Quote for the day:
"Ideas are easy, implementation is hard." -- Guy Kawasaki
India’s AI Paradox: Why We Need Cloud Sovereignty Before Model Sovereignty
As is clear, cloud sovereignty is the new pillar supporting national security
and having control over infrastructure, data, and digital operations. It has the
capacity to safeguard the country’s national interests, including (but not
limited to) industrial data, citizen information, and AI workloads. For India,
specifically, building a sovereign digital infrastructure guarantees continuity
and trust. It gives the country power to enforce its own data laws, manage
computing resources for homegrown AI systems, and stay insulated from the
tremors of foreign policy decisions or transnational outages. It’s the digital
equivalent of producing energy at home—self-reliant, secure, and governed by
national priorities. ... Sovereign infrastructure is less a matter of where data
sits and more about who controls it and how securely it is managed. With
connected systems, AI workloads spread across networks. This makes it imperative
for security to be built into every layer and stage. As systems grow more
connected and AI workloads spread across networks, security needs to be built
into every layer of technology, not added as an afterthought. That’s where edge
computing and modern cloud-security frameworks come in. ... There is a real cost
involved in neglecting cloud sovereignty. If our AI models continue to depend
upon infrastructure that lies outside our jurisdiction, any changes in foreign
regulations might suddenly restrict access to critical training
datasets. Do CISOs need to rethink service provider risk?
Security leaders face mounting pressure from boards to provide assurance about
third-party risks, while services provider vetting processes are becoming more
onerous — a growing burden for both CISOs and their providers. At the same time,
AI is becoming integrated into more business systems and processes, opening new
risks. CISOs may be forced to rethink their vetting processes with partners to
maintain a focus on risk reduction while treating partnerships as a shared
responsibility. ... When looking to engage a services provider, his vetting
process starts with building relationships first and then working towards a
formal partnership and delivery of services. He believes dialogue helps
establish trust and transparency and underpin the partnership approach. “A lot
of that is ironed out in that really undocumented process. You build up those
relationships first, and then the transactional piece comes after that.” ... “If
your questions stop once the form is complete, you’ve missed the chance to
understand how a partner really thinks about security,” Thiele says. “You learn
a lot more from how they explain their risk decisions than from a yes/no tick
box.” Transparency and collaboration are at the heart of stronger partnerships.
“You can’t outsource accountability, but you can become mature in how you manage
shared responsibility,” Thiele says. ... With AI, Cruz has started to monitor
vendors acquiring ISO 42001 certification for AI governance. “It’s a trend I’m
seeing in some of the work that we’re doing,” she says.The Silent Technical Debt: Why Manual Remediation Is Costing You More Than You Think
A far more challenging and costly form of this debt has silently embedded itself
into the daily operations of nearly every software development team, and most
leaders don’t even have a line item for it. This liability is remediation debt:
The ever-growing cost of manually fixing vulnerabilities in the open source
components that form the backbone of modern applications. For years, we’ve
accepted this process as a necessary chore. A scanner finds a flaw, an alert is
sent, and a developer is pulled from their work to hunt down a patch. ... The
complexity doesn’t stop there. The report reveals that 65% of manual remediation
attempts for a single critical vulnerability require updating at least five
additional “transitive” dependencies, or a dependency of a dependency. This is
the dreaded “dependency conundrum” that developers lament, where fixing one
problem creates a cascade of new compatibility issues. ... It’s time to reframe
our way of dealing with this: the goal is not just to find vulnerabilities
faster but to remediate them instantly. The path forward lies in shifting from
manual labor to intelligent remediation. This means evolving beyond tools that
simply populate dashboards with problems and embracing platforms that solve them
at their source. Imagine a system where a vulnerability is identified, and
instead of creating a ticket, the platform automatically builds, tests, and
delivers a fully patched and compatible version of the necessary component
directly to the developer. AI Isn’t Coming for Data Jobs – It’s Coming for Data Chaos
Data chaos arises when organizations lose control of their information landscape. It’s the confusion born from fragmentation, duplication, and inconsistency when multiple versions of “truth” compete for authority. Poor data quality and disconnected data governance processes often amplify this chaos. This chaos manifests as conflicting reports, inaccurate dashboards, mismatched customer profiles, and entire departments working from isolated datasets that refuse to align. ... Recent industry analyses reveal an accelerating imbalance in the data economy. While nearly 90% of the world’s data has been generated in just the past two years, data professionals and data stewards represent only about 3% of the enterprise workforce, creating a widening gap between information growth and the human capacity to govern it. ... Data chaos doesn’t just strain systems, it strains people. As enterprises struggle to keep pace with growing data volume and complexity, the very professionals tasked with managing it find themselves overwhelmed by maintenance work. ... When applied strategically, AI can transform the data management lifecycle from ingestion to governance reducing human toil and freeing engineers to focus on design, quality, and strategy. Paired with an intelligent data catalog, these systems make information assets instantly discoverable and reusable across business domains. AI-driven data classification tools now tag, cluster, and prioritize assets automatically, reducing manual oversight.Why IT projects still fail
Failure today means an IT project doesn’t deliver expected benefits, according
to CIOs, project leaders, researchers, and IT consultants. Failure can also mean
a project doesn’t produce returns, runs so late as to be obsolete when
completed, or doesn’t engage users who then shun it in response. ... IT leaders
and now business leaders, too, get enamored with technologies, despite years of
admonishments not to do so. The result is a misalignment between the project
objectives and business goals, experienced CIOs and veteran project managers
say. ... Stettler says a business owner with clear accountability is needed to
ensure that business resources are available when required as well as to ensure
process changes and worker adoption happen. He notes that having CIOs — instead
of a business owner — try to make those things happen “would be a
tail-wagging-the-dog scenario.” ... “Executives need to make more time and
engage across all levels of the program. They can’t just let the leaders come
talk to them. They need to do spot checks and quality reviews of deliverable
updates, and check in with those throughout the program,” Stettler says. “And
they have to have the attitude of ‘Bring stuff to me when I can be helpful.’”
