Daily Tech Digest by Kannan Subbiah: Daily Tech Digest

Quote for the day:

"Lost money can be found. Lost time is lost forever. Protect what matters most." -- @ValaAfshar

PTP is the New NTP: How Data Centers Are Achieving Real-Time Precision

Precision Time Protocol (PTP) – an approach that is more complex to implement but worth the extra effort, enabling a whole new level of timing synchronization accuracy. ... Keeping network time in sync is important on any network. But it’s especially critical in data centers, which are typically home to large numbers of network-connected devices, and where small inconsistencies in network timing could snowball into major network synchronization problems. ... NTP works very well in situations where networks can tolerate timing inconsistencies of up to a few milliseconds (meaning thousandths of a second). But beyond this, NTP-based time syncing is less reliable due to limitations ... Unlike NTP, PTP doesn’t rely solely on a server-client model for syncing time across networked devices. Instead, it uses time servers in conjunction with a method called hardware timestamping on client devices. Hardware timestamping involves specialized hardware components, usually embedded in network interface cards (), to track time. Central time servers still exist under PTP. But rather than having software on servers connect to the time servers, hardware devices optimized for the task do this work. The devices also include built-in clocks, allowing them to record time data faster than they could if they had to forward it to the generic clock on a server.

Why AI adoption requires a dedicated approach to cyber governance

Today enterprises are facing unprecedented internal pressure to adopt AI tools at speed. Business units are demanding AI solutions to remain competitive, drive efficiency, and innovate faster. But existing cyber governance and third-party risk management processes were never designed to operate at this pace. ... Without modernized cyber governance and AI-ready risk management capabilities, organizations are forced to choose between speed and safety. To truly enable the business, governance frameworks must evolve to match the speed, scale, and dynamism of AI adoption – transforming security from a gatekeeper into a business enabler. ... What’s more, compliance doesn’t guarantee security. , , and other regulatory frameworks set only minimum requirements and rely on reporting at specific points in time. While these reports are accurate when submitted, they capture only a snapshot of the organization’s security posture, so gaps such as human errors, legacy system weaknesses, or risks from fourth- and Nth-party vendors can still emerge afterward. What’s more, human weakness is always present, and legacy systems can fail at crucial moments. ... While there’s no magic wand, there are tried-and-tested approaches that resolve and mitigate the risks of AI vendors and solutions. Mapping the flow of data around the organization helps reveal how it’s used and resolve blind spots. Requiring AI tools to include references for their outputs ensures that risk decisions are trustworthy and reliable.

What CIOs get wrong about integration strategy and how to fix it

As advises, business and IT should be equal partners in the definition of integration strategy, representing a radical departure from the traditional IT delivery and business “project sponsorship” model. This close collaboration and shared accountability result in dramatically higher success rates ... A successful integration strategy starts by aligning with the organization’s business drivers and strategic objectives while identifying the integration capabilities that need to be developed. Clearly defining the goals of technology implementation, establishing governance frameworks and decision-making authority and setting standards and principles to guide integration choices are essential. Success metrics should be tied to business outcomes, and the integration approach should support broader digital transformation initiatives. ... Create cross-functional data stewardship teams with authority to make binding decisions about data standards and quality requirements. Document what data needs to be shared between systems, which applications are the “source of truth.” Define and document any regulatory or performance requirements to guide your technical planning. ... Integrations that succeed in production are designed with clear system-of-record rules, traceable transactions, explicit recovery paths and well-defined operational ownership. Preemptive integration is not about reacting faster — it’s about ensuring failures never reach the business.

