, and formal standards were only recently finalized. Security professionals recommend that organizations begin planning their transition carefully, ensuring they maintain strong fundamental security practices rather than becoming distracted by future threats.

Reducing cyber risk is still hard: Why CTEM stalls at action

Many organizations struggle to actually reduce cyber risk because finding vulnerabilities is fundamentally easier than fixing them. While security teams are highly skilled at identifying threats, the responsibility for applying software patches usually falls to IT operations. This division of labor creates delays, particularly when dealing with older infrastructure where teams worry that an update might disrupt normal business operations. As a result, many modern security programs often stall out. They provide excellent visibility into potential risks but fail to drive the practical actions necessary to secure them. The current roadblocks are well documented. Security and IT teams frequently use different systems and have competing priorities, leading to extended repair timelines. Furthermore, security leaders find it difficult to communicate complex technical risks to company executives in clear financial terms. To bridge this gap, organizations need to shift their focus away from simply discovering flaws and toward managing the fixes practically. By establishing a unified system, companies can consolidate their asset data and automate fixes. When direct patching is unworkable, they can apply alternative containment measures. Ultimately, effective risk reduction requires prioritizing system flaws based on actual business and revenue impact, turning technical insight into measurable action.

Serverless Architecture

Serverless architecture fundamentally shifts how developers build applications by removing the need to manage backend infrastructure. In this cloud computing model, providers handle provisioning, scaling, and execution, allowing teams to deploy discrete units of code—functions—that are triggered by specific events. This approach is highly effective for background tasks, internal tools, and rapid prototyping, as it enables teams to focus entirely on business logic rather than server maintenance. However, serverless is not a universal solution. It imposes strict limits on execution time, making it unsuitable for long-running processes or complex workflows without careful architectural redesign. Furthermore, while it removes server management, it redistributes complexity into areas like state management, distributed communication, and transaction coordination. Functions are naturally stateless, meaning developers must rely heavily on external databases and services to maintain context. Cold starts and vendor lock-in present additional challenges that require thoughtful mitigation. Ultimately, rather than completely replacing traditional systems, serverless functions are best used as powerful building blocks within a hybrid architecture. When applied to the right workloads and isolated behind clean code boundaries, serverless computing can significantly accelerate development cycles and reduce operational costs.

12 Questions and Answers About purdue model architecture

Originally developed in 1991 as an engineering guide for manufacturing data flows, the Purdue Model has evolved into an essential security framework for industrial control systems. The architecture structures networks into a six-level hierarchy, establishing clear boundaries between physical operational technology and corporate information technology. The lowest tiers, from Levels 0 to 2, manage the physical hardware, sensors, and direct control systems on the factory floor. The upper tiers, from Levels 3 to 5, handle business management, enterprise systems, and internet connectivity. By segmenting these distinct zones, the model provides a practical blueprint for a layered defense strategy. This structured approach ensures that security breaches in corporate office networks cannot easily move laterally to disrupt critical physical machinery. As modern industries connect their formerly isolated factories to cloud networks and integrate automated tools, the security risks of bridging these environments grow significantly. Despite its age, the Purdue Model remains a highly relevant method for organizations to logically organize network defenses, deploy targeted firewalls, and safely manage the complex flow of data between enterprise offices and operational equipment.

GDPR at 10: Landmark data protections, increasing business burden

Ten years after the General Data Protection Regulation (GDPR) went into effect, the results show a clear divide between enhanced consumer privacy and growing business frustrations. On the positive side, the regulation has successfully established stronger data protection habits across Europe. Significantly more companies have adopted these standards, and consumers are far more aware of how their personal information is handled. Regulatory enforcement has also matured from high-profile, record-breaking fines into a steady review of daily operational compliance. However, the business community increasingly views the ongoing regulation as a heavy administrative burden. A vast majority of companies report that the rules make their operations far more complicated and demand a high level of continuous effort to keep up with shifting technical and legal changes. This dissatisfaction is especially visible in data-driven fields like artificial intelligence. Because AI development requires massive amounts of data, many European businesses feel that strict privacy laws put them at a serious competitive disadvantage globally. Consequently, industry leaders are calling for reforms that balance genuine privacy risks with the practical needs of technological innovation, ensuring that data protection does not needlessly stall progress.

