Quote for the day:
"Our greatest fear should not be of failure but of succeeding at things in life that don't really matter." -- Francis Chan
The rise of vCISO as a viable cybersecurity career path
Companies that don’t have the means to hire a full-time CISO still face the
same harsh realities their peers do — heightened compliance demands,
escalating cyber incidents, and growing tech-related risks. A part-time
security leader can help them assess their state of security and build out a
program from scratch, or assist a full-time director-level security leader
with a project. ... In some of these ongoing relationships this could be to
fill the proverbial chair of the CISO, doing all the traditional work of the
role on a part-time basis. This is the kind of arrangement most likely to be
referred to as a fractional role. Other retainer arrangements may just be for
an advisory position where the client is buying regular mindshare of the vCISO
to supplement their tech team’s knowledge pool. They could be a strategic
sounding board to the CIO or even a subject-matter expert to the director of
security or newly installed CISO. But vCISOs can work on a project-by-project
or hourly basis as well. “It’s really what works best for my potential
client,” says Demoranville. “I don’t want to force them into a box. So, if a
subscription model works or a retainer, cool. If they only want me here for a
short engagement, maybe we’re trying to put in a compliance regimen for ISO
27001 or you need me to review NIST, that’s great too.”
Why Indian Banks Need a Sovereign Cloud Strategy
Enterprises need to not only implement better compliance strategies but also
rethink the entire IT operating model. Managed sovereign cloud services can
help enterprises address this need. ... The need for true sovereignty
becomes crucial in a world where many global cloud providers, even when
operating within Indian data centers, are subject to foreign laws such as the
U.S. Clarifying Lawful Overseas Use of Data Act or the Foreign Intelligence
Surveillance Act. These regulations can compel disclosure of Indian banking
data to overseas governments, undermining trust and violating the spirit of
data localization mandates. "When an Indian bank chooses a global cloud
provider with U.S. exposure, they're essentially opening a backdoor for
foreign jurisdictions to access sensitive Indian financial data," Rajgopal
said. "Sovereignty is a strategic necessity." Managed sovereign clouds not
only align with India's compliance frameworks but also reduce complexity by
integrating regulatory controls directly into the cloud stack. Instead of
treating compliance as an afterthought, it is incorporated in the
architecture. ... "Banks today are not just managing money; they are managing
trust, security and compliance at unprecedented levels. Sovereign cloud is no
longer optional. It's the future of financial resilience," said Pai.
Study Suggests Quantum Entanglement May Rewrite the Rules of Gravity
Entanglement entropy measures the degree of quantum correlation between
different regions of space and plays a key role in quantum information theory
and quantum computing. Because entanglement captures how information is shared
across spatial boundaries, it provides a natural bridge between quantum theory
and the geometric fabric of spacetime. In conventional general relativity, the
curvature of spacetime is determined by the energy and momentum of matter and
radiation. The new framework adds another driver: the quantum information
shared between fields. This extra term modifies Einstein’s equations and
offers an explanation for some of gravity’s more elusive behaviors, including
potential corrections to Newton’s gravitational constant. ... One of the more
striking implications involves black hole thermodynamics. Traditional
equations for black hole entropy and temperature rely on Newton’s constant
being fixed. If gravity “runs” with energy scale — as the study proposes —
then these thermodynamic quantities also shift. ... Ultimately, the study does
not claim to resolve quantum gravity, but it does reframe the problem. By
showing how entanglement entropy can be mathematically folded into Einstein’s
equations, it opens a promising path that links spacetime to information — a
concept familiar to quantum computer scientists and physicists alike.
Maximising business impact: Developing mission-critical skills for organisational success
Often, L&D is perceived merely as an HR-led function tasked with building
workforce capabilities. However, this narrow framing extensively limits its
potential impact. As Cathlea shared, “It’s time to educate leaders that
L&D is not just a support role—it’s a business-critical responsibility
that must be shared across the organisation. By understanding what success
looks like through the eyes of different functions, L&D teams can design
programmes that support those ambitions — and crucially, communicate value in
language that business leaders understand. The panel referenced a case from a
tech retailer with over 150,000 employees, where the central L&D team
worked to identify cross-cutting capability needs, such as communication,
project management, and leadership, while empowering local departments to
shape their training solutions. This balance of central coordination and local
autonomy enabled the organisation to scale learning in a way that was both
relevant and impactful. ... The shift towards skill-based development is also
transforming how learning experiences are designed and delivered. What matters
most is whether these learning moments are recognised, supported, and
meaningfully connected to broader organisational goals.
What software developers need to know about cybersecurity
Training developers to write secure code shouldn’t be looked at as a one-time
assignment. It requires a cultural shift. Start by making secure coding
techniques are the standard practice across your team. Two of the most
critical (yet frequently overlooked) practices are input validation and input
sanitization. Input validation ensures incoming data is appropriate and safe
for its intended use, reducing the risk of logic errors and downstream
failures. Input sanitization removes or neutralizes potentially malicious
content—like script injections—to prevent exploits like cross-site scripting
(XSS). ... Authentication and authorization aren’t just security check
boxes—they define who can access what and how. This includes access to code
bases, development tools, libraries, APIs, and other assets. ... APIs may be
less visible, but they form the connective tissue of modern applications. APIs
are now a primary attack vector, with API attacks growing 1,025% in 2024
alone. The top security risks? Broken authentication, broken authorization,
and lax access controls. Make sure security is baked into API design from the
start, not bolted on later. ... Application logging and monitoring are
essential for detecting threats, ensuring compliance, and responding promptly
to security incidents and policy violations. Logging is more than a
check-the-box activity—for developers, logging can be a critical line of
defense.
