Daily Tech Digest by Kannan Subbiah: Daily Tech Digest

Quote for the day:

“When opportunity comes, it’s too late to prepare.” -- John Wooden

All in the Data: The State of Data Governance in 2026

For years, was treated as the “nice” approach — the softer way to apply discipline without disruption. But 2026 has rewritten that narrative. Now, is increasingly seen as the only sustainable way to govern data in a world of continuous transformation. Traditional “assign people to be stewards” approaches simply cannot keep up with , edge analytics, , and the modern demand for organizational agility. ... Governance becomes the spark that ignites faster value, safer AI, more confident decision-making, and a culture that welcomes transformation instead of bracing for it. This catalytic effect is why organizations that embrace “The Data Catalyst³” in 2026 are not merely improving — they are accelerating, compounding their gains, and outpacing peers who still treat governance as a slow, procedural necessity rather than the engine of modern data excellence. ... This year, is no longer an afterthought. It is the bloodstream of governance. Organizations are finally acknowledging that without shared understanding, consistent definitions, and a reliable inventory of where data comes from and who touches it, AI will hallucinate confidently while leaders make decisions blindly. ... Perhaps the greatest evolution in 2026 is the rise of governance that keeps pace with AI. Organizations can no longer review policies once a year or update data inventories only during budget cycles. Decision cycles are compressing. Change windows are shrinking.

The Next Two Years of Software Engineering

AI unlocks massive demand for developers across every industry, not just tech. Healthcare, agriculture, manufacturing, and finance all start embedding software and automation. Rather than replacing developers, AI becomes a force multiplier that spreads development work into domains that never employed coders. We’d see more entry-level roles, just different ones: “” developers who quickly build automations and integrations for specific niches. ... Position yourself as the guardian of quality and complexity. Sharpen your core expertise: architecture, security, scaling, domain knowledge. Practice modeling systems with AI components and think through failure modes. Stay current on vulnerabilities in AI-generated code. Embrace your role as mentor and reviewer: define where AI use is acceptable and where manual review is mandatory. Lean into creative and strategic work; let the junior+AI combo handle routine API hookups while you decide which APIs to build. ... Lean into leadership and architectural responsibilities. Shape the standards and frameworks that AI and junior team members follow. Define code quality checklists and policies. Stay current on compliance and security topics for AI-produced software. Focus on system design and integration expertise; volunteer to map data flows across services and identify failure points. Get comfortable with orchestration platforms. Double down on your role as technical mentor: more code reviews, design discussions, technical guidelines.

What will IT transformation look like in 2026, and how do you know if you're on the right track?

The IT organization will become the keeper of the journal in terms of business value, and a lot of organizations haven't developed those muscles yet. ... Technical complexity remains a huge challenge. Back-end systems are becoming more complicated, requiring stronger architecture frameworks, faster design cycles and reliable data access to support emerging agentic AI frameworks. ... "Many IT organizations have taken the easy way," said de la Fe, referring to cloud and application service providers. As a result, their data is spread across different environments. Organizations may technically own their data, he said, but "it isn't with them -- or architected in a manner where they can access and use it as they may need to." ... "They believe it's a period of architectural redux because applications are becoming more heterogeneous," Vohra said. "Their architecture must be more modular and open, but they can't simply say no to core applications, because the business will demand them. They must be more responsive to the business than ever before." ... Without business-IT alignment, IT cannot deliver the business impact the organization now expects. CIOs are under increasing pressure from senior leadership and boards to improve efficiency and deliver business value, as measured in rather than traditional IT KPIs. On the technology side, CIOs also need to ensure they are architecting for the future.

