Quote for the day:
"Go confidently in the direction of your dreams. Live the life you have imagined." -– Henry David Thoreau
Why Observability Needs To Go Headless
Not all logs have long-term value, but that’s one of the advantages of headless
observability and decoupled storage. Teams have the freedom and flexibility to
determine which logs should be retained for longer periods. Web application
firewall (WAF) and other security logs can be retained over the long term and
made available to cybersecurity teams and threat hunters. Other application logs
can provide long-term insights into how resources are being used for capacity
planning and anomaly detection. Let’s take a closer look at a real, tangible use
case where observability data can be valuable for other teams: real user
monitoring (RUM). In the realm of observability, RUM allows teams to proactively
monitor how end users are experiencing web applications. Issues like slow page
loads can be mitigated before they frustrate users. Beyond observability, RUM
data can also provide insights into how your end users are interacting with your
brand and your products. This data is invaluable for marketing, advertising and
leadership teams that need to plan strategy. ... As a real-world example, many
enterprises use CDN log data for real user monitoring. In the short term,
monitoring CDNs is important for ensuring good user experiences and fast loading
times of digital assets. However, being able to retain huge volumes of log data
long term and cost-effectively provides certain advantages to enterprises.
Why the CIO role should be split in two
The fact is that within enterprises, existing architecture is overly complex,
often including new digital systems interconnected with legacy systems. This
‘hybrid’ architecture is a combination of best and bad practice. When there is
an outage, the new digital platforms can invariably be restored to recover
business process support. But because they do not operate in isolation, instead
connecting with legacy technologies, business operations themselves may not
fully recover if the legacy systems continue to be impacted by the outage. For
most enterprises stuck in this hybrid state, the way forward is to be more
discipline around architecture. ... Simplifying architecture at an enterprise
level is something the CIO and CISO should work together concurrently as a
shared goal. The benefits of doing so will accrue over time rather than
immediately, hence there can be some reluctance to prioritize. ... What does all
this have to do with my opening discussion about the CIO and complementary IT
executive roles? Splitting the CIO role into smaller and smaller pieces would be
okay if doing so led to better outcomes. But I would argue that examples like
the ones above show that the multiple-exec approach is not a success story we
should be bragging about. In this structure, the two CIOs would share ownership
of the IT strategy.
Generative AI vs. the software developer
AI is not going to turn your customer support people (Elvis bless them) into
senior software developers. A customer support person might be able to think “I
need to track the connection between items in inventory, the customer’s shopping
cart, and the discount pricing for a given item,” but unless that person also
knows how to code, they will have a seriously hard time instructing an AI model
to generate the code they need. Most likely, they aren’t going to know if the
code the AI produces even runs, let alone works correctly. But AI can help
actual developers in many ways. It can look at existing code you have written
and help you produce the next thing that you need to write. It can even write
large routines and classes that you ask it to. But it is not going to create the
things you need without you having a large say in what that is. You need to know
how to craft a prompt to get precisely what is needed. ... Now, that prompt will
be pretty effective in getting what is asked for. But the trick here, obviously,
is that you have to know what a React component is, what Tailwind is, the fact
that you want tests, what TypeScript is, what null is, and that you’d even need
to handle missing values. There is a lot of knowledge and experience wrapped up
in that prompt, and it’s not something that an inexperienced developer, or
certainly a non-developer, would be able to write.
Beyond the Screen: Humanising Digital Learning
Digital learning holds a lot of promise, aiming to bring the most dynamic and
engaging elements of in-person training into the digital space. Interactive
tools like quizzes, breakout rooms, and mini-tasks demonstrate just how far
we’ve come in replicating real-world engagement online. However, we continue to
see issues with retention and follow through. Recent research shows that 66% of
employees still find on-the-job learning to be more effective than formal online
courses. This disconnect often stems from a lack of deep, meaningful engagement.
Without it, employees are less likely to retain knowledge or apply their skills
effectively in the workplace. This is particularly crucial when it comes to
human skills—broader soft skills like communication, emotional intelligence, and
critical thinking. Unlike technical skills that are typically learned ‘by the
book’, softer skills are learned and applied every day. The solution lies in
moving beyond passive consumption to real-world, interactive learning
simulations. ... The shift to digital learning offers incredible potential, but
realising that potential requires a thoughtful approach. By embracing AI-powered
technologies and prioritising interactive, personalised and bite-sized content,
organisations can create learning experiences that are engaging, practical and
transformative.
Shadow AI: How unapproved AI apps are compromising security, and what you can do about it
Shadow AI introduces significant risks, including accidental data breaches,
compliance violations and reputational damage. It’s the digital steroid that
allows those using it to get more detailed work done in less time, often beating
deadlines. Entire departments have shadow AI apps they use to squeeze more
productivity into fewer hours. “I see this every week,” Vineet Arora, CTO at
WinWire, recently told VentureBeat. “Departments jump on unsanctioned AI
solutions because the immediate benefits are too tempting to ignore.” ... “If
you paste source code or financial data, it effectively lives inside that
model,” Golan warned. Arora and Golan find companies training public models
defaulting to using shadow AI apps for a wide variety of complex tasks. Once
proprietary data gets into a public-domain model, more significant challenges
begin for any organization. It’s especially challenging for publicly held
organizations that often have significant compliance and regulatory
requirements. Golan pointed to the coming EU AI Act, which “could dwarf even the
GDPR in fines,” and warns that regulated sectors in the U.S. risk penalties if
private data flows into unapproved AI tools. There’s also the risk of runtime
vulnerabilities and prompt injection attacks that traditional endpoint security
and data loss prevention (DLP) systems and platforms aren’t designed to detect
and stop.