Software Supply Chain Security Shifts Toward AI, SBOM Operations and Delivery Governance

The software supply chain security (SSCS) landscape is rapidly evolving beyond basic vulnerability checks to address complex threats from artificial intelligence, third-party software, and delivery pipelines. According to Gartner, securing software factories now requires organizations to actively manage external risks from open-source tools, commercial vendors, and AI components like large language models. Rather than just scanning for flaws, modern security practices emphasize strong governance across the entire software lifecycle. A central element of this shift is the operational use of Software Bills of Materials (SBOMs), moving past simple document generation to continuous analysis, lifecycle management, and downstream sharing. Additionally, businesses must evaluate whether their security tools can automate remediation, enforce policies directly within developer workflows, and reliably handle external code dependencies. Protecting the supply chain now means ensuring software delivery infrastructure is fully auditable while integrating safeguards into source control and deployment systems. By treating software security as a comprehensive control layer from acquisition through delivery, organizations can better mitigate risks and confidently protect their intellectual property against emerging external and AI-related threats.

Daily Tech Digest - October 14, 2025

Quote for the day:

"What you get by achieving your goals is not as important as what you become by achieving your goals." -- Zig Ziglar

Know your ops: Why all ops lead back to devops

When you see more terms that include the “ops” suffix, you should understand them as ideas that, as Graham Krizek, CEO of Voltage, puts it, “represent different layers of the same overarching goal. These concepts are not isolated silos but overlapping practices that support automation, collaboration, and scalability.” ... While site reliability engineering (SRE) and infrastructure as code (IaC) don’t have “ops” attached to their names, they can be seen in many ways as offshoots of the . SRE applies software engineering techniques to operations problems, with an emphasis on and . IaC shops manage and provision infrastructure using machine-readable definition files and scripts that can be version-controlled, automated, and tested just like application code. IaC underpins devops, , and many specialized ops practices. ... “While it is not necessary for every IT professional to master each one individually, understanding the principles behind them is essential for navigating modern infrastructure,” he says. “The focus should remain on creating reliable systems and delivering value, not simply keeping up with new terminology.” In other words: you don’t need to collect ops like trading cards. You need to understand the fundamentals, specialize where it makes sense, and ignore the rest. Start with devops, add security if your compliance requirements demand it, and adopt practices if you’re heavily in the cloud.

Digital Trust as a Strategic Asset: Why CISOs Must Think Like CFOs

CFOs are great at framing problems in terms of money. CISOs must also figure out how much risks cost, what not taking action costs, how much revenue loss comes from , and how much it will cost to recover. Boards want the truth, not spin. Translate technical metrics into business impact (e.g., how detection/response times and dwell time drive incident scope and recovery costs). Recent threat reports show global median dwell time has fallen to ~10 days, but impact still depends on speed of containment. ... Stop talking about technology. Start describing cybersecurity as keeping your business running, protecting your reputation and building consumer trust – not simply operational disruption, but also how risk scenarios affect P&Ls. ... CISOs need to know how to read , not simply logs. This entails being able to understand risk economics, insurance models and how to allocate resources strategically. ... We are entering a new era in which CFOs and CISOs are both responsible for keeping the business running: Earnings calls that include integrated trust measures; Cyber insurance coverage that is in line with active threat modeling; Cyber posture reports that meet regulatory standards, like financial audits; and Shared leadership on risk and value initiatives at the board level. CISOs who understand trust economics will impact the futures of businesses by making security a part of strategy as well as operations.

