Quote for the day:
"Success is the progressive realization of a worthy goal or ideal." -- Earl Nightingale
AI And The End Of Progress? Why Innovation May Be More Fragile Than We Think
“If progress was inevitable, the first industrial revolution would have
happened a lot earlier,” he explained in our recent conversation. “And if
progress was inevitable, most countries around the world would be rich and
prosperous today.” Many societies have seen periods of intense innovation
followed by stagnation or collapse. Ancient cities such as Ephesus once
thrived and then disappeared. The Soviet Union industrialized rapidly but
failed to keep up when the computer era began. ... Artificial intelligence
sits squarely at the center of this fragile transition. Early breakthroughs,
from transformers to generative AI, came from open experimentation in
universities and small labs. ... Many organizations are using AI primarily for
process automation and cost-cutting. Frey believes this will not deliver
transformative growth. “If AI means we do email and spreadsheets a bit more
efficiently and ease the way we book travel, the transformation is not going
to be on par with electricity or the internal combustion engine,” he said.
True prosperity comes from creating new industries and doing previously
inconceivable things. ... “If you want to thrive as a business in the AI
revolution, you need to give people at low levels of the organization more
decision-making autonomy to actually implement the improvements they are
finding for themselves,” he said.Why every manager should have trauma literacy
Trauma literacy is the ability to recognize that unhealed past experiences
show up in daily behavior and to respond in ways that foster safety and
resilience. You don’t need to know someone’s history to be mindful of trauma’s
effects. You just need to assume that trauma exists, and that it may be
shaping how people show up at work. ... Managers are trained in financial
strategy, forecasting, and performance management. But few are trained to
recognize the external manifestations of what I felt back in that tech office:
the racing heart, the sense of dread, and the silent withdrawal. Most workers
are taught to push harder instead of pausing to hold space for emotions.
Emotions are messy, and it often feels safer to stick with technical tasks and
leave feelings unaddressed. ... Once someone shares something vulnerable,
don’t rush to fix it or dismiss it. Just reflect it back: “Thanks for sharing
that, I hear you,” or “That makes a lot of sense.” From there, you might ask,
“Is there anything you need from me today?” or “Would it help to adjust your
workload this week?” ... Trauma literacy isn’t a one-off conversation; it’s a
culture. Build in rituals for reflection, adjust workloads proactively, and
allocate time and resources toward psychological safety. When resilience is
designed into structures, managers don’t have to rely on intuition alone.Botnets are getting smarter and more dangerous
They don’t stop at automation. Natural language processing can be used to
generate convincing phishing emails at scale. Reinforcement learning lets
malware adjust strategies based on firewall responses. Image recognition can
help bots evade visual CAPTCHAs. These capabilities give attackers a terrifying
new playbook, one that relies less on scale and more on sophistication. What
makes this trend especially insidious is that botnets can now be smaller and
stealthier than ever. Instead of infecting millions of devices to overwhelm a
system, an AI-driven botnet might only need a few thousand nodes to carry out
highly targeted, surgical operations. That makes detection harder, attribution
fuzzier and mitigation more complex. ... A compromised software development kit
or node package manager can serve as a delivery mechanism for an AI-powered
botnet, enabling it to infiltrate thousands of businesses in a single attack.
From there, the botnet doesn’t just wait for instructions; it scouts, learns and
adapts. IOT devices remain another massive vulnerability. ... The regulatory
angle is becoming more critical as well. As botnet sophistication grows,
governments and commercial organizations are being forced to reconsider their
cybercrime frameworks. The blurred line between AI research and weaponization is
becoming a legal gray zone. Will training a model to bypass CAPTCHA become
criminalized? What about selling an AI model that can autonomously scan for
zero-day exploits?
