Daily Tech Digest by Kannan Subbiah: Daily Tech Digest

Quote for the day:

"What you leave out is just as important as what you leave in." -- Jason Fried

Post-quantum cryptography adoption and the national security implications

As quantum computers rapidly advance, they are turning theoretical vulnerabilities in modern encryption into immediate real-world threats. Experts warn that the transition to post-quantum cryptography must begin today, even if fully capable systems remain several years away. Because building these massive machines requires immense capital and infrastructure, their use will largely be restricted to nation-states and powerful corporations rather than everyday cybercriminals. This dynamic creates a severe national security risk. Hostile governments can routinely harvest encrypted data right now with the clear intention of decrypting it later when the technology fully matures. While large banks and federal agencies will likely prioritize upgrading their defenses, smaller targets like local utilities, regional hospitals, and critical manufacturing facilities often lack the resources or perceived risk to invest in new security standards. This leaves a dangerous gap in collective defense that state-sponsored actors can exploit for economic espionage or infrastructure disruption. To combat this uneven landscape, experts suggest enforcing strict government mandates, integrating updated algorithms by default into cloud services, increasing executive awareness, and expanding academic training. Addressing these vulnerabilities early ensures that critical networks remain secure, proving that immediate preparation is absolutely essential for long-term national security.

The need to fortify cloud integrity as cracks increase

As organizations rapidly integrate artificial intelligence and complex networking models, managing cloud security is becoming increasingly difficult. Jim Reavis, chief executive of the Cloud Security Alliance, notes that while modern cloud technology is highly capable, the operating structures surrounding it remain fragmented and messy. A major recurring issue is the shared responsibility model. Many companies mistakenly assume their cloud providers handle all security, yet customers often carry the bulk of the burden for protecting their data, applications, and user identities. The rapid rise of artificial intelligence complicates this further. Because these predictive tools are prone to errors and unintended actions, companies must establish clear boundaries, defined goals, and strict oversight rather than expecting the technology to police itself. Reavis highlights the concept of limiting automated systems by introducing strict autonomy rules, ensuring they only perform specific, approved tasks to prevent accidental damage or data loss caused by simple misconfigurations. Furthermore, outdated operational technology and disconnected internal teams create dangerous blind spots. When security, risk, and development departments operate in isolation, they leave cracks that intruders easily exploit. To safely adopt new capabilities, businesses must modernize their structural operations, unify their risk management strategies, and consistently maintain human control across their digital systems.

What AI Is Revealing About Your Bank’s Transformation

Financial institutions are moving artificial intelligence from testing phases into daily operations, but this shift is exposing hidden flaws in how these organizations function. The technology itself is not creating new problems; rather, it is shining a light on old, unresolved issues from past attempts to modernize. Many banks upgraded their digital tools over the years while leaving their internal departments disconnected. Because these separate systems do not share information smoothly, the resulting environment is too fragmented for advanced tools to work properly. As a result, companies discover that while their new technology is ready to go, their internal foundations are not. Banks that previously took the time to truly connect their systems are now seeing clear, measurable benefits. Meanwhile, those that simply pasted new tools over old habits are struggling to see real value. The focus is now moving away from programs that simply offer advice toward systems that actively manage routine tasks. To succeed today, these banks must stop viewing this as just a technology issue and recognize it as a fundamental operational challenge. Strengthening their internal foundations will allow them to actually improve customer experiences and stay ahead in the market.

Backlogs? Where We’re Going We Don’t Need Backlogs

This episode of the CISO Series Podcast features producer David Spark and co-host Steve Zalewski alongside Varsha Agrawal, head of information security at Prosper Marketplace. They explore the challenging reality of artificial intelligence vendors and the growing issue of lock-in. While businesses hope AI will seamlessly clear backlogs and save time, attendees at AI summits often leave with more questions than answers, realizing no magical solution currently exists. The hosts discuss the risk of handing over critical workflows, customer experiences, and data models to external vendors whose incentives might suddenly shift. Agrawal argues that vendor lock-in with AI is uniquely unpredictable because pricing models and the very existence of the tools frequently change, making it impossible to evaluate long-term costs upfront. She highlights that lock-in extends beyond data and contracts—it deeply affects employees who become accustomed to specific tools and workflows. Instead of blindly trusting AI solutions, the panel stresses the importance of having confidence in a system's constraints and building organizational readiness to switch tools when necessary. Furthermore, the episode briefly touches on boardroom communication, noting that true security governance requires boards to ask critical questions about detection and recovery rather than relying on oversimplified dashboards.

Leap second proposal will keep software stacks in sync

Global timekeeping experts are preparing to vote on a crucial proposal to end the practice of adding or subtracting leap seconds to Coordinated Universal Time. For decades, scientists added leap seconds to keep atomic clocks synchronized with the Earth's gradually slowing rotation. However, because the planet's rotation has recently accelerated, timekeepers now face the unprecedented prospect of applying a negative leap second. This poses a significant threat to global digital infrastructure. Computer systems, databases, and interconnected software applications were never designed to subtract time, and doing so could trigger widespread system failures, database corruption, and major outages across financial networks and cloud platforms. To prevent these risks, the General Conference on Weights and Measures will vote to make coordinated time continuous starting in May 2027. This change would allow atomic time to drift slightly from the Earth's physical rotation over centuries, up to a maximum of one hour. Technology analysts strongly support this transition, arguing that preserving exact astronomical time synchronization is no longer worth the severe operational risks to modern enterprise technology. Passing the proposal ensures long term stability and predictability for the countless computer systems that run our highly connected modern world.

