Quote for the day:
"Successful leadership requires positive self-regard fused with optimism about a desired outcome." -- Warren Bennis
Forget the chief AI officer - why your business needs this 'magician
There's a lot of debate about who should be responsible for ensuring the
business makes the most out of generative AI. Some experts suggest the CIO
should oversee this crucial role, while others believe the responsibility should
lie with a chief data officer. Beyond these existing roles, other experts
champion the chief AI officer (CAIO), a newcomer to the C-suite who oversees key
considerations, including governance, security, and identification of potential
use cases. ... Many people across other business units are confused about the
different roles of technology and data teams. When Panayi joined Howden in
August last year, he decided to head off that issue at the pass. ... "I think
companies are missing a trick if they've not got someone ensuring that people
are using things like Copilot and so on. These tools are new enough that we do
need people to help with adoption," he said. "And at the moment, I don't think
we can assume the narrative is correct that people using AI at home to help them
book holidays is the same as how it can help them be more productive at work."
... "It's like he's a magician, showing people who have to deal with thousands
of pages of stuff, how to get the answers they need quickly," he said, outlining
how the director of productivity highlights the benefits of gen AI to the firm's
brokers. "These people are not at the computer all day. They are out in the
market, talking and making decisions."Just Relying on Data Doesn’t Make You Data-driven — Advantage Solutions CDO
O’Hazo then draws a line between measurement and transformation. Success in data programs, she explains, is not only about performance indicators; it is also about whether the organization is starting to internalize the mindset behind them. “Success for me in this data and AI space is all about, ‘Are my stakeholders starting to actually speak some of my language?’” When stakeholders begin to “believe” and “trust,” she says, the shift becomes visible not only in outcomes but also in demand. The moment data starts becoming embedded in the business is the moment the need for the CDO office outgrows its capacity. ... She ties true data-driven maturity to operational efficiency and responsiveness: Accurate, timely information; Faster decision-making cycles; Quicker reactions to market conditions; and Lower effort to extract value from data. In her view, strong data foundations should reduce friction instead of creating new burdens. Speed, however, is not just about moving fast, it’s about winning the race to insight. “Once you have that foundation built, to get to the answer quickly, you have to be the first one there. If you’re not the first one there, you’ve lost.” ... As the conversation returns to the governance part of transformation, O’Hazo underscores that governance becomes sustainable only when people are comfortable using data and confident enough to surface risks early. For her, the true differentiator is not policy; it is talent and environment.The Three Mindsets That Shape Your Life, Work And Fulfillment
Mission Mindset is goal-oriented but not outcome-obsessed. It begins with
clarity about a specific, measurable and time-bound goal. Decades of research on
goal-setting, including the work of Stanford psychologist Carol Dweck, shows
that how we interpret challenges influences how we engage with them—and that
mindset creates very different psychological worlds for people facing the same
obstacles. Here's where most people go wrong. ... If mission provides direction,
identity provides stability. Identity Mindset is rooted in a healthy, coherent
self-image that does not rise and fall with every outcome. It answers a deeper
question: Who am I when the going gets tough or disappointment abounds? Many
people identify with their performance. Success feels like validation, and
failure feels personal. That volatility makes progress emotionally expensive
because every result threatens their self-worth. In contrast, PsychCentral
broadly defines resilience as adapting well to adversity; individuals who are
stable in how they see themselves are better able to regulate emotions, process
setbacks and continue forward without losing themselves in the struggle. ...
Agency Mindset is where actual momentum lives. It is the lived belief that you
are the author of your life, not a character reacting to circumstances. Agency
does not deny reality or minimize hardship. It refuses to play the victim, make
excuses or place blame. Why We Can’t Let AI Take the Wheel of Cyber Defense
When we talk about fully autonomous systems, we are talking about a loop: the AI
takes in data, makes a decision, generates an output, and then immediately
consumes that output to make the next decision. The entire chain relies heavily
on the quality and integrity of that initial data. The problem is that very few
organizations can guarantee their data is perfect from start to finish. Supply
chains are messy and chaotic. We lose track of where data originated. Models
drift away from accuracy over time. If you take human oversight out of that
loop, you aren’t building a better system; you are creating a single point of
systemic failure and disguising it as sophistication. ... There is no magical
self-healing feature that puts everything back together elegantly. When a breach
happens, it is people who rebuild. Engineers are the ones trying to deal with
the damage and restoring services. Incident commanders are the ones making the
tough calls based on imperfect information. AI can and absolutely should support
those teams—it’s great at surfacing weak signals, prioritizing the flood of
alerts, or suggesting possible actions. But the idea that AI will independently
put the pieces back together after a major attack is a fantasy. ... So, how do
we actually do this? First, make “human-in-the-loop” the default setting for any
AI that can act on your systems or data. Automated containment can save your
skin in the first few seconds of an attack, but every autonomous process needs
guardrails. Connecting the dots on the ‘attachment economy’
In the attention economy paradigm, human attention is a currency with monetary
value that people “spend.” The more a company like Meta can get people to
“spend” their attention on Instagram or Facebook, the more successful that
company will be. ... Tristan Harris at the Center for Humane Technology coined
the phrase “attachment economy,” which he criticizes as the “next evolution” of
the extractive-tech model; that’s where companies use advanced
technologies to commodify the human capacity to form attached bonds with
other people and pets. In August, the idea began to gain traction in business
and academic circles with a London School of Economics and Political Science
blog post entitled, “Humans emotionally dependent on AI? Welcome to the
attachment economy” by Dr. Aurélie Jean and Dr. Mark Esposito. ... The rise of
attachment-forming tech is similar to the rise in subscriptions. While posting
an article or YouTube video may get attention, getting people to subscribe to a
channel or newsletter is better. It’s “sticky,” assuring not only attention now,
but attention in the future as well. Likewise, the attachment economy is
the “sticky” version of the attention economy. Unlike content subscription
models, the attachment idea causes real harm. It threatens genuine human
connection by providing an easier alternative, fostering addictive emotional
dependencies on AI, and exploiting the vulnerabilities of people with mental
health issues. From monitoring blind spots to autonomous action: Rethinking observability in an Agentic AI world
AI-supported observability tools help teams not only understand system
performance but also uncover the reasons behind issues. By linking signals
across interconnected parts, these tools provide actionable insights and
usually resolve problems automatically, reducing Mean Time to Resolution
(MTTR) and cutting the risk of outages. ... AI-driven observability can trace
service dependencies from start to finish, connect signals across third-party
platforms, and spot early signs of unusual behavior. By examining traffic
patterns, error rates, and configuration changes in real-time, observability
helps teams identify emerging issues sooner, understand the potential impact
quickly, and respond before full disruptions occur. While observability cannot
prevent every third-party outage, it can greatly reduce uncertainty and
response time, allowing solutions to be introduced sooner and helping rebuild
customer trust. ... When AI-driven applications fail, teams often lack clear
visibility into what went wrong, putting significant AI investments at risk.
