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"No man is good enough to govern another man without that other's consent." -- Abraham Lincoln

The Quantum Wake-Up Call: Preparing Your Organization for PQC

promises transformative breakthroughs across industries—but it also threatens the cryptographic foundations that secure our digital world. As quantum capabilities evolve, organizations must proactively prepare for the shift to post-quantum cryptography (PQC) to safeguard sensitive data and maintain trust. ... The very mathematical "hardness" that makes RSA and ECC secure against classical computers is precisely what makes them fatally vulnerable to quantum computing. : This quantum algorithm, developed by in 1994, is capable of solving the integer factorization and problems exponentially faster than any classical machine. Once a sufficiently stable and large-scale quantum computer is built, an encryption that might take a supercomputer millions of years to break could be broken in hours or even minutes. The Decryption Time Bomb: Because current PKC is used to establish long-term trust and to encrypt keys, the entire cryptographic ecosystem is a single point of failure. The threat is compounded by the "Harvest Now, Decrypt Later" strategy, meaning sensitive data is already being harvested and stored by adversaries, awaiting the quantum moment to be unlocked. Quantum computing is no longer theoretical—it’s a looming reality. Algorithms like , which underpin most public-key cryptography, are vulnerable to quantum attacks via Shor’s algorithm.

Producing a Better Software Architecture with Residuality Theory

is a very simple process. Sometimes, people are put off because the theoretical work necessary to prove that residuality works is very heavy, but applying it is easy, explained: We start out with a suggestion, a naive architecture that solves the functional problem. From there we stress the architecture with potential changes in the environment. These stressors allow us to uncover the attractors, often through conversations with domain experts. For each attractor, we identify the residue, what’s left of our architecture in this attractor, and then we change the naive architecture to make it survive better. We do this many times and, at the end, integrate all of these augmented residues into a coherent architecture. We can then test this to show that it survives unknown forms of stress better than our naive architecture. In complex business environments with uncertainty, residuality makes it possible to create architectures quickly instead of chasing down stakeholders demanding specific requirements or answers to questions that are unknown by the business itself, O’Reilly said. It pulls technical architects out of details and teaches them to productively engage with a business environment without the lines and boxes of traditional enterprise architecture, he concluded. ... Senior architects report that it gives a theoretical justification for practices that many had already figured out and a shared vocabulary for teams to talk about architecture.

Will AI-SPM Become the Standard Security Layer for Safe AI Adoption?

By continuously monitoring AI assets, AI-SPM helps ensure that only trusted data sources are used during model development. and red-team exercises detect vulnerabilities caused by malicious data. The system actively identifies abnormal model behavior, such as biased, toxic, or manipulated output, and brings them up for remediation prior to production release. ... AI-SPM continuously checks system requests and user inputs to find dangerous patterns before they lead to security problems, like attempts to remove or change built-in directives. It also uses protection against and , which are common ways to access or alter system-level commands. By finding unapproved AI tools and services, it stops the use of insecure or poorly set up LLMs that could reveal system prompts. ... is starting to get more attention, and for good reason. Like shadow IT, employees are using public AI tools without authorization. That might mean uploading sensitive data or sidestepping governance rules, often without realizing the risks. The problem isn’t just the tools themselves, but the lack of visibility around how and where they’re being used. AI-SPM should work to identify all AI tools in play across networks, endpoints, cloud platforms, and dev environments, mapping how data moves between them, which is often the missing piece when trying to understand exposure risks.

How to write nonfunctional requirements for AI agents

can be like those for applications, where user stories are granular and target delivering small, atomic functions. These NFRs can guide developers in answering how to develop the functionality described in user stories and to help quantify what should pass a code review. However, you may need another set of NFRs expressed at a feature or release level. ... “Agile teams often struggle with how to evaluate NFRs like , , or , which may seem nonfunctional, but with a little specification work, they can often be made concrete and part of a user story with clear pass/fail tests,” says , co-founder of Imandra. “We use to turn NFRs into mathematical functional requirements we can prove or disprove.” ... AI agent NFRs that connect dev with ops have all the complexities of applications, infrastructure, automations, and AI models bundled together. Deploying the AI agent is just the beginning of its lifecycle, and NFRs for maintainability and observability help create the feedback loops required to diagnose issues and make operational improvements. As many organizations aim toward autonomous agentic AI and agent-to-agent workflows, standardizing a list of NFRs that are applied across all AI agents becomes important.

Unplug Gemini from email and calendars, says cybersecurity firm

CSOs should consider turning off access to employees’ Gmail and Google Calendars, because the chatbot is vulnerable to a form of prompt injection, says the head of a cybersecurity firm that discovered the vulnerability. ”If you’re worried about the risk, you might want to turn off automatic email and calendar processing by Gemini until this, and potentially other things like it, are addressed,” , CEO of US-based , said in an interview. ... This flaw is “particularly dangerous when LLMs, like Gemini, are deeply integrated into enterprise platforms like Google Workspace,” the report adds. FireTail tested six AI agents. , , and caught the attack. Gemini, , and failed. In a test, FireTail researchers were able to change the word “Meeting” in an appointment in Google Calendar to “Meeting. It is optional.” ... “ against AIs aren’t new,” commented , a US-based cybersecurity and AI expert. “I saw one demonstrated over a year ago.” He didn’t specify where, but in August 2024, a security researcher blogged about an ASCII smuggling vulnerability in Copilot. The finding was reported to Microsoft. Many ways of disguising malicious prompts will be discovered over time, he added, so it’s important that IT and security leaders ensure that AIs don’t have the power to act without human approval on prompts that could be damaging.

