Quote for the day:
“Make sure you don’t start seeing yourself through the eyes of those who don’t value you.” -- Anonymous
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AI observability: How CIOs can see past their org blind spots
The article discusses AI observability, highlighting how traditional IT
monitoring tools are insufficient for evaluating artificial intelligence
performance. As AI applications expand across modern businesses, CIOs frequently
struggle with deep blind spots regarding system usage, model drift, performance
degradation, and unauthorized "shadow AI" tools. Unlike standard software that
relies on predictable metrics like uptime, AI systems operate probabilistically,
meaning the exact same inputs can yield wildly varying outcomes. This inherent
unpredictability creates compounding risks, especially as enterprises connect
multiple autonomous agents into complex workflows where minor data issues can
quietly corrupt downstream results for weeks before finally breaking. To address
these organizational vulnerabilities, experts suggest shifting from front-loaded
risk assessments to continuous, full-stack visibility. This comprehensive
approach involves setting up automated guardrails for model outputs, maintaining
a clear catalog of active systems, and establishing an integrated control plane.
By compiling system telemetry, semantic mapping, and risk thresholds into a
single shared interface, different corporate stakeholders, such as finance,
human resources, and security teams, can easily monitor the metrics relevant to
their own departments. Ultimately, treating observability as a core design
principle rather than an afterthought enables leadership to safely scale their
AI initiatives, manage ballooning costs, and build lasting organizational
trust.The Validation Gap Is Costing You More Than You Think
According to a report on software delivery, development teams are writing more
code than ever, but less of it is actually reaching production. Analysis of
millions of workflows reveals that while development throughput has spiked,
main branch success rates have fallen to a five-year low of roughly seventy
percent. This drop stems from a gap in how software is validated. Traditional
continuous integration systems were designed for humans who commit code
gradually. Today, automated artificial intelligence tools generate code at a
rapid pace that completely overwhelms traditional review processes. When
errors are caught late in the shared integration system, it results in
expensive compute costs, wasted time, and broken focus as the automated tools
have already moved on to other tasks. To solve this dilemma, engineering teams
must shift testing much earlier into the initial writing phase. By running
smaller, targeted tests while the automated code generator is still actively
focused on a task, teams can fix errors immediately without draining
infrastructure resources. When this early testing stage and the final
integration pipeline share historical information, the entire delivery system
becomes smarter and more efficient. Ultimately, addressing this validation
imbalance helps organizations safely increase their software output without
absorbing downstream failures.Why Attack Surface Management Breaks in OT (and What Actually Works)
Traditional Attack Surface Management (ASM) fails in Operational Technology
(OT) environments because industrial infrastructure operates on fundamentally
different principles than standard enterprise IT systems. Many legacy
industrial protocols, such as Modbus, DNP3, and BACnet, were created decades
ago without built-in encryption, session management, or authentication
mechanisms. Consequently, their lack of security is an inherent property of
the system design rather than a simple configuration mistake that can easily
be patched. Furthermore, the active interrogation techniques standard in IT
security can severely disrupt operational networks; sending aggressive probes
often overwhelms the limited network stacks of Programmable Logic Controllers
(PLCs), causing critical physical machinery to misbehave or shut down
entirely. Because these industrial environments do not support software agents
or standard diagnostic queries, establishing a reliable asset inventory is
remarkably difficult. To mitigate risks effectively, security teams must
reverse their usual enterprise instincts by defaulting to passive network
monitoring and treating active probing as a tightly managed privilege.
Utilizing passive internet search data allows analysts to map exposed external
components safely without introducing disruptive traffic to live plants.
Ultimately, embedding clear safety workflows and strict rate limits into
automated security tools ensures that scanning efforts do not cause unintended
physical operational downtime.Backup and recovery architecture best practices for UK SMEs
The Security Boulevard article explains that smaller businesses in the UK
should treat backup and recovery as a practical safety measure rather than a
simple file storage task. A sensible backup plan focuses entirely on
restoration outcomes, ensuring a company can keep trading after an incident
like an accidental deletion, system failure, or cyberattack. Instead of buying
expensive software tools first, these organizations should prioritize their
systems based on how a disruption directly impacts their daily operations,
clearly defining how much downtime and data loss they can realistically
handle. To build stronger protection, companies must keep multiple copies of
their files across separate locations and accounts so that a single compromise
or mistake cannot destroy both the live data and the backups. Furthermore,
restricting access to named administrative accounts, applying settings that
prevent recent copies from being altered or deleted, and choosing backup
styles that match different types of systems will lower overall risk. Because
copying data does not automatically mean a system can be successfully rebuilt,
regular testing is necessary to catch unexpected delays and overlooked
technical connections. Ultimately, the article recommends documenting these
steps in short, straightforward guides with clear ownership so that staff can
respond calmly when an unexpected outage occurs.
