Daily Tech Digest by Kannan Subbiah: Daily Tech Digest

Quote for the day:

"The only person you are destined to become is the person you decide to be." -- Ralph Waldo Emerson

When is an AI agent not really an agent?

If you believe today’s marketing, everything is an “AI agent.” A basic workflow worker? An agent. A single large language model (LLM) behind a thin UI wrapper? An agent. A smarter chatbot with a few tools integrated? Definitely an agent. The issue isn’t that these systems are useless. Many are valuable. The problem is that calling almost anything an agent blurs an important architectural and risk distinction. ... If a vendor knows its system is mainly a deterministic workflow plus LLM calls but markets it as an , buyers are misled not just about branding but also about the system’s actual behavior and risk. That type of misrepresentation creates very real consequences. Executives may assume they are buying capabilities that can operate with minimal human oversight when, in reality, they are procuring brittle systems that will require substantial supervision and rework. Boards may approve investments on the belief that they are leaping ahead in AI maturity, when they are really just building another layer of . Risk, compliance, and security teams may under-specify controls because they misunderstand what the system can and cannot do. ... demand evidence instead of demos. Polished demos are easy to fake, but architecture diagrams, evaluation methods, , and documented limitations are harder to counterfeit. If a vendor can’t clearly explain how their agents reason, plan, act, and recover, that should raise suspicion.

Five identity-driven shifts reshaping enterprise security in 2026

Organizations that continue to treat identity as a static access problem will fall behind attackers who exploit AI-powered automation, credential abuse, and . The enterprises that succeed will be those that re-architect identity security as a continuous, data-aware control plane, one built to govern humans, machines, and AI with the same rigor, visibility, and accountability. ... Unlike traditional shadow IT, is both more powerful and more dangerous. Employees can deploy advanced models trained on sensitive company data, and these tools often store or transmit privileged credentials, API keys, and service tokens without oversight. Even sanctioned AI tools become risky when improperly configured or connected to internal workflows. ... With AI-driven automation, sophisticated playbooks previously reserved for top-tier nation-states become accessible to countries, and non-state actors, with far fewer resources. This levels the playing field and expands the number of threat actors capable of meaningful, identity-focused cyber aggression. In 2026, expect more geopolitical disruptions driven by identity warfare, synthetic information, and AI-enabled critical infrastructure targeting. ... Machine identities have become the primary source of privilege misuse, and their growth shows no sign of slowing. As AI-driven automation accelerates and IoT ecosystems proliferate, organizations will hit a governance tipping point.2026 will force security teams to confront a tough reality. Identity-first security can’t stop with humans.

Implementing NIS2 — without getting bogged down in red tape

NIS2 essentially requires three things: concrete security measures; processes and guidelines for managing these measures; and robust evidence that they work in practice. ... Therefore, two levels are crucial for NIS2: the technical measures and the evidence that they are effective. This is precisely where the transformation of recent years becomes apparent. Previously, concepts, measures, and specifications for software and IT infrastructures were predominantly documented in text form. ... The second area that NIS2 and the new for digital services are enshrining in law is in the company’s own code and supply chain. This requires regular vulnerability scans, procedures for assessment and prioritization, timely remediation of critical vulnerabilities, and regulated vulnerability handling and — where necessary — coordinated vulnerability disclosure. Cloud and SaaS providers also face additional supply chain obligations ... The third area where NIS2 quickly becomes a paper tiger is the combination of monitoring, incident response, and the new reporting requirements. The directive sets clear deadlines: early warning within 24 hours, a structured report after 72 hours, and a final report no later than one month. ... NIS2 forces companies to explicitly define their security measures, processes, and documentation. This is inconvenient — especially for organizations that have previously operated largely on an ad-hoc basis.