Broken Opt-Outs, Big Fines: Tractor Supply Shows Privacy Enforcement Has Arrived for Retail

The reveal a clear enforcement pattern. Broken opt-out links that route to dead webforms. ignored entirely. Privacy notices that skip job applicant data disclosures. Vendor agreements without data restriction clauses. These aren’t random oversights. They’re the exact gaps that surfaced across recent enforcement by the Attorney General and CPPA orders. Regulators are building a playbook: test the opt-out mechanisms, check for GPC compliance, review all privacy notices including HR portals, and audit third-party contracts. If any piece fails, expect enforcement. Regulators no longer accept opt-outs in theory or privacy policies in fine print. ... The message is clear: prove you have control. Not just over the data you collect, but over the algorithms that process it. Retailers who can’t show governance across both will face scrutiny on multiple fronts. The same broken opt-out that triggers a privacy fine could signal to regulators that your AI systems lack oversight too. This isn’t about adding more compliance checkboxes. It’s about recognizing that data governance and AI governance are becoming inseparable. The retailers who understand this convergence will build unified systems that handle both. The ones who don’t will scramble to retrofit governance after the fact, just like they’re doing with privacy today.

Why Enterprises Continue to Stick With Traditional AI

AI success also depends on digital maturity. Many organizations are still laying data foundations. "Let's say you want to run analytics on how many tickets were raised, do a dashboard on how many tickets one can expect … all of that was over a call. Nothing was digitized. There is no trace of it. That is the reason why chatbots are getting created because they are now recording and getting traced," Iyer said. ... Strict compliance and privacy requirements push enterprises toward controlled AI development. … Even in such cases, we ensure the data in the model that we build, it stays exclusively. At any point of time, your data or your model is not going to be used for the betterment of someone else," Iyer said. This approach reflects broader enterprise concerns about AI governance. According to research, frameworks such as local interpretable model-agnostic explanations and help clarify AI decisions, support compliance and build stakeholder confidence. ... Iyer said enterprise needs are often highly contextual, making massive models unnecessary. "Do you need a 600-700 billion [parameter] model sitting in your enterprise running inferences when the questions are going to be very contextual?" she said. This practical wisdom is supported by recent industry analysis. Traditional ML models often produce classification accuracy at a fraction of the cost compared to deep learning alternatives.

Lead with a human edge: Why empathy is the new strategy

Traditional management was built on control: plans, processes, and hierarchies designed to tame complexity. But as noted, ‘organisations are living organisms. They evolve, sense, and respond. Trying to manage them like machines is an illusion. The leaders of tomorrow will not be engineers of systems — they will be gardeners of cultures.’ “Planting a tree is very easy,” Bidwai said. “The real game is how you nurture, how you create an environment, how you enable the culture.” Nurturing, not directing, is the leadership mindset for an era of interdependence. ... Perhaps the most striking moment of Pushkar’s talk was not analytical but symbolic. He invited participants to discard their corporate titles just for a moment and invent new ones that reflected their purpose, not their position. “Sometimes titles define how we operate. Can we look beyond titles?” His own? In People Matters, Pushkar stated that he visualises his creative title as Plumber. “Wherever anything needs fixing, I will go and fix things.” The metaphor landed. Leadership, stripped of status, is about service. To lead with a human edge is to roll up your sleeves, listen, and fix what’s broken, in systems, in relationships, in ourselves. ... What Pushkar calls ‘the human edge’ is not a nostalgic pushback against technology. It is a pragmatic blueprint for sustainable growth. The leaders who will define the next decade will be those who use AI to augment human potential, not replace it those who recognise that data drives decisions, but empathy drives destiny.

Building a modern fraud prevention stack: why centralised data, not point solutions, is the answer

The fraud prevention landscape is riddled with fragmented tools, reactive approaches and blind spots. Despite the best of intentions, many organisations rely on outdated, point-in-time methods that are ill-suited for today’s dynamic fraud landscape. And fraud no longer plays by the old rules. It unfolds across the entire customer journey, mutating with every new channel, payment method or customer behaviour pattern. A fraudster may test stolen credentials one day, then come back weeks later to exploit a weak link in the onboarding or refund process. These disjointed systems miss multi-step attacks and patterns that unfold over time. ... while many organisations have historically relied on a patchwork of tools to cover each threat vector, it’s becoming clear that more tools aren’t the answer. Better coordination is. A modern stack doesn’t need to come from a single vendor, but it does need to operate like a single, unified system. That means integrated data, shared intelligence and orchestration that supports real-time response, not after-the-fact analysis. While investment is rising, with 85% of organisations having increased their fraud prevention budgets, it’s crucial to highlight that spending must be strategic. So, what does a modern fraud prevention stack actually look like? And how can organisations build one that’s unified, flexible and future-proof?

CISOs, Start Securing Software's Agentic Future Now

Industry-wide challenges create obstacles to AI governance, leaving leaders uncertain about where to focus their strategic efforts most effectively. The non-deterministic nature of agents causes them to behave in unexpected ways, which has been proven to disrupt existing security boundaries. Adding to this security complexity, universal protocols, such as and , are emerging to streamline data access and improve agent interoperability, but their ecosystem-building capabilities introduce additional security considerations. But these challenges cannot stop security leaders from prioritizing AI governance. ... A culture of security now requires . 43% of survey respondents acknowledged a widening AI skills gap, which is likely to grow unless technical leaders prioritize upskilling teams to understand model behavior, , and how to evaluate model inputs and outputs critically. Understanding where models are performant versus where their use is suboptimal helps teams avoid unnecessary security risk and technical debt. ... Teams should also recognize that no model can replace human ingenuity. When models fail in domains where security engineers or developers lack expertise, they will not be able to identify the security gaps the model has left behind. CISOs should consider dedicating a portion of learning and development budgets to continuous technical education.

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Daily Tech Digest - October 09, 2025