Why CISOs Must Adopt the Chief Risk Officer Playbook

As the threat landscape becomes increasingly complex due to AI acceleration, shifting regulations, and geopolitical volatility, the role of the security leader is evolving. For CISOs and their teams, the McKinsey research provides a blueprint for transforming from technical gatekeepers into strategic risk leaders. ... A common question in the industry is whether a company needs both a Chief Risk Officer and a Chief Information Security Officer (CISO). ... Understanding the difference in what these two leaders look for is key to collaboration. Primary goal for CRO: Protect the organization's financial health and long-term viability. Primary goal for the CISO: Protect the confidentiality, integrity, and availability of digital assets. Key metric for CRO: and insurance premium outcomes. Key metric for CISO: Mean time to detect (MTTD), threat actor activity, and control effectiveness. Focus area for CRO: Market shifts, credit risk, geopolitical crises, and supply chain fragility. Focus area for CISO: Vulnerabilities, , , and . Outcome for CRO: Ensuring the business can survive any "bad day," financial or otherwise. Outcome for CISO: Ensuring the digital infrastructure is resilient against constant attack. ... The next generation of cybersecurity leaders will not just be the ones who can write the best code or configure the tightest firewall. They will be the ones who can walk into a boardroom, speak the language of the CRO, and explain how a specific technical risk impacts the organization's bottom line.

Passwords are where PCI DSS compliance often breaks down

CISOs often ask where fit within the PCI DSS language. The standard does not mandate specific technologies, but it defines outcomes that password managers help achieve. Requirement 8 focuses on identifying users and authenticating access. Unique credentials and protection of authentication factors are core expectations. Requirement 12.6 addresses security awareness. Training must reflect real risks and employee responsibilities. Demonstrating that employees are trained to use approved credential management tools strengthens assessment evidence. Self-assessment questionnaires reinforce this operational focus. They ask how credentials are handled, how access is reviewed, and how training is documented, pushing organizations to demonstrate process rather than policy. ... “Security leaders want to know who accessed what and when. That visibility turns password management from a convenience feature into a control.” ... Culture shows up in small choices. Whether employees ask before sharing access. Whether they trust approved tools. Whether security feels like support or friction. PCI DSS 4.x pushes organizations to take those signals seriously. Passwords sit at the center of that shift because they touch every system and every user. Training alone does not change behavior. Tools alone do not create understanding.

AI Demand and Policy Shifts Redraw Europe’s Data Center Map for 2026

Rising demand for AI, particularly large language models (LLMs) and generative AI, is driving the need for large-scale GPU clusters and advanced infrastructure. The EU's forthcoming Cloud and AI Development Act aims to triple the region's data center processing capacity within five to seven years, with streamlined approvals and public funding for energy-efficient facilities expected to stimulate growth. ... “We expect to see a strategic bifurcation,” Lamb said, with FLAP-D metros continuing to attract latency-sensitive enterprise and inference workloads that require proximity to end users, while large-scale AI training deployments gravitate toward regions with abundant, cost-effective renewable energy. ... Despite abundant renewables and favorable cool conditions, the Nordics have not scaled as quickly as anticipated. Thorpe reported steady but slower growth, citing municipal moratoriums – particularly in Sweden – and lower fiber density. Even so, AI training workloads are renewing interest in Norway and Finland. “The northern part of Norway is a good example,” Thorpe said, noting OpenAI’s planned Stargate facility powered entirely by hydroelectric energy. “They are able to achieve much lower PUE [power usage effectiveness] because of the cooler climate.” ... Meanwhile, stricter energy-efficiency requirements are complicating the planning process.

Top cyber threats to your AI systems and infrastructure

Multiple attack types against AI systems are arising. Some attacks, such as data poisoning, occur during training. Others, such as adversarial inputs, happen during inference. Still others, such as model theft, occur during deployment. ... Here, the attack goes after the model itself, seeking to produce inaccurate results by tampering with the model’s architecture or parameters. Some definitions of model poisoning models also include attacks where the model’s training data has been corrupted through data poisoning. ... “With prompt injection, you can change what the AI agent is supposed to do,” says Fabien Cros ... Model owners and operators use perturbed data to test models for resiliency, but hackers use it to disrupt. In an adversarial input attack, malicious actors feed deceptive data to a model with the goal of making the model output incorrect. ... Like other software systems, AI systems are built with a combination of components that can include open-source code, open-source models, third-party models, and various sources of data. Any security vulnerability in the components can show up in the AI systems. This makes AI systems vulnerable to supply chain attacks, where hackers can exploit vulnerabilities within the components to launch an attack. ... Also called model jailbreaking, attackers’ goal here is to get AI systems — primarily through engaging with LLMs — to disregard the guardrails that confine their actions and behavior, such as safeguards to prevent harmful, offensive, or unethical outputs.

