Quote for the day:
“You live longer once you realize that any time spent being unhappy is wasted.” -- Ruth E. Renkl
DPDP Rules and the Future of Child Data Safety
Most obligations for Data Fiduciaries, including verifiable parental consent,
security safeguards, breach notifications, data minimisation, and processing
restrictions for children’s data, come into force after 18 months. This means
that although the law recognises children’s rights today, full legal
protection will not be enforceable until the culmination of the 18-month
window. ... Parents’ awareness of data rights, online safety, and responsible
technology is the backbone of their informed participation. The government
needs to undertake a nationwide Digital Parenting Awareness Campaign with the
help of State Education Departments, modelled on literacy and health awareness
drives. ... schools often outsource digital functions to vendors without due
diligence. Over the next 18 months, they must map where the student data is
collected and where it flows, renegotiate contracts with vendors, ensure
secure data storage, and train teachers to spot data risks. Nationwide
teacher-training programmes should embed digital pedagogy, data privacy, and
ethical use of technology as core competencies. ... effective implementation
will be contingent on the autonomy, resourcefulness, and accessibility of the
Data Protection Board. The regulator should include specialised talent such as
cybersecurity specialists and privacy engineers. It should be supported by
building an in-house digital forensics unit, capable of investigating leaks,
tracing unauthorised access, and examining algorithmic profiling. 5 best practices for small and medium businesses (SMEs) to strengthen cybersecurity
First, begin with good access control which would entail restricting employees to only the permissions that they specifically require. It is also important to have multi-factor authentication in place, and regularly audit user accounts, particularly when roles shift or personnel depart. Second, keep systems and software current by immediately patching operating systems, applications, and security software to close vulnerabilities before they can be exploited by attackers. Similarly, updates should be automated to avoid human error. The staff are usually at the front line of the defence, so the third essential practice is the continuous ongoing training of employees in identifying phishing attempts, suspicious links, and social engineering methods, making them active guardians of corporate data and effectively cutting the risk of a data breach. Fourth is the safeguarding your data which can be implemented by having regular backups stored safely in multiple places and by complementing them with an explicit disaster recovery strategy, so that you are able to restore operations promptly, reduce downtime, and constrain losses in the event of a cyber attack. Fifth and finally, companies should embrace the layered security paradigm using antivirus tools, firewalls, endpoint protection, encryption, and safe networks. Each of those layers complement each other, creating a resilient defence that protects your digital ecosystem and strengthens trust with partners, customers, and stakeholders.How Artificial Intelligence is Reshaping the Software Development Life Cycle (SDLC)
With AI tools, workflows become faster and more efficient, giving engineers
more time to concentrate on creative innovation and tackling complex
challenges. As these models advance, they can better grasp context, learn from
previous projects, and adapt to evolving needs. ... AI streamlines software
design by speeding up prototyping, automating routine tasks, optimizing with
predictive analytics, and strengthening security. It generates design options,
translates business goals into technical requirements, and uses fitness
functions to keep code aligned with architecture. This allows architects to
prioritize strategic innovation and boosts development quality and efficiency.
... AI is shifting developers’ roles from manual coding to strategic "code
orchestration." Critical thinking, business insight, and ethical
decision-making remain vital. AI can manage routine tasks, but human
validation is necessary for security, quality, and goal alignment. Developers
skilled in AI tools will be highly sought after. ... AI serves to augment, not
replace, the contributions of human engineers by managing extensive data
processing and pattern recognition tasks. The synergy between AI's
computational proficiency and human analytical judgment results in outcomes
that are both more precise and actionable. Engineers are thus empowered to
concentrate on interpreting AI-generated insights and implementing informed
decisions, as opposed to conducting manual data analysis.Innovative Approaches To Addressing The Cybersecurity Skills Gap
In a talent-constrained world, forward-leaning organizations aren’t hiring
more analysts—they’re deploying agentic AI to generate continuous,
cryptographic proof that controls worked when it mattered. This defensible
automation reduces breach impact, insurer friction and boardroom risk—no
headcount required. ... Create an architecture and engineering review board
(AERB) that all current and future technical designs are required to flow
through. Make sure the AERB comprises a small group of your best engineers,
developers, network engineers and security experts. The group should meet
multiple times a year, and all technical staff should be required to rotate
through to listen and contribute to the AERB. ... Build security into product
design instead of adding it in afterward. Embed industry best practices
through predefined controls and policy templates that enforce protection
automatically—then partner with trusted experts who can extend that foundation
with deep, domain-specific insight. Together, these strategies turn scarce
talent into amplified capability. ... Rather than chasing scarce talent,
companies should focus on visibility and context. Most breaches stem from
unknown identities and unchecked access, not zero days. By strengthening
identity governance and access intelligence, organizations can multiply the
impact of small security teams, turning knowledge, not headcount, into their
greatest defense.The Configurable Bank: Low‑Code, AI, and Personalization at Scale
What does the present day modern banking system look like: The answer depends
on where you stand. For customers, Digital banking solutions need to be
instant, invisible, and intuitive – a seamless tap, a scan, a click. For
banks, it’s an ever-evolving race to keep pace with rising expectations. ...
What was once a luxury i.e. speed and dependability – has become the standard.