Five actions for CISOs to manage cloud concentration risks

To effectively mitigate concentration risks, CISOs should start by identifying and documenting both , with a focus on the most critical cloud providers. It is important to recognize that some non-cloud products may also have cloud dependencies, such as management consoles or reporting engines. Collaborating closely with strategic procurement and vendor management (SPVM) leaders ensures that each cloud provider has a clearly documented owner who understands their responsibilities. ... CISOs should not rely solely on service level agreements (SLAs) to mitigate financial losses from outages, as SLA payouts are often insufficient. Instead, focus on designing applications to gracefully manage limited failures and use cloud-native resilience patterns. In , focus on short-term failure of some cloud services first, rather than catastrophic failure of a large provider and use cloud-native resilience patterns in your architecture. In addition, special attention should be given to due to their position as a large single point of failure. ... To reduce the risk associated with single-vendor dependency, organizations should intentionally distribute applications and workloads across at least two cloud providers. While single-vendor solutions can simplify integration and sourcing, a multi-cloud approach limits the potential impact of an issue affecting any one provider.

Your cyber risk problem isn’t tech — it’s architecture

The development of a — including appetite, tolerance and profile — within the scope of the management program is essential to provide real visibility into ongoing risks, how they are being perceived and mitigated, and to leverage the organization’s ability to improve its security posture. Consequently, the company begins to deliver reliable products to customers, secure its reputation and build a secure image to achieve a competitive advantage and brand recognition. ... Another important factor to be developed in parallel with raising risk culture is the continuous Information security awareness process. This action should include all employees, especially those involved in and . ... From a technical standpoint, it is important to select and implement appropriate controls from the stages: Identify, Protect, Detect, Respond and Recover. However, the selection of each control for building guardrails will depend on the overall cybersecurity big picture and market best practices. For each identified issue, the corresponding control must be determined, each monitored by the three lines of defense ... Finally, the cyber management program must also consider legal, regulatory and regional requirements, including privacy and cybersecurity laws. This covers , Central Bank regulations, etc., to understand the consequences of non-compliance, which can pose serious issues for the organization.

Even the best AI agents are thwarted by this protocol - what can be done

An emerging category of artificial intelligence middleware known as Model Context Protocol is meant to make generative AI programs such as chatbots bots more powerful by letting them connect with various resources, including packaged software such as databases. Multiple studies, however, reveal that even the best struggle to use . ... Having a standard does not mean that an AI model, whose functionality includes a heavy dose of chance ("probability" in technical terms), will faithfully implement MCP. An AI model plugged into MCP has to generate output that achieves several things, such as formulating a plan to answer a query by choosing which external resources to access, in what order to contact the MCP servers that lead to those external applications, and then structuring several requests for information to produce a final output to answer the query. ... The immediate takeaway from the various benchmarks is that AI models need to adapt to a new epoch in which using MCP is a challenge. AI models may have to evolve in new directions to fulfill the challenge. All three studies identify a problem: Performance degrades as the AI models have to access more MCP servers. The complexity of multiple resources starts to overwhelm even the models that can best plan what steps to take at the outset. As Wu and team put it in their MCPMark paper, the complexity of all those MCP servers strains any AI model's ability to keep track of it all.

Chaos engineering on Google Cloud: Principles, practices, and getting started

A common misconception is that cloud environments automatically provide application resiliency, eliminating the need for testing. Although cloud providers do offer various levels of resiliency and SLAs for their cloud products, these alone do not guarantee that your business applications are protected. If applications are not designed to be fault-tolerant or if they assume constant availability of cloud services, they will fail when a particular cloud service they depend on is not available. ... As a proactive discipline, enables organizations to identify weaknesses in their systems before they lead to significant outages or failures, where a system includes not only the technology components but also the people and processes of an organization. By introducing controlled, real-world disruptions, chaos engineering helps test a system's robustness, recoverability, and fault tolerance. This approach allows teams to uncover potential vulnerabilities, so that systems are better equipped to handle unexpected events and continue functioning smoothly under stress. ... is an open-source framework written in that provides a modular architecture where you can plug in other libraries (also known as ‘drivers’) to extend your chaos engineering experiments. ... to enable Google Cloud customers and engineers to introduce chaos testing in their applications, we’ve created a series of Google Cloud-specific chaos engineering recipes. Each recipe covers a specific scenario to introduce chaos in a particular Google Cloud service.