From Spend to Strategy: A CISO's View
Company executives view cybersecurity as a core business risk, but CISOs must communicate risk in a similar capacity to other risk functions through heat maps. These heat maps communicate the likelihood of a security incident impacting what matters most to the business - which includes key business capabilities, critical systems and services, and core locations or facilities - and the materiality of such an impact. Using these heat maps, CISOs can and should show the progress made in terms of reducing incident likelihood and impact, the progress expected to be made over the coming reporting period, and gaps that require additional funding to reduce corresponding risks to an acceptable level. From a security spend perspective, this means explaining to leadership how the function will deliver better business outcomes, not only with more budget but also with reallocated funding that can help create better ROI. CISOs must be prepared to answer inbound questions, such as: Haven't we already invested in this? What are you able to deliver with 20% more budget for these new capabilities that you weren't able to deliver before? Staying away from highly technical metrics like vulnerability counts with no direct correlation to business risk must be avoided at all costs. It's about helping executives understand the progress being made and soon to be made, along with gaps tied to reducing risk related to what the business cares about most.The Future of Data Center Security: What Businesses Must Know
Unlike in the past, when cyberattacks mainly targeted networks, today’s hackers
combine online attacks with physical sabotage in what is known as the
“dual-attack model.” For example, while a cybercriminal tries to breach a
network firewall, another may attempt to disable equipment physically inside the
data center building. This coordinated attack can cause far-reaching damage. ...
Alongside security, power management is a top priority. Indian data centers face
rising energy demands. Reports show rack power consumption is climbing steadily,
especially for AI workloads. Mumbai and Hyderabad, leading India’s AI data
center growth, are investing in advanced cooling technologies and reliable
backup energy systems to ensure smooth operations and prevent downtime. Failures
in cooling or power systems can cause major outages that result in millions in
losses. ... Cybersecurity experts also warn that more attacks today are
concealed within encrypted network traffic, bypassing traditional firewalls. To
counter this, Indian data centers are adopting tools that decrypt, inspect, and
then re-encrypt data communications in real time. ... Indian companies must act
decisively to implement next-generation security measures. Those that do will
benefit from uninterrupted operations, stronger compliance, and gain a
competitive edge in an increasingly digital economy.
4 ways to use time to level up your security monitoring
Most security events start small. You notice a few unusual logins, a traffic
spike or abnormal activities in a certain system. Where raw log pipelines add
parsing or enrichment delays before data is ready for analysis, time series
arrives consistently structured and ready for immediate querying. This makes it
easier to establish behavioral baselines and even apply statistical models like
rolling averages and standard deviations to detect anomalies quickly. ...
Detection is only half the battle. Time series systems handle low-latency
ingest, allowing alerts and triggers to be fired in real-time as new data points
arrive. When a device needs to be quarantined, access tokens revoked or an
attacker’s behavior spun up into a forensics workflow to prevent lateral
movement, it can do so in real-time. Because most SaaS log platforms batch and
index events before they are fully queryable, SIEM-driven responses can lag by
minutes, depending on configuration and data volume. Time series systems process
data points in real-time, reducing that lag. ... SIEMs remain indispensable, and
logs are foundational for investigations and compliance. High-precision time
series, continuously ingested and analyzed, enables faster detection, longer
retention and real-time response. All without the cost and performance tradeoffs
of relying on logs alone.The Leadership Style That’s Winning in the AI Era
Technology can generate ideas and reinforce existing thinking, but it cannot
replace authentic human connection. Quiet leaders understand this instinctively:
They build credibility through genuine relationships, not algorithms. These
leaders share a common set of principles and practices that guide how they work
and show up for their teams ... Respect grows when leaders admit their
limitations, take responsibility for mistakes and remain grounded. Employees
appreciate leaders who share when they don’t have all the answers and ask others
to contribute to solutions. This kind of openness increases their credibility
and influence. ... The best leaders treat all conversations as learning
opportunities. A curious leader doesn’t jump to conclusions or cut discussions
short. They ask thoughtful questions and listen actively, signaling to their
teams that their input matters. This kind of curiosity encourages innovation and
creates space for better ideas to surface. ... Rather than seeking credit, quiet
leaders focus on building organizations that thrive beyond any one individual.