Yet, behind the sleek mobile apps and fast payments, many banks are still
anchored to quarterly release cycles and manual processes that slow
innovation. To thrive in this landscape, banks don’t need to rip out their
core systems. What they need is configurability – the ability to re-engineer
services to be more agile, composable, and responsive. By making their systems
configurable rather than fixed, banks can launch products faster, adapt
policies in real time, and reduce the cost and complexity of change. ... The
idea of the Configurable Bank is built on this shift – where technology,
powered by low-code and AI, transforms banking into a living, adaptive
platform. One that learns, evolves, and personalizes at scale – not by
replacing the core, but by reimagining how it connects with everything around
it. ... This is not just a technology shift; it’s a strategic one. With
low-code, innovation is no longer the privilege of IT alone. Business teams,
product leaders, and even customer-facing units can now shape and deploy
digital experiences in near real time. Deepfake crisis gets dire prompting new investment, calls for regulation
Kevin Tian, Doppel’s CEO, says that organizations are not prepared for the
flood of AI-generated deception coming at them. “Over the past few months,
what’s gotten significantly better is the ability to do real-time, synchronous
deepfake conversations in an intelligent manner. I can chat with my own
deepfake in real-time. It’s not scripted, it’s dynamic.” Tian tells Fortune
that Doppel’s mission is not to stamp out deepfakes, but “to stop social
engineering attacks, and the malicious use of deepfakes, traditional
impersonations, copycatting, fraud, phishing – you name it.” The firm says its
R&D team has “just scratched the surface” of innovations it plans to bring
to existing and upcoming products, notably in social engineering defense
(SED). The Series C funds will “be used to invest in the core Doppel gang to
meet the exponential surge in demand.” ... Advocating for “laws that
prioritize human dignity and protect democracy,” the piece points to the EU’s
AI Act and Digital Services Act as models, and specifically to new copyright
legislation in Denmark, which bans the creation of deepfakes without a
subject’s consent. In the authors’ words, Denmark’s law would “legally
enshrine the principle that you own you.” ... “The rise of deepfake technology
has shown that voluntary policies have failed; companies will not police
themselves until it becomes too expensive not to do so,” says the piece.
The what, why and how of agentic AI for supply chain management
To be sure, software and automation are nothing new in the supply chain space.
Businesses have long used digital tools to help track inventories, manage fleet
schedules and so on as a way of boosting efficiency and scalability. Agentic AI,
however, goes further than traditional SCM software tools, offering capabilities
that conventional systems lack. For instance, because agents are guided by AI
models, they are capable of identifying novel solutions to challenges they
encounter. Traditional SCM tools can’t do this because they rely on pre-scripted
options and don’t know what to do when they encounter a scenario no one
envisioned beforehand. AI can also automate multiple, interdependent SCM
processes, as I mentioned above. Traditional SCM tools don’t usually do this;
they tend to focus on singular tasks that, although they may involve multiple
steps, are challenging to automate fully because conventional tools can’t reason
their way through unforeseen variables in the way AI agents do. ... Deploying
agents directly into production is enormously risky because it can be
challenging to predict what they’ll do. Instead, begin with a proof of concept
and use it to validate agent features and reliability. Don’t let agents touch
production systems until you’re deeply confident in their abilities. ... For
high-stakes or particularly complex workflows, it’s often wise to keep a human
in the loop.
How AI can magnify your tech debt - and 4 ways to avoid that trap
The survey, conducted in September, involved 123 executives and managers from
large companies. There are high hopes that AI will help cut into and clear up
issues, along with cost reduction. At least 80% expect productivity gains, and
55% anticipate AI will help reduce technical debt. However, the large
segment expecting AI to increase technical debt reflects "real anxiety about
security, legacy integration, and black-box behavior as AI scales across the
stack," the researchers indicated. Top concerns include security vulnerabilities
(59%), legacy integration complexity (50%), and loss of visibility (42%). ...
"Technical debt exists at many different levels of the technology stack," Gary
Hoberman, CEO of Unqork, told ZDNET. "You can have the best 10X engineer or the
best AI model writing the most beautiful, efficient code ever seen, but that
code could still be running on runtimes that are themselves filled with
technical debt and security issues. Or they may also be relying on open-source
libraries that are no longer supported." ... AI presents a new raft of problems
to the tech debt challenge. The rising use of AI-assisted code risks "unintended
consequences, such as runaway maintenance costs and increasing tech debt,"
Hoberman continued. IT is already overwhelmed with current system maintenance.
The State and Current Viability of Real-Time Analytics
Data managers now prefer real-time analytical capabilities built within their
applications and systems, rather than a separate, standalone, or bolted-on
project. Interest in real-time analytics as a standalone effort has dropped
from 50% to 32% during the past 2 years, a recent survey of 259 data managers
conducted by Unisphere Research finds ... So, the question becomes: Are
real-time analytics ubiquitous to the point in which they are automatically
integrated into any and all applications? By now, the use of real-time
analytics should be a “standard operating requirement” for customer experience,
said Srini Srinivasan, founder and CTO at Aerospike. This is where the rubber
meets the road—where “the majority of the advances in real-time applications
have been made in consumer-oriented enterprises,” he added. Along these lines,
the most prominent use cases for real-time analytics include “risk analysis,
fraud detection, recommendation engines, user-based dynamic pricing, dynamic
billing and charging, and customer 360,” Srinivasan continued. “For over a
decade, these systems have been using AI and machine learning [ML], inferencing
for improving the quality of real-time decisions to improve customer experience
at scale. The goal is to ensure that the first customer and the
hundred-millionth customer have the same vitality of customer experience.” ...
“Within industries such as energy, life sciences, and chemicals, the next decade
of real-time analytics will be driven by more autonomous operations,” said
David Streit
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