The attack surface you can’t see: Securing your autonomous AI and agentic systems

The deep, non-deterministic nature of the underlying Large Language Models (LLMs) and the complex, multi-step reasoning they perform create systems where key decisions are often unexplainable. When an AI agent performs an unauthorized or destructive action, auditing it becomes nearly impossible. ... When you give an AI agent autonomy and tool access, you create a new class of trusted digital insider. If that agent is compromised, the attacker inherits all its permissions. An autonomous agent, which often has persistent access to critical systems, can be compromised and used to move laterally across the network and escalate privileges. The consequences of this over-permissioning are already being felt. ... The sheer speed and scale of agent autonomy demand a shift from traditional perimeter defense to a specifically engineered for AI. This is no longer an optional security project; it is an organizational mandate for any leader deploying AI agents at scale. ... Securing Agentic AI is not just about extending your traditional security tools. It requires a new governance framework built for autonomy, not just execution. The complexity of these systems demands a new security playbook focused on control and transparency ... The future of enterprise efficiency is agentic, but the future of enterprise security must be built around controlling that agency.

Systems that Sustain: Lessons that Nature Never Forgot but We Did

In practice, a major flaw in many technology projects is that existing multi-level approval systems are simply digitalised, leading to only marginal improvements. The process becomes a of the old: while processing speeds increase, the workflow itself remains long, redundant, and often cumbersome. The introduction of a new digital interface adds to the woes rather than simplifies them. Had processes been genuinely reengineered, digitisation could have saved time by simplifying steps, reducing the training load, improving efficiency, cutting costs, and enabling quicker adaptation in response to change. Another persistent pitfall in public sector digital transformation is misunderstanding the promise of analytics, and more crucially, confusing outputs with outcomes. ... Humans, as players in nature’s game, are unique. Evolution gifted us consciousness, language, memory, and complex social bonds—traits that allowed the creation of technology, law, storytelling, and culture. Yet these very blessings seeded traits antithetical to nature’s raw logic ... Artificial intelligence presents a tantalising prospect. Unlike its human creators, a well-designed AI can, under ideal circumstances, create technologies based on the same bias-free principles that drive nature: redesign for purpose, learn and adapt from data, and commit to real, measurable outcomes.

California introduces new child safety law aimed at AI chatbots

The law is set to come into effect on Jan. 1, 2026, and requires chatbot operators to implement age verification and warn users of the risks of companion chatbots. The bill implements harsher penalties for anyone profiting from illegal deepfakes, with fines of up to $250,000 per offense. In addition, technology companies must establish protocols that seek to prevent self-harm and suicide. These protocols will have to be shared with the California Department of Health to ensure they’re suitable. Companies will also be required to share statistics on how often their services issue crisis center prevention alerts to their users. Some AI companies have already taken steps to protect children, with recently introducing parental controls and content safeguards in , along with a self-harm detection feature. Meanwhile, has added a disclaimer to its chatbot that reminds users that all chats are generated by AI and fictional. Newsom is no stranger to AI legislation. In September, he signed into law another bill called , which mandates greater transparency from AI companies. More specifically, it requires AI firms to be fully transparent about the safety protocols they implement, while providing protections for whistleblower employees. The bill means that California is the first U.S. state to require AI chatbots to implement safety protocols, but other states have previously introduced more limited legislation.

