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 (NICs), 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. DORA,
NIS2, 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 Gartner 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 LLMs 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 Didi Gurfinkel in an interview
with VentureBeat. 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 CSC domain security study 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. Lookalike domains 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 Pentera Labs, 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 Hackazon,
a free, intentionally vulnerable test site developed by Deloitte, 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 AWS, Azure, and Google Cloud platforms. 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 Jacob Ideskog ... 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
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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
Check Point Research, 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: Micron Technology, SK Hynix, and Samsung.
TrendForce, a Taipei-based market researcher that specializes in the memory
market, recently said it expects average DRAM 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 DDR4 and DDR5, 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 Windows 10, a whole
lot of laptops are being replaced with Windows 11 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 Natasha August, founder of RM11, a content monetization
platform for creators in Carrollton, Texas. “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 TechNewsWorld. In business, the gap lies between AI’s raw
analytical capabilities and its ability to drive tangible, repeatable business
outcomes, maintained Nithin Mummaneni ... “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.”