Rethinking Anomaly Detection for Resilient Enterprise IT

Being armed with this knowledge is only the first step, though. The next challenge is detecting anomalies consistently and accurately in complex environments. This task is becoming increasingly difficult as IT environments undergo continuous digital transformation, shift towards hybrid-cloud setups, and rely on legacy systems that are well past their prime. These challenges introduce dynamic data, pushing IT leaders to rethink their processes. ... By incorporating seasonal patterns, user behavior, and workload types, filter out the noise and highlight genuine deviations. Another factor to integrate is the overall context of a situation. Metrics rarely operate in isolation. During planned deployment, it would be anticipated for a spike in network latency. This same spike would be seen completely differently if it were to occur during steady operations. By combining telemetry with contextual signals, anomaly detection systems can separate the expected from the unexpected. ... Anomaly detection is meant to strengthen operations and improve overall resilience. However, it is not capable of delivering on this promise when teams are constantly swimming through the seas of generated alerts. By contextually and comprehensively adopting new approaches to the variety of anomalies, systems can identify root causes, uniformly correct systemic failures created from multiple metrics points, and mitigate the risk of outages.

Bridging the Gap: Engineering Resilience in Hybrid Environments (DR, Failover, and Chaos)

Resilience in a hybrid environment isn't just about preventing failure; it’s about enduring it. It requires moving beyond hope as a strategy and embracing a tripartite approach: Robust Disaster Recovery (DR), automated Failover, and proactive . ... Disaster Recovery is your insurance policy for catastrophic events. It is the process of regaining access to data and infrastructure after a significant outage—a hurricane hitting your primary data center, a massive ransomware attack, or a prolonged regional cloud failure. ... While DR handles catastrophes, Failover handles the everyday hiccups. Failover is the (ideally automatic) process of switching to a redundant or standby system upon the failure of the primary system, mostly automatic. Failover mechanisms in a hybrid environment ensure immediate operational continuity by automatically switching workloads from a failed primary system (on-premises or cloud) to a redundant secondary system with minimal downtime. This requires coordinating recovery across cloud and on-premises platforms. ... Chaos engineering is a proactive discipline used to stress-test systems by intentionally introducing controlled failures to identify weaknesses and build resilience. In hybrid environments—which combine on-premises infrastructure with cloud resources—this practice is essential for navigating the added complexity and ensuring continuous reliability across diverse platforms.

Should CIOs rethink the IT roadmap?

As technology consultancy states: “You don’t need bigger plans — you need faster moves.” This is a fitting mantra for IT roadmap development today. CIOs should ask themselves where the most likely business and technology plan disrupters are going to come from. ... Understandably, CIOs can only develop future-facing technology roadmaps with what they see at a present point in time. However, they do have the ability to improve the quality of their roadmaps by reviewing and revising these plans more often. ... CIOs should revisit IT roadmaps quarterly at a minimum. If roadmaps must be altered, CIOs should communicate to their CEOs, boards, and C-level peers what’s happening and why. In this way, no one will be surprised when adjustments must be made. As CIOs get more engaged with lines of business, they can also show how technology changes are going to affect company operations and finances before these changes happen ... Equally important is emphasizing that a seismic change in technology roadmap direction could impact budgets. For instance, if AI-driven security threats begin to impact company AI and general systems, IT will need AI-ready tools and skills to defend and to mitigate these threats. ... Now is the time for CIOs to transform the IT roadmap into a more malleable and responsive document that can accommodate the disruptive changes in business and technology that companies are likely to experience.

Why shadow IT is a growing security concern for data centre teams

It is essential to recognise that employees use shadow IT to get their work done efficiently, not to deliberately create security risks. This should be front of mind for any IT teams and data centre consultants involved in infrastructure design and security provision. Finding blame or taking an approach that blocks everything does not work. A more effective way to address shadow IT use is to invest for the long term in a culture which promotes IT as a partner to workplace productivity, not something which is a hindrance. Ideally, this demands buy-in from senior management. Although it falls to IT teams to provide people with the tools for their jobs, providing choice, listening to employees’ requests and offering prompt solutions, will encourage the transparency so much needed for IT to analyse usage patterns, identify potential issues and address minor issues before they grow into costly problems. Importantly, this goes a long way towards embracing new technologies and avoiding employees turning to shadow IT that they find and use without approval. ... While IT teams are focused on gaining visibility and control over the software, hardware and services gainfully used by their organisations, they also need to be careful not to stifle innovation. It is here that data centre operators can share ideas on ways to best achieve this balance, as there is never going to be one model that suits every business.