Embedding Security into Enterprise Architecture: A TOGAF-Based Approach to Risk-Aligned Design

Treating security as a separate discipline leads to inefficiencies, redundancies, and vulnerabilities. Bolting on security after systems are designed often results in costly retrofits, fragmented controls, and misaligned priorities. It also creates friction between teams — where security is seen as a blocker rather than a partner. Integrating ESA into EA from the outset changes the dynamic. It ensures that security is considered in every architectural decision — from business processes to data flows, from application design to infrastructure deployment. It aligns security with business goals, reduces risk exposure, and accelerates delivery. ... ISM brings operational rigor to ESA. It defines how security is implemented, monitored, and improved. ISM includes identity and access management, continuity planning, compliance management, and security awareness. When ISM is integrated into EA, security becomes part of the enterprise fabric. It’s not just a set of policies — it’s a way of working. ... This integration is not a technical adjustment — it’s a strategic evolution. It requires collaboration, shared language, and a commitment to embedding security into every architectural decision. When done right, it reduces risk, accelerates delivery, and builds confidence across the enterprise. Security by design is not a luxury — it’s a necessity. And EA Capability is how we make it real.

Daily Tech Digest - October 08, 2025

Quote for the day:

"Life is what happens to you while you’re busy making other plans." -- John Lennon

Network digital twin technology faces headwinds

Just like is able to overlay information, such as driving directions, traffic alerts or locations of gas stations or restaurants, digital twin technology enables network teams to overlay information, such as a software upgrade, a change to firewalls rules, new versions of network operating systems, vendor or tool consolidation, or network changes triggered by mergers and acquisitions. Network teams can then run the model, evaluate different approaches, make adjustments, and conduct validation and assurance to make sure any rollout accomplishes its goals and doesn’t cause any problems, explains Maccioni ... “Configuration errors are a major cause of network incidents resulting in downtime,” says Zimmerman. “Enterprise networks, as part of a modern change management process, should use digital twin tools to model and test network functionality business rules and policies. This approach will ensure that network capabilities won’t fall short in the age of vendor-driven agile development and updates to operating systems, firmware or functionality.” ... Another valuable use case is testing failover scenarios, says Wheeler. Network engineers can design a topology that has alternative traffic paths in case a network component fails, but there’s really no way to stress test the architecture under real world conditions. He says that in one digital twin customer engagement “they found failure scenarios that they never knew existed.”

Autonomous AI hacking and the future of cybersecurity

The balance has long skewed towards the attackers; these developments threaten to tip the scales completely. We’re potentially looking at a for cyber attackers. Key parts of the attack chain are becoming automated and integrated: persistence, obfuscation, command-and-control, and endpoint evasion. Vulnerability research could potentially be carried out during operations instead of months in advance. The most skilled will likely retain an edge for now. But don’t have to be better at a human task in order to be useful. They just have to excel in one of four dimensions: speed, scale, scope, or sophistication. But there is every indication that they will eventually excel at all four. By reducing the skill, cost, and time required to find and exploit flaws, AI can turn rare expertise into commodity capabilities and gives average criminals an outsized advantage. ... If enterprises adopt the way they adopted continuous integration/continuous delivery (), several paths open up. AI vulnerability discovery could become a built-in stage in delivery pipelines. We can envision a world where AI vulnerability discovery becomes an integral part of the software development process, where vulnerabilities are automatically patched even before reaching production — a shift we might call continuous discovery/continuous repair (CD/CR).

AI inference: reshaping the enterprise IT landscape across industries

AI inference is a complex operation that transforms intricate models into actionable agents. This process is essential for making real-time decisions, which can significantly improve user experiences. ... As AI systems handle more sensitive information, data security and become a key part of effective inference processes. In cloud and environments, where data often moves between multiple networks and devices, ensuring the confidentiality of user information is paramount. Private AI limits queries and requests to a company's internal database, SharePoint, API, or other private sources. It prevents unauthorized access and ensures that sensitive information remains confidential even when processed in the cloud or at the Edge. ... For AI to be truly transformative, low latency is a necessity, ensuring that real-time responses are both swift and seamless. In the realm of , for instance, the difference between a seamless conversation and a frustrating user experience often comes down to the speed of the AI’s response. Users expect immediate and accurate replies, and any delay can lead to a loss of engagement and trust. By minimising latency, AI chatbots can provide a more natural and fluid interaction, enhancing user satisfaction, and driving better outcomes. ... By reducing the distance data must travel, Edge computing significantly reduces latency, enabling faster and more reliable AI inference.