CFOs are now getting their own 'vibe coding' moment thanks to Datarails

For the modern CFO, the hardest part of the job often isn't the math—it's the storytelling. After the books are closed and the variances calculated, finance teams spend days, sometimes weeks, manually copy-pasting charts into PowerPoint slides to explain why the numbers moved. ... Datarails’ new agents sit on top of a unified data layer that connects these disparate systems. Because the AI is grounded in the company’s own unified internal data, it avoids the hallucinations common in generic while offering a level of privacy required for sensitive financial data. "If the CFO wants to leverage AI on the CFO level or the organization data, they need to consolidate the data," explained Datarails CEO and co-founder in an interview with . By solving that consolidation problem first, Datarails can now offer agents that understand the context of the business. "Now the CFO can use our agents to run analysis, get insights, create reports... because now the data is ready," Gurfinkel said. ... "Very soon, the CFO and the financial team themselves will be able to develop applications," Gurfinkel predicted. "The LLMs become so strong that in one prompt, they can replace full product runs." He described a workflow where a user could simply prompt: "That was my budget and my actual of the past year. Now build me the budget for the next year."

The internet’s oldest trust mechanism is still one of its weakest links

Attackers continue to rely on domain names as an entry point into enterprise systems. A finds that large organizations leave this part of their attack surface underprotected, even as attacks become more frequent. ... Large companies continue to add baseline protections, though adoption remains uneven. Email authentication shows the most consistent improvement, driven by phishing activity and regulatory pressure. Organizations still leave email domains partially protected, which allows spoofing to persist. Other protections see much slower uptake. ... Consumer oriented registrars tend to emphasize simplicity and cost. Organizations that rely on them often lack access to protections that limit the impact of account compromise or social engineering. Risk increases as domain portfolios grow and change. ... Brand impersonation through domain spoofing remains widespread. tied to major brands are often owned by third parties. Some appear inactive while still supporting email activity. Inactive domains with mail records allow attackers to send phishing messages that appear associated with trusted brands. Others are parked with advertising networks or held for later use. A smaller portion hosts malicious content, though dormant domains can be activated quickly. ... Gaps appear in infrastructure related areas. DNS redundancy and registry lock adoption lag, and many unicorns rely on consumer grade registrars. These limitations become more pronounced as operations scale.

Misconfigured demo environments are turning into cloud backdoors to the enterprise

Internal testing, product demonstrations, and security training are critical practices in cybersecurity, giving defenders and everyday users the tools and wherewithal to prevent and respond to enterprise threats. However, according to new research from , when left in default or misconfigured states, these “test” and “demo” environments are yet another entry point for attackers — and the issue even affects leading security companies and Fortune 500 companies that should know better. ... After identifying an exposed instance of , a free, intentionally vulnerable test site developed by , during a routine cloud security assessment for a client, Yaffe performed a five-step hunt for exposed apps. His team uncovered 1,926 “verified, live, and vulnerable applications,” more than half of which were running on enterprise-owned infrastructure on . They then discovered 109 exposed credential sets, many accessible via a low-priority lab environment, tied to overly-privileged identity access management (IAM) roles. These often granted “far more access” than a ‘training’ app should, Yaffe explained, and provided attackers:Administrator-level access to cloud accounts, as well as full access to S3 buckets, GCS, and Azure Blob Storage; The ability to launch and destruct compute resources and read and write to secrets managers; Permissions to interact with container registries where images are stored, shared, and deployed.

Cyber Insights 2026: API Security – Harder to Secure, Impossible to Ignore

“We’re now entering a new API boom. The previous wave was driven by cloud adoption, mobile apps, and microservices. Now, the rise of AI agents is fueling a rapid proliferation of APIs, as these systems generate massive, dynamic, and unpredictable requests across enterprise applications and cloud services,” comments ... The growing use of agentic AI systems and the way they act autonomously, making decisions and triggering workflows, is ballooning the number of APIs in play. “It isn’t just ‘I expose one billing API’,” he continues, “now there are dozens of APIs that feed data to LLMs or AI agents, accept decisions from AI agents, facilitate orchestration between services and micro-apps, and potentially expose ‘agentic’ endpoints ... APIs have been a major attack surface for years – the problem is ongoing. Starting in 2025 and accelerating through 2026 and beyond, the rapid escalation of enterprise agentic AI deployments will multiply the number of APIs and increase the attack surface. That alone suggests that attacks against APIs will grow in 2026. But the attacks themselves will scale and be more effective through adversaries’ use of their own agentic AI. Barr explains: “Agentic AI means that bad actors can automate reconnaissance, probe API endpoints, chain API calls, test business-logic abuse, and execute campaigns at machine scale. Possession of an API endpoint, particularly a self-service, unconstrained one, becomes a lucrative target. And AI can generate payloads, iterate quickly, bypass simple heuristics, and map dependencies between APIs.”