The future of authentication in 2026: Insights from Yubico’s experts

As we look ahead to the future of authentication and identity, 2026 will be a pivotal year as the industry intensifies its focus on the standardization work required to make post-quantum cryptography (PQC) viable at scale as we near a post-quantum future. ... The proven, most effective solution to combat stolen and fake identities is the use of – specifically, strong authentication combined with digital identity verification. The good news is countries around the world are taking action, with the EU moving forward with a bold plan over the next year: By late December 2026, each Member State must make at least one available. ... AI's usefulness has rapidly improved over the years, and I anticipate that it will eventually help the general public in a meaningful way. In 2026, the cybersecurity industry should focus more efforts globally on accelerating the adoption of digital content transparency and authenticity standards to help everyone discern fact from fiction and continue the phishing-resistant MFA journey to minimize some of the impact of scams. ... In 2026, there will be a pivotal shift in the digital identity landscape as the industry moves beyond a narrow, consumer-centric focus to one focused on the enterprise. While the public conversation around digital identities has historically centered on consumer-facing scenarios like age verification, the coming year will bring a realisation that robust digital identity truly belongs in the heart of businesses.

7 changes to the CIO role in 2026

As AI transforms how people do their jobs, CIOs will be expected to step up and help lead the effort.
“A lot of the conversations are about implementing AI solutions, how to make solutions work, and how they add value,” says Ryan Downing. “But the reality is with the transformation AI is bringing into the workplace right now, there’s a fundamental change in how everyone will be working.” ... This year, the build or buy decisions for AI will have dramatically bigger impacts than they did before. In many cases, vendors can build AI systems better, quicker, and cheaper than a company can do it themselves. And if a better option comes along, switching is a lot easier than when you’ve built something internally from scratch. ... The key is to pick platforms that have the ability to scale, but are decoupled, he says, so enterprises can pivot quickly, but still get business value. “Right now, I’m prioritizing flexibility,” he says. , chief AI officer at management consulting firm , recommends CIOs identify aspects of AI that are stable, and those that change rapidly, and make their platform selections accordingly. ... “In the past, IT was one level away from the customer,” he says. “They enabled the technology to help business functions sell products and services. Now with AI, CIOs and IT build the products, because everything is enabled by technology. They go from the notion of being services-oriented to product-oriented.”

Agentic AI scaling requires new memory architecture

To avoid recomputing an entire conversation history for every new word generated, models store previous states in the . In agentic workflows, this cache acts as persistent memory across tools and sessions, growing linearly with sequence length. This creates a distinct data class. Unlike financial records or customer logs, KV cache is derived data; it is essential for immediate performance but does not require the heavy durability guarantees of enterprise file systems. General-purpose storage stacks, running on standard CPUs, expend energy on metadata management and replication that agentic workloads do not require. The current hierarchy, spanning from GPU HBM (G1) to shared storage (G4), is becoming inefficient ... The industry response involves inserting a purpose-built layer into this hierarchy. The ICMS platform establishes a “G3.5” tier—an Ethernet-attached flash layer designed explicitly for gigascale inference. This approach integrates storage directly into the compute pod. By utilising the data processor, the platform offloads the management of this context data from the host CPU. The system provides petabytes of shared capacity per pod, boosting the scaling of agentic AI by allowing agents to retain massive amounts of history without occupying expensive HBM. The operational benefit is quantifiable in throughput and energy.

Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - January 08, 2026