From Digitalization to Intelligence: How AI Is Redefining Enterprise Workflows

In the AI economy, digitalization plays another important role—turning paper documents into data suitable for LLM engines. This will become increasingly important as more sites restrict crawlers or require licensing, which reduces the usable pool of data. A 2024 report from the nonprofit watchdog Epoch AI projected that large language models (LLMs) could run out of fresh, human-generated training data as soon as 2026. Companies that rely purely on publicly available crawl data for continuous scaling likely will encounter diminishing returns. To avoid the looming publicly accessed data shortage, enterprises will need to use their digitized documents and corporate data to fine‐tune models for domain specific tasks rather than rely only on generic web data. Intelligent capture technologies can now recognize document types, extract key entities, and validate information automatically. Once digitized, this data flows directly into enterprise systems where AI models can uncover insights or predict outcomes. ... Automation isn’t just about doing more with less; it’s about learning from every action. Each scan, transaction, or decision strengthens the feedback loop that powers enterprise AI systems. The organizations recognizing this shift early will outpace competitors that still treat data capture as a back-office function. The winners will be those that turn the last mile of digitalization into the first mile of intelligence.

Boardrooms demand tougher AI returns & stronger data

Budget scrutiny is increasing as wider economic conditions remain uncertain and as organisations review early generative AI experiments. "AI investment is no longer about FOMO. Boards and CFOs want answers about what's working, where it's paying off, and why it matters now. 2026 will be a year of focus. Flashy experiments and perpetual pilots will lose funding. Projects that deliver measurable outcomes will move to the center of the roadmap," said McKee, CEO, . ... "For years people have predicted that AI will hollow out data teams, yet the closer you get to real deployments, the harder that story is to believe. Once agents take over the repetitive work of querying, cleaning, documenting, and validating data, the cost of generating an insight will begin falling toward zero. And when the cost of something useful drops, demand rises. We've seen this pattern with steam engines, banking, spreadsheets, and cloud compute, and data will follow the same curve," said Keyser. Keyser said easier access to data and analysis is likely to change behaviours in business units that have not traditionally engaged with central data groups. He expects a rise in AI-literate staff across operational functions and a larger need for oversight. ... The organizations that adopt agents will discover something counterintuitive. They won't end up with fewer data workers, but more. This is applied to analytics. When insight becomes easier, curiosity will expand and decision-making will accelerate.

The Blind Spots Created by Shadow AI Are Bigger Than You Think

If you think it’s the same as the old “shadow IT” problem with different branding, you’re wrong. Shadow AI is faster, harder to detect, and far more entangled with your intellectual property and data flows than any consumer SaaS tool ever was. ... Shadow AI is not malicious in nature; in fact, the intent is almost always to improve productivity or convenience. Unfortunately, the impact is a major increase in unplanned data exposure, untracked model interactions, and blind spots across your attack surface. ... Most AI tools don’t clearly explain how long they keep your data. Some retrain on what you enter, others store prompts forever for debugging, and a few had almost no limits at all. That means your sensitive info could be copied, stored, reused for training, or even show up later to people it shouldn’t. Ask , whose internal code found its way into a public model’s responses after an engineer uploaded it. They banned AI instantly. Hardly the most strategic solution, and definitely not the last time you’ll see this happen. ... Shadow AI bypasses Identity controls, DLP controls, SASE boundaries, Cloud logging, and Sanctioned inference gateways. All that “AI data exhaust” ends up scattered across a slew of unsanctioned tools and locations. Your exposure assessments are, by default, incomplete because you can’t protect what you can’t see. ... Shadow AI has changed from an occasional or unusual instance case to everyday behavior happening across all departments.

Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - December 24, 2025