Slow or incorrect responses turn troubleshooting into guesswork, as teams
struggle to understand agent interactions, find delays, or identify the
responsible agent or tool. This lack of clarity slows down root-cause
analysis, extends downtime, diverts engineering efforts from innovation, and
can ultimately lead to lost revenue and customer trust. Observability
addresses this challenge by providing complete visibility into AI application
behavior.
Architecture Testing in the Age of Agentic AI: Why It Matters Now More Than Ever
Historically, architecture testing functioned as a safeguard against emergent
complexity in distributed systems. Whenever an organization deployed a network
of interdependent services, message buses, caches, and APIs, the potential for
unforeseen interactions grew. Even before AI entered the picture, architects
confronted the reality that large systems behave in ways no single engineer
fully anticipates. ... Agentic systems challenge traditional testing practices
in several fundamental ways. First, these systems are inherently
non‑deterministic. A test that succeeds at 9:00 might fail just minutes later
simply because the agent followed a different reasoning path. This creates a
widening ‘verification gap,’ where deterministic enterprise systems and
probabilistic, adaptive agents operate according to fundamentally different
reliability expectations. Second, these agents operate within environments that
are constantly shifting—APIs, user interfaces, databases, and document stores
all evolve independently of the agent itself. Because agents are expected to
detect these changes and adapt their behavior, long‑held architectural
assumptions about stability and interface contracts become far more fragile.
... Third, agentic AI introduces a new level of emergent behavior.
Operating through multi‑step reasoning loops and tool interactions, agents can
develop strategies or intermediate actions that were never explicitly designed
or anticipated. While emergence has always existed in complex distributed
systems, with agents it becomes the rule rather than the exception.
Data Privacy Day warns AI, cloud outpacing governance
Kornfeld commented, "Data Privacy Day is a reminder that protecting sensitive
information requires consistent discipline, not just policies. This discipline
starts with infrastructure choices. As organizations continue to evaluate
cloud-first strategies, many are also reassessing where their most critical data
should live. For workloads that demand predictable performance, strong
governance and clear ownership, on-site infrastructure continues to play an
essential role in a sound privacy strategy." ... Russel said, "Data Privacy Day
often prompts the usual reminders: update policies, refresh consent language,
and train staff on security and resilience strategies. These are important
steps, but increasingly they are simply the baseline. In 2026, the board-level
question leaders should also be asking is: can we demonstrate control of
personal data and sustain trust through disruption, whether it stems from a
compromise, misconfiguration, insider error, or a supplier incident?" ...
Russell commented that identity controls and response processes sit at the core
of this shift as attackers continue to exploit account compromise to reach
sensitive information in cloud environments. "Identity is a privacy fault line.
In cloud environments, compromised identities are often the fastest route to
sensitive data. Resilience means detecting abnormal access early, limiting blast
radius, and recovering confidently when identity controls are bypassed."Security teams are carrying more tools with less confidence
Security leaders express mixed views about the performance of their SIEM
platforms. Most say their SIEM contributes to faster detection and response, yet
only half describe that contribution as strong. Confidence in long-term
scalability follows a similar pattern, with many teams expressing partial
confidence as data volumes and monitoring demands continue to grow. Satisfaction
with log management and security analytics tools mirrors this split. Teams that
express higher satisfaction also report stronger alignment between their tooling
and application environments. ... Threat detection represents the most common
use of AI and machine learning within security operations. Fewer teams apply AI
to incident triage, automated response, or anomaly detection. Despite this
limited scope, security leaders consistently associate AI with reduced alert
fatigue and improved signal quality. Many also prioritize AI capabilities when
evaluating SIEM platforms, alongside real-time analytics. ... Security leaders
frequently describe operational cost as a top pain point. Multiple point
solutions contribute to overlapping capabilities, siloed data, and increased
alert noise. Data that remains isolated across tools complicates threat analysis
and slows investigations, particularly when teams attempt to reconstruct
activity across cloud, identity, and application layers.
No comments:
Post a Comment