Smarter Systems, Safer Data: How to Outsmart Threat Actors

One of the clearest signs that a cybersecurity strategy is outdated is a lack of control and visibility over who can access what data, and on which systems. Many organizations still rely on fragmented identity management systems or grant broad access to database administrators. Others have yet to implement basic protections such as . ... Security concerns are commonly quoted as a top barrier to innovation. This is why many organizations struggle to adopt artificial intelligence, migrate to the cloud, share data externally or even internally. The only way to unblock this impasse is to start treating security as an enabler. Think about it this way: when done right, security is that key element that allows data to be moved, analyzed and shared. To exemplify this approach, if data is de-identified to maintain data privacy through the means of , in a situation of a breach, it will remain useless to attackers. ... What’s been key for the organizations that succeed in managing data risk while simultaneously unlocking value is a mindset shift. They stop seeing security as a roadblock and start seeing it as a foundation for growth. As an example, a large financial institution client has built an AI-powered solution for anti-money laundering. By protecting incoming data before it enters their system, they ensure that no sensitive data is fed to their algorithms, and thus the risk of a privacy breach, even incidental, is essentially null.

AI could prove CIOs’ worst tech debt yet

AI tools can be used to clean up old code and trim down bloated software, thus reducing one major form of tech debt. In September, for example, Microsoft announced a new suite of autonomous AI agents designed to automatically modernize and .NET applications. At the same time, IT leaders see the potential for AI to add to their tech debt, with too many AI projects relying on models or agents that can be expensive to deploy and maintain and AI coding assistants generating more lines of software than may be necessary. ... Endless AI pilot projects create their own form of tech debt as well, says Ryan Achterberg, CTO at IT consulting firm Resultant. This “pilot paralysis,” in which organizations launch dozens of proofs of concepts that never scale, can drain IT resources, he says. “Every experiment carries an ongoing cost,” Achterberg says. “Even if a model is never scaled, it leaves behind artifacts that require upkeep and security oversight.” Part of the problem is that AI data foundations are still shaky, even as AI ambition remains high, he adds. ... In addition to tech debt from too many AI pilot projects, coding assistants can create their own problems without proper oversight, adds Jaideep Vijay Dhok, COO for technology at digital engineering provider Persistent Systems. In some cases, AI coding assistants will generate more lines of software than a developer asked for, he says.

Hackers Exploit RMM Tools to Deploy Malware

typically operate with elevated permissions across endpoints. Once compromised, they offer adversaries a ready-made channel for , lateral movement and payload delivery, including ... Threat actors frequently repurpose legitimate RMM tools or hijack valid credentials, allowing malicious activity to blend seamlessly with routine administrative tasks. This tactic complicates detection and response, especially in environments lacking . ... "This is a typical living-off-the-land attack used by many adversaries considering the success and ease of execution. Typically, such software are whitelisted in most of the controls to avoid blocking and noise, due to which its activities are not monitored much," Varkey said. "Like in most adversarial acts, getting access to the software is their initial step, so if access is limited to specific people with multifactor authorization and audited periodically, unauthorized access can be limited. .." ... "Treat RMM seriously. Assume compromise is possible and build defenses around prevention, detection and rapid response. Start with a full audit of your RMM deployment - map every agent, session and integration to identify shadow access points: asset management is key and a good RMM solution should be able to assist here. Layered controls are key - think defense-in-depth tailored to RMM's remote nature," Beuchelt said.