Complex VoidLink Linux Malware Created by AI

An advanced cloud-first malware framework targeting Linux systems was created almost entirely by artificial intelligence (AI), a move that signals significant evolution in the use of the technology to develop advanced malware. VoidLink — comprised of various cloud-focused capabilities and modules and designed to maintain long-term persistent access to Linux systems — is the first case of wholly original malware being developed by AI, according to , which discovered and detailed the malware framework last week. While other AI-generated malware exists, it's typically "been linked to inexperienced threat actors, as in the case of FunkSec, or to malware that largely mirrored the functionality of existing open-source malware tools," ... The malware framework, linked to a suspected, unspecified Chinese actor, includes custom loaders, implants, rootkits, and modular plug-ins. It also automates evasion as much as possible by profiling a Linux environment and intelligently choosing the best strategy for operating without detection. Indeed, as Check Point researchers tracked VoidLink in real time, they watched it transform quickly from what appeared to be a functional development build into a comprehensive, modular framework that became fully operational in a short timeframe. However, while the malware itself was high-functioning out of the gate, VoidLink's creator proved to be somewhat sloppy in their execution.

What’s causing the memory shortage?

Right now, the industry is suffering the worst memory shortage in history, and that’s with three core suppliers: . , a Taipei-based market researcher that specializes in the memory market, recently said it expects average memory prices to rise between 50% and 55% this quarter compared to the fourth quarter of 2025. Samsung recently issued a similar warning. So what caused this? Two letters: AI. The rush to build AI-oriented data centers has resulted in virtually all of the memory supply being consumed by data centers. AI requires massive amounts of memory to process its gigantic data sets. A traditional server would usually come with 32 GB to 64 GB of memory, while AI servers have 128 GB or more. ... There are other factors at play here, too, of course. The industry is in a transition period between and , as DDR5 comes online and DDR4 fades away. These transitions to a new memory format are never quick or easy, and it usually take years to make a full shift. There has also been increased demand from both client and server sides. With Microsoft ending support for , a whole lot of laptops are being replaced with systems, and new laptops come with DDR5 memory — the same memory used in an AI server. ... “What’s likely to happen, from a market perspective, is we’ll see the market grow less in ’26 than we had anticipated, but ASPs are likely to stay or increase. ...” he said.

OpenAI CFO Comments Signal End of AI Hype Cycle

By focusing on “practical adoption,” OpenAI can close the gap between what AI now makes possible and how people, companies, and countries are using it day to day. “The opportunity is large and immediate, especially in health, science, and enterprise, where better intelligence translates directly into better outcomes,” she noted. “Infrastructure expands what we can deliver,” she continued. “Innovation expands what intelligence can do. Adoption expands who can use it. Revenue funds the next leap. This is how intelligence scales and becomes a foundation for the global economy.” The framing reflects a shift from big-picture AI promise to day-to-day deployment and measurable results. ... There’s also a gap between what AI can do and how people are actually using it in daily life, noted , founder of , a content monetization platform for creators in . “AI tools are incredibly powerful, but for many people and businesses, it’s still unclear how to turn that power into something practical like saving time, making money, or improving how they work,” she told . In business, the gap lies between AI’s raw analytical capabilities and its ability to drive tangible, repeatable business outcomes, maintained ... “The winning play is less ‘AI that answers’ and more ‘AI that completes tasks safely and predictably,'” he continued. “Adoption happens when AI becomes part of the workflow, not a separate destination.”

Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - January 22, 2026