They delegate, ensuring that their team can take real ownership of projects and
celebrate success together. ... Leaders who engage in the day-to-day work of the
business gain credibility and insight. Whether it’s walking the production floor
or sitting on customer service calls, this engagement deepens the understanding
of the business, the customer experience and the challenges team members face.
How autonomous businesses succeed by engaging with the world
Autonomous machines are designed from the outside in, while conventional
machines are designed from the inside out. We are witnessing a fundamental shift
in how successful systems are designed, and agentic AI sits at the heart of this
revolution. Today, businesses are being designed more and more to resemble
machines. ... For companies becoming autonomous machines, this outside-in
orientation has profound implications for how they think about customers,
markets, and value creation. Traditional companies are often internally focused.
They design products based on their capabilities, organize around their
processes, and optimize for efficiency. Customers are external entities who
hopefully will want what the company produces. The company's internal logic, its
org chart, processes, and systems become the center of attention, with customers
orbiting around these internal priorities. ... Autonomous companies must be
world-oriented rather than center-oriented. Customers represent the primary
external environment they need to understand and respond to, but they're not a
center to be served; they're part of a dynamic world to be engaged with. Just as
a Tesla can't function without sophisticated environmental sensing, an
autonomous company can't function without a deep, real-time understanding of
customer needs, behaviors, and changing requirements.
Indian factories and automation: The ‘everything bagel’ is here
/dq/media/media_files/2025/10/02/indian-factories-and-automation1-2025-10-02-11-32-01.jpg)
True competitiveness in manufacturing now hinges on integrating automation right
from the design stage and not just on the assembly floor, indicates
Krishnamoorthy. “By connecting CAD environments with robots friendly jigs,
manufacturers can reduce programming times by 30 per cent, speeding up product
launches and boosting agility in responding to market demands.” You can now walk
around a plant inside your computer- thanks to the power of modelling
technology. ... As attractive and revolutionary this advent of automation is,
some holes still remain to be looked into. Like labor replacement, robot taxes,
turbulence in brownfield facilities and accidents due to automation changing so
much in the factories. Dai avers that automation may displace low-skill jobs but
will address labor shortages. As to Robot taxes, they will become a norm in the
long term amid the rise of robotics to balance innovation and social disruption.
“Robotics governance is becoming increasingly critical to ensure security,
privacy, ethics, and regulatory compliance.” He feels. ... “The future of
robotics in manufacturing is about more than efficiency gains—it is about
reshaping industrial culture, building resilience, and redefining global
competitiveness. India, with its rapid adoption and supportive ecosystem, is not
just catching up but positioning itself as a potential leader in this next era
of intelligent manufacturing.” Captures Krishnamoorthy.
Old-school engineering lessons for AI app developers
Models keep getting smarter; apps keep breaking in the same places. The gap
between demo and durable product remains the place where most engineering
happens. How are development teams breaking the impasse? By getting back to
basics. ... When data agents fail, they often fail silently—giving
confident-sounding answers that are wrong, and it can be hard to figure out what
caused the failure.” He emphasizes systematic evaluation and observability for
each step an agent takes, not just end-to-end accuracy. ... The teams that win
treat knowledge as a product. They build structured corpora, sometimes using
agents to lift entities and relations into a lightweight graph. They grade their
RAG systems like a search engine: on freshness, coverage, and hit rate against a
golden set of questions. ... As Valdarrama quips, “Letting AI write all of my
code is like paying a sommelier to drink all of my wine.” In other words, use
the machine to accelerate code you’d be willing to own; don’t outsource
judgment. In practice, this means developers must tighten the loop between
AI-suggested diffs and their CI and enforce tests on any AI-generated changes,
blocking merges on red builds ... And then there’s security, which in the age of
generative AI has taken on a surreal new dimension. The same guardrails we put
on AI-generated code must be applied to user input, because every prompt should
be treated as potentially hostile.
No comments:
Post a Comment