From Data to Doing: Agentic AI Will Revolutionize the Enterprise

Where do organizations see the greatest opportunities for ? The answer is: everywhere. Survey results show that business leaders view agentic AI as equally relevant to productivity gains, better decision-making, and enhanced customer experiences. When asked to rank potential benefits, improving customer experience and personalization emerge as the top priority, followed closely by sharper decision-making and increased efficiency. What's telling is what landed at the bottom of the list. Few organizations currently view market and business expansion as critical. This suggests that, at least in the near term, agentic AI will be applied less as a driver of bold new growth and more as a catalyst for improving and extending existing operations. ... Agentic AI is not simply the next technology wave -- it is the next great inflection point for enterprise software. Just as client–server, the Internet, and the cloud radically redefined industry leaders, agentic AI will determine which vendors and enterprises can adapt quickly enough to thrive. The lesson is clear: organizations that treat data as a strategic asset, modernize their platforms, and embed intelligence into their workflows will not only move faster but also serve customers better. The rest risk being left behind -- just as the mainframe giants once were.

Is That Your Boss or a Deepfake on the Other Side of That Video Call?

Sophisticated had perfectly replicated not just the appearance but the mannerisms and decision-making patterns of the company’s executives. The real managers were elsewhere, unaware their digital twins were orchestrating one of the largest deepfake heists in corporate history. This reflects a terrifying trend of AI fraud that is shaking the financial services industry. Deepfake-enabled attacks have grown by an alarming 1,740% in just one year, representing one of the fastest-growing AI-powered threats. More than half of businesses in the U.S. and U.K. have been targeted by deepfake-powered financial scams, with 43% falling victim. ... The deepfake threat extends far beyond immediate financial losses. Each successful attack erodes the foundation of digital communication itself. When employees can no longer trust that their CEO is real during a video call, the entire remote work infrastructure becomes suspect in particular for financial institutions, which deal in the currency of trust. ... Financial services companies must implement comprehensive AI governance frameworks, continuous monitoring systems, and robust incident response plans to address these evolving threats while maintaining operational efficiency and customer trust. These systems and protocols must extend not only within their front office but to their back office, including vendor management and third-party suppliers who manage their data.

Rethinking AI security architectures beyond Earth

The researchers outline three architectures: centralized, distributed, and federated. In a centralized model, the heavy lifting happens on Earth. Satellites send telemetry data to a large AI system, which analyzes it and sends back security updates. Training is fast because powerful ground-based resources are available, but the response to threats is slower due to long transmission times. In a distributed model, satellites still rely on the ground for training but perform inference locally. This setup reduces delay when responding to a threat, though smaller onboard systems can limit model accuracy. Federated learning goes a step further. Satellites train and infer on their own data without sending it to Earth. They share only model updates with other satellites and ground stations. This keeps latency low and improves privacy, but synchronizing models across a large constellation can be difficult. ... Byrne pointed out that while space-based architectures vary in resilience, recovery often depends on shared fundamentals. “Most systems across all segments will need to be restored from secure backups,” he said. “One architectural enhancement to help reduce recovery time is the implementation of distributed . These links enable faster propagation of recovery updates between satellites, minimizing latency and accelerating system-wide restoration.”

Who Governs Your NHIs? The Challenge of Defining Ownership in Modern Enterprise IT

What we should actually mean by ownership is the person who can answer the basic questions about why this NHI exists, what access it has, how often credentials should be rotated, whether it's being used in a way that could introduce new risks, and whether the credentials have been properly stored or have been leaked. ... Instead of focusing solely on assigning human ownership, we should be working to ensure that the questions we would ask the owner are easily answerable by our tools. This approach makes answers persistent and usable by multiple teams over time and provides consistency across the organization. It does not rely on specific individuals being eternally available or up to speed on how the NHI they created is being used. Ultimately, it scales better than human-dependent processes. Just as governing an application and all of the NHIs involved is almost never going to be the responsibility of one person, the ideal scenario where a single person can outright own an NHI and be responsible for every aspect is going to be a rare situation. ... The conversation about ownership often gets stuck on blame. Let's reframe it around assurance. Let's ensure that if a secret exists, no matter where or how it is stored, governance questions can be answered quickly and consistently.