Challenging AI Assumptions
In his Forbes article, John Werner encourages readers to reconsider common
assumptions about artificial intelligence that might limit our ability to
effectively navigate the future. He notes that early technology milestones,
such as the IBM Watson era, conditioned the public to view machine
intelligence as a centralized database focused entirely on factual recall,
rapid calculation, and deterministic logic. However, as the field quickly
moves toward a future centered on autonomous software agents, Werner argues
that continuing to rely on these old centralized frameworks is a foundational
mistake. Drawing from insights shared at a recent MIT-linked conference, he
suggests that the true development of artificial intelligence will ultimately
mirror biological organisms and complex economic networks rather than
centralized computer hardware. Because the long-term impact of this technology
on global society is frequently compared to foundational discoveries like fire
or electricity, our structural approach must evolve accordingly. Instead of
designing isolated, top-down systems, we should foster collaborative,
decentralized, and biologically inspired ecosystems of digital agents. By
shifting our perspective away from rigid central control, human society can
establish cooperative frameworks that allow these increasingly autonomous
systems to be integrated smoothly, sustainably, and safely into everyday
life.The Architecture Questions I Ask Before an Initiative Starts
In his article, Eetu Niemi outlines three practical architectural questions to
ask before any major business project begins, aiming to clarify scope and
prevent costly downstream surprises. The first question focuses on what is
actually changing within the organization. Project names can often be
deceptive, so teams must carefully distinguish between a project's stated
scope and its actual, wider impact. If a change only alters a single isolated
system, heavy architectural planning is rarely needed. The second question
addresses visible dependencies, identifying which software applications, data
streams, teams, or external vendors the project relies upon. Uncovering this
scattered knowledge early helps avoid scheduling or financial surprises down
the line without over-documenting every minor connection. The final question
evaluates which decisions would be expensive to reverse later on. While
choices regarding technology platforms, data models, or core software might
seem like minor delivery choices initially, they quickly harden into fixed
constraints once other systems are built around them. By addressing what is
changing, identifying dependencies, and flagging irreversible choices early
on, architects can guide decision-making through plain conversations and basic
diagrams. This upfront evaluation allows organizations to balance development
speed with long-term operational stability without drowning teams in
unnecessary paperwork or rigid governance structures.
Building a Quantum-Safe Foundation: WWT and Cisco Accelerate Post-Quantum Readiness
The Next Wow Factor: A Conversation with Sidney Lu, Chairman and CEO, Foxconn Interconnect Technology (FIT)
In this interview, Sidney Lu, the chairman and chief executive officer of Foxconn Interconnect Technology, reflects on his forty year career and personal leadership philosophy. He oversees a large global workforce that manufactures vital electrical parts, such as connectors and cables, for common electronics like smartphones, electric vehicles, and computer servers. Lu credits his way of leading to a balance of Eastern discipline and Western workplace confidence, which he gained while studying and working in the United States. A foundational lesson from his mother taught him to take full responsibility, avoid self pity, and quickly move past mistakes, a clear mindset he later applied to difficult engineering problems. As a leader, Lu strongly emphasizes supporting his employees by taking personal blame for business setbacks rather than shifting it downward to others. To stay relevant and avoid falling behind, he consistently challenges his team to deliver an unexpected, fresh product or advancement every three years. Under his quiet guidance, the company has expanded significantly while building long lasting relationships with clients based on deep trust. Ultimately, Lu attributes his steady motivation to a simple, genuine enjoyment of his daily work and a constant curiosity about what comes next.Post-quantum cryptography is not the future. It is your current reality
The article explains that post-quantum cryptography is an immediate
operational necessity rather than a distant concern. Major tech companies and
governments are already deploying these new algorithms because waiting for a
functional quantum computer introduces severe, immediate risks to digital
infrastructure. Chief among these is the "Harvest Now, Decrypt Later"
strategy, where adversaries actively intercept and store encrypted network
traffic today with the intention of decrypting it once advanced quantum
hardware becomes available. Additionally, existing digital signatures and root
certificates face future retroactive forgery, threatening the core
authenticity of secure software supply chains. Successfully upgrading an
enterprise is rarely an issue of funding or algorithm selection; the real
challenge is an absolute lack of visibility. Modern corporate networks contain
countless forgotten encryption points hidden within legacy software, cloud
environments, and device firmware. To address this, organizations must
establish a continuous inventory, known as a Cryptography Bill of Materials,
to locate and evaluate their vulnerable assets. Once an organization maps
these internal elements, it can cultivate true cryptographic agility, enabling
systems to swap underlying protocols smoothly without disrupting daily
operations or breaking system compatibility. Rather than delaying, companies
must prioritize data based on its overall longevity and methodically adapt to
finalized standards, securing their systems before the available
implementation runway runs out entirely.Non-Human Identities Are Outgrowing Your Governance Model
Many companies have developed dependable systems to manage human user
identities, but they are falling behind when it comes to non-human accounts.
Machine identities, such as service accounts, API keys, security certificates,
and automated workloads, now vastly outnumber human credentials, particularly
in cloud computing environments. Because these digital entities lack
individual managers, specific start dates, or standard offboarding processes,
they often slip through traditional corporate tracking systems completely
unnoticed. This ongoing management gap leads to significant security problems,
including orphaned accounts that maintain high-level administrative access
years after a project ends, static passwords that are never rotated, and old
third-party integrations that leave access doors wide open to former external
vendors. Additionally, neglecting these machine identities creates serious
compliance exposure during regulatory audits under strict frameworks like SOC
2 or ISO 27001, which mandate clear internal accountability and regular access
reviews. To fix these issues, organizations need to update their tracking
strategies and treat non-human credentials with the exact same discipline
applied to human staff. This approach means assigning clear owners to every
automated account, mapping their actual usage patterns, setting up predictable
update cycles, and deleting them automatically when software is retired. By
establishing this structured oversight, security teams can successfully close
dangerous operational loopholes and maintain control.