... Phillips acknowledges that project teams don’t usually overlook entire
divisions, but they sometimes fail to identify and include all the stakeholders
they should in the project process. Consequently, they miss key requirements to
include, regulations to consider, and opportunities to capitalize on.
The Human Plus AI Quotient: Inside Ascendion's strategy to make AI an amplifier of human talent
Technical skills evolve—mainframes lasted forty years, client-server about twenty, and digital waves even less. Skills will come and go, so we focus on candidates with a strong willingness to learn and invest in themselves. That’s foundational. What’s changed now is the importance of being open to AI. We don’t require deep AI expertise at the outset, but we do look for those who are ready to embrace it. This approach explains why our workforce is so quick to adapt to AI—it’s ingrained in how we hire and develop our people. ... The war for talent has always existed—it’s just the scale and timing that change. For us, the quality of work and the opportunities we provide are key to retention. Being fundamentally an AI-first company is a big differentiator, and our “AI-first” mindset is wired into our DNA. Our employees see a real difference in how we approach projects, always asking how AI can add value. We’ve created an environment that encourages experimentation and learning, and the IP our teams develop—sometimes even around best practices for AI adoption—becomes part of our organisational knowledge base. ... The good news is that for a large cross-section of the workforce, "skilling in AI" is not about mastery of mathematics; it's about improving English writing skills to prompt effectively. We often share prompt libraries with clients because the ability to ask the right question and interpret the output is a significant win.Recruitment Class: What CIOs Want in Potential New Hires
Candidates should be comfortable operating in a very complex, deep digital
ecosystem, Avetisyan said. Now, digital fluency means much more than knowing how
to use a certain tool that is currently popular, including AI tools. There needs
to be an awareness of the broader implications and responsibilities that come
with implementing AI. "It's about integrating AI responsibly and designing for
accessibility," Avetisyan said -- both of which represent big challenges that
must be tackled and kept continuously top of mind. AI should elevate user
experiences. ... There's still a need to demonstrate technical skills with human
skills such as problem-solving, communication, and ethical awareness, she said.
"You can't just be an exceptional coder and right away be effective in our
organization if you don't understand all these other aspects," she said. One
more thing: While vibe coding -- letting AI shoulder much or most of the work --
is a buzzy concept, she said she is not ready to turn her shop of developers
into vibe coders. A more grounded approach to teaching AI fluency is -- or
should be -- the educational mission. ... As for programming? A programmer is
still a programmer, but the job has evolved to become more strategic, Ruch said.
Technical talent will be needed; however, the first few revisions of code will
be pre-written based on the specifications given to AI, he said.
Do programming certifications still matter?
“Certifications are shifting from a checkbox to a compass. They’re less about
proving you memorized syntax and more about proving you can architect systems,
instruct AI coding assistants, and solve problems end-to-end,” says Faizel Khan,
lead AI engineer at Landing Point, an executive search and recruiting firm. ...
Certifications really do two things, Khan adds. “First, they force you to learn
by doing,” he says. “If you’re taking AWS Solutions Architect or Terraform, you
don’t pass by guessing—you plan, build, and test systems. That practice matters.
Second, they act as a public signal. Think of it like a micro-degree. You’re not
just saying, ‘I know cloud.’ You’re showing you’ve crossed a bar that thousands
of other engineers recognize.” But there are cons, too. “In tech, employers
don’t just want credentials, they want proof you can deliver,” says Kevin
Miller, CTO at IFS. “Programming certifications can be a valuable indicator of
your baseline knowledge and competencies, especially if you’re early in your
career or pivoting into tech, but their importance is dwindling.” ... “I’m more
interested in a candidate’s attitude and aptitude: what problems they’ve solved,
what they’ve built, and how they’ve approached challenges,” Watts says.
“Certifications can show commitment and discipline, and they’re especially
useful in highly specialized roles. But I’m cautious when someone presents a
laundry list of certifications with little evidence of real-world application.”
Guarding the Digital God: The Race to Secure Artificial Intelligence
Securing an AI is fundamentally different from securing a traditional computer
network. A hacker doesn’t need to breach a firewall if they can manipulate the
AI’s “mind” itself. The attack vectors are subtle, insidious, and entirely new.
... The debate over whether people or AI should lead this effort presents a
false choice. The only viable path forward is a deep, symbiotic partnership. We
must build a system where the AI is the frontline soldier and the human is the
strategic commander. The guardian AI should handle the real-time, high-volume
defense: scanning trillions of data points, flagging suspicious queries, and
patching low-level vulnerabilities at machine speed. The human experts, in turn,
must set the strategy. They define the ethical red lines, design the security
architecture, and, most importantly, act as the ultimate authority for critical
decisions. If the guardian AI detects a major, system-level attack, it shouldn’t
act unilaterally; it should quarantine the threat and alert a human operator who
makes the final call. ... The power of artificial intelligence is growing at an
exponential rate, but our strategies for securing it are lagging dangerously
behind. The threats are no longer theoretical. The solution is not a choice
between humans and AI, but a fusion of human strategic oversight and AI-powered
real-time defense. For a nation like the United States, developing a
comprehensive national strategy to secure its AI infrastructure is not optional.
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