Beyond shared responsibility: When AI acts, who owns the blast radius?

As artificial intelligence evolves from answering questions to actively executing tasks, the traditional shared-responsibility models used for cloud computing are no longer sufficient. Cloud security models historically divided duties by infrastructure layers, with vendors securing the environment and customers securing their data. However, agentic AI operates differently, distributing authority across complex chains of models, platforms, and partners at machine speeds. Today, an AI agent might possess legitimate access and permissions but still produce unintended or harmful business outcomes, separating authorization from the actual intent and final result. Because these systems now hold agency within business processes—capable of accessing data, calling tools, and executing thousands of steps autonomously—the industry desperately needs a new shared-accountability framework. This emerging model must clearly define who authorizes actions, who can intervene, and who ultimately owns the consequences when something goes wrong. Security platforms are racing to become the control layer, aiming to validate identity and contain runtime behaviors. Yet, organizations remain accountable for defining acceptable outcomes and managing recovery when AI systems trigger unforeseen events. Ultimately, establishing clear ownership across every automated handoff is critical before deploying these powerful, independent agents into production environments.

Retail colo in the age of AI: One size does not fit all

The rapid expansion of artificial intelligence is fundamentally changing how retail colocation data centers operate around the world, proving that standardized infrastructure is no longer sufficient. Historically, colocation providers offered uniform spaces with predictable power and cooling limits, which worked perfectly for traditional enterprise applications. However, artificial intelligence introduces workloads that demand significantly higher power density and advanced cooling methods, such as liquid cooling systems. Providers are realizing that a single operational model cannot accommodate these extreme variations. While some customers require massive clusters for training complex models, others need smaller setups closer to end users for swift inference tasks. Consequently, retail colocation facilities must become much more flexible. They need to redesign their environments to support diverse requirements within the same building, balancing specialized zones with traditional racks. This essential shift requires strategic investments in upgraded power distribution and innovative thermal management systems. By moving away from rigid approaches, data center operators can successfully cater to the unique demands of artificial intelligence without alienating their conventional enterprise clients. Ultimately, embracing true adaptability allows colocation providers to remain competitive, ensuring they can support the next generation of computing while maintaining sustainable and highly efficient operations across their diverse customer base.

80% of AI projects fail, and Gallagher’s India CIO says he knows why

Many enterprise artificial intelligence initiatives fall short of expectations because companies focus on the technology rather than the core business problem. According to Julen Mohanty, a technology leader at the insurance firm Gallagher, roughly 80% of AI projects fail for this exact reason. Instead of finding a practical use case that increases revenue, reduces costs, or manages risk, organizations often adopt the latest tools and then search for places to apply them. Similarly, starting a project simply to reduce headcount is a misguided approach. The real goal should be to improve the underlying process. While automation can drastically speed up tasks like proposal generation and claims processing, human oversight remains vital. Machines can perform repetitive work efficiently, but accountability must always rest with people. A successful strategy requires measuring a process before automating it to ensure real efficiency gains are possible. Furthermore, robust data governance must come first, as data is only valuable when a company knows how to connect it to a specific outcome. Ultimately, a collaborative company culture and strong security controls are just as important as the chosen platform. By keeping humans in the loop and solving real problems, businesses can implement these advanced systems successfully.

AI notetakers at work could leave companies at risk for lawsuits

AI note-taking applications have become popular workplace tools for recording meetings and generating helpful summaries, but their rapid rise has sparked significant privacy concerns and complex legal challenges. According to attorney Brian McGinnis, multiple lawsuits against vendors like Otter, Fireflies, and Granola focus on whether these tools unlawfully capture communications without adequate notice or proper consent. A major issue is how conversation data is subsequently processed, particularly if it is used to train AI models or create highly regulated biometric voiceprints. These specific practices potentially violate federal wiretapping statutes and strict state laws, such as the Illinois Biometric Information Privacy Act and California's two-party consent rules, which require every single participant to agree to being recorded. While an outright ban on AI notetakers is highly unlikely, companies face substantial risks if they allow employees to freely deploy these applications without clear operational guidelines. To mitigate legal exposure, McGinnis advises organizations to establish comprehensive internal policies governing AI usage. Businesses should ensure employees only use approved tools, enable all built-in notice features, and strictly obtain explicit consent from all meeting participants before recording begins. As the technology expands into wearable devices, navigating the complex rules around privacy and recording consent will remain a critical, ongoing challenge for employers.

The five important tools for controlling AI costs

As generative artificial intelligence becomes a standard feature in modern software applications, managing the associated computing costs has become a critical challenge for engineering teams. Fortunately, there are five practical methods to keep these expenses under control without sacrificing overall performance. First, teams should use model routing, which directs simpler tasks to smaller, cheaper models rather than relying on the most powerful, expensive option for everything. Second, semantic caching helps by identifying identical user intents, even when phrased differently, and serving previously stored answers to bypass the AI entirely. Third, prompt caching allows developers to keep essential background data stored directly in the AI engine's memory, eliminating the need to repeatedly send and pay for the same context. Fourth, practicing prompt discipline through data filtering ensures that only the most relevant information reaches the AI, which cuts down on wasteful input charges. Finally, setting strict response constraints forces the AI to output exactly what is needed, like pure data, instead of generating polite but expensive conversational filler. By implementing these five core strategies, developers can build smart, reliable tools while maintaining a firm grip on their budgets, ensuring that technological progress does not lead to unexpected financial strain over time.

Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - September 10, 2026