Why security teams cannot rely solely on AI guardrails
The core issue is that most guardrails are implemented as standalone NLP
classifiers—often lightweight models fine-tuned on curated datasets—while the
LLMs they are meant to protect are trained on far broader, more diverse
corpora. This leads to misalignment between what the guardrail flags and how
the LLM interprets inputs. Our findings show that prompts obfuscated with
Unicode, emojis, or adversarial perturbations can bypass the classifier, yet
still be parsed and executed as intended by the LLM. This is particularly
problematic when guardrails fail silently, allowing semantically intact
adversarial inputs through. Even emerging LLM-based judges, while promising,
are subject to similar limitations. Unless explicitly trained to detect
adversarial manipulations and evaluated across a representative threat
landscape, they can inherit the same blind spots. To address this, security
teams should move beyond static classification and implement dynamic,
feedback-based defenses. Guardrails should be tested in-system with the actual
LLM and application interface in place. Runtime monitoring of both inputs and
outputs is critical to detect behavioral deviations and emergent attack
patterns. Additionally, incorporating adversarial training and continual red
teaming into the development cycle helps expose and patch weaknesses before
deployment.
Finding the Right Architecture for AI-Powered ESG Analysis
Rather than choosing between competing approaches, we developed a hybrid
architecture that leverages the strengths of both deterministic workflows and
agentic AI: For report analysis: We implemented a structured workflow that
removes the Intent Agent and Supervisor from the process, instead providing
our own intention through a report workflow. This orchestrates the process
using the uploaded sustainability file, synchronously chaining prompts and
agents to obtain the company name and relevant materiality topics, then
asynchronously producing a comprehensive analysis of environmental, social,
and governance aspects. For interactive exploration: We maintained the
conversational, agentic architecture as a core component of the solution.
After reviewing the initial structured report, analysts can ask follow-up
questions like, “How does this company’s emissions reduction claims compare to
their industry peers?” ... By marrying these approaches, enterprise architects
can build systems that maintain human oversight while leveraging AI to handle
data-intensive tasks – keeping human analysts firmly in the driver’s seat with
AI serving as powerful analytical tools rather than autonomous
decision-makers. As we navigate the rapidly evolving landscape of AI
implementation, this balanced approach offers a valuable pathway forward.
The Rise of xLMs: Why One-Size-Fits-All AI Models Are Fading
To reach its next evolution, the LLM market will follow all other widely
implemented technologies and fragment into an “xLM” market of more specialized
models, where the x stands for various models. Language models are being
implemented in more places with application- and use case-specific demands, such
as lower power or higher security and safety measures. Size is another factor,
but we’ll also see varying functionality and models that are portable, remote,
hybrid, and domain and region-specific. With this progression, greater
versatility and diversity of use cases will emerge, with more options for
pricing, security, and latency. ... We must rethink how AI models are trained to
fully prepare for and embrace the xLM market. The future of more innovative AI
models and the pursuit of artificial general intelligence hinge on advanced
reasoning capabilities, but this necessitates restructuring data management
practices. ... Preparing real-time data pipelines for the xLM age inherently
increases pressure on data engineering resources, especially for organizations
currently relying on static batch data uploads and fine-tuning. Historically,
real-time accuracy has demanded specialized teams to complete regular batch
uploads while maintaining data accuracy, which presents cost and resource
barriers.
Ernst & Young exec details the good, bad and future of genAI deployments
“There is a huge skills gap in data science in terms of the number of people
that can do that well, and that is not changing. Everywhere else we can talk
about what jobs are changing and where the future is. But AI scientists, data
scientists, continue to be the top two in terms of what we’re looking for. I do
think organizations are moving to partner more in terms of trying to leverage
those skills gap….” The more specific the case for the use of AI, the more
easily you can calculate the ROI. “Healthcare is going to be ripe for it. I’ve
talked to a number of doctors who are leveraging the power of AI and just doing
their documentation requirements, using it in patient booking systems, workflow
management tools, supply chain analysis. There, there are clear productivity
gains, and they will be different per sector. “Are we also far enough along to
see productivity gains in R&D and pharmaceuticals? Yes, we are. Is it the
Holy Grail? Not yet, but we are seeing gains and that’s where I think it gets
more interesting. “Are we far enough along to have systems completely automated
and we just work with AI and ask the little fancy box in front of us to print
out the balance sheet and everything’s good? No, we’re a hell of a long way away
from that.
How Human-Machine Partnerships Are Evolving in 2025
“Soon, there will be no function that does not have AI as a fundamental
ingredient. While it’s true that AI will replace some jobs, it will also create
new ones and reduce the barrier of entry into many markets that have
traditionally been closed to just a technical or specialized group,” says
Bukhari. “AI becoming a part of day-to-day life will also force us to embrace
our humanity more than ever before, as the soft skills AI can’t replace will
become even more critical for success in the workplace and beyond.” ... CIOs and
other executives must be data and AI literate, so they are better equipped to
navigate complex regulations, lead teams through AI-driven transformations and
ensure that AI implementations are aligned with business goals and values.
Cross-functional collaboration is also critical. ... AI innovation is already
outpacing organizational readiness, so continuous learning, proactive strategy
alignment and iterative implementation approaches are important. CIOs must
balance infrastructure investments, like GPU resource allocation, with
flexibility in computing strategies to stay competitive without compromising
financial stability. “As the enterprise landscape increasingly incorporates
AI-driven processes, the C-suite must cultivate specific skills that will
cascade effectively through their management structures and their entire human
workforce,” says Miskawi.