Think being CISO of a cybersecurity vendor is easy? Think again
When people in this industry hear that a CISO is working at a cybersecurity
vendor, it can trigger a number of assumptions — many of them misguided. There’s
a stereotype that the role isn’t “real” CISO work, that it’s more akin to being
a field CISO, someone primarily outward-facing and focused on supporting sales
or amplifying the brand. The assumption goes something like this: How hard can
it be to secure a security company, and isn’t the “real” work done at companies
outside of this bubble? ... Some might think that working at a security company
limits your perspective of what’s out there in the broader industry, but I found
the opposite to be true. I gained a deeper understanding of how organizations
evaluate security solutions and what they truly care about. I saw firsthand the
challenges customers faced when implementing security tools, and that experience
gave me empathy, insight, and a renewed ability to speak their language. Now
that I’m back in industry, I’m bringing that perspective with me. The transition
wasn’t a step “down” or a shift away from anything; it was just the next phase
in my career. Security leadership is security leadership, no matter where you
practice it. The challenges remain complex, the responsibilities remain vast,
and the importance of aligning security with business outcomes remains
paramount.
Lack of regulations, oversight in health care IT can cause harm
Increasingly, health care organizations have outsourced their health IT
infrastructure to companies owned and operated by private equity, venture
capital and Big Tech firms that view them as platforms to experiment with
unproven AI and machine-learning tools. "The unregulated integration of AI tools
into these systems will make it even harder to protect patients' rights,"
Appelbaum said. "Moreover, because these records contain so much information and
are centralized, they are among the most lucrative targets for cyberattacks and
hackers," Batt said, noting that in 2024, data breaches exposed the health
records of more than 200 million Americans. As a result, health care
organizations must now invest billions more in cybersecurity systems owned and
operated by venture capital, private equity and Big Tech. The authors argue that
the federal government is once again behind in setting safeguards for the
adoption of new health IT, and that the lessons from 30 years of attempts to set
adequate standards for information-sharing in electronic health systems—as
detailed in these reports—should spur regulators to act quickly and rein in
unregulated financial activities in health IT. Batt explained, "The history of
the health IT implementation and the lack of sufficient regulatory oversight and
enforcement of standards should give us great pause for the current enthusiasm
over the adoption of AI and machine learning in health information systems."
The Future of Data: How Decision Intelligence is Revolutionizing Data
Decision Intelligence is an interdisciplinary field that uses AI to enhance all
aspects of decision-making across all areas of a Business. It blends concepts of
Data Science (statistics, machine learning, AI, analytics) with Behavioral
Sciences (psychology, neuroscience, economics, and managerial sciences) to
understand how decisions are made and how outcomes are measured. ... Decision
Intelligence (DI) can be considered a subset where it uses AI to build a
reliable data foundation by collecting, organizing, and connecting data and then
applying AI and analytics to turn that data into useful insights for better
decision-making. In short, while AI provides the technology to mimic human
intelligence, DI focuses on applying that technology to improve how decisions
are made. ... You can use any of your machine learning models, like regression
models, classification models, time series forecasting models, clustering
algorithms, or reinforcement learning for implementing Decision Intelligence.
These machine learning will help identify patterns in the data and make
predictions based on those patterns, but decision intelligence will take that
information one step further by incorporating it into a broader framework that
can actively guide the decision-making process by considering the predictions
and the potential outcomes and consequences of different choices.
ManpowerGroup exec explains how to manage an AI workforce
It’s not just a technology anymore. We are looking for individuals that have the
industry experience. We can take somebody with industry experience and train
them on the technical part of the job. “It’s a lot harder for us to take
somebody with the technical skills and teach them how the industry works. I
think there’s a focus on looking at the soft skills: the problem solving, the
complex reasoning ability, and communications. Because it’s not just developing
AI for the sake of software technology; it’s to address that larger business
problem. It’s about looking at all of the business functions, and taking all of
that into consideration. ... The problem is [that] the gap is getting wider
between those employees who understand AI technology and are willing to learn
more about it and those who don’t want to have anything to do with it. But I
think everybody will be a technologist, eventually. It’s going to be talent
augmented by technology. ... “There are so many things, and it’s happening so
fast. So, we are still learning as fast as we can. We’re trying to understand
what the impact of AI will be, and how it will change our business models. Even
from a talent organization like ours, which is providing global talent
solutions, what does that do for us? Now, our company is going to start looking
for your talent plus the AI agents you’ll need. So AI becomes part of a hiring
solution.
Debunking the AI Hype: Inside Real Hacker Tactics
While headlines are trumpeting AI as the one-size-fits-all new secret weapon for
cybercriminals, the statistics—again, so far—are telling a very different story.
In fact, after poring over the data, Picus Labs found no meaningful upswing in
AI-based tactics in 2024. Yes, adversaries have started incorporating AI for
efficiency gains, such as crafting more credible phishing emails or creating/
debugging malicious code, but they haven't yet tapped AI's transformational
power in the vast majority of their attacks so far. In fact, the data from the
Red Report 2025 shows that you can still thwart the majority of attacks by
focusing on tried-and-true TTPs. ... Attackers are increasingly targeting
password stores, browser-stored credentials, and cached logins, leveraging
stolen keys to escalate privileges and spread within networks. This threefold
jump underscores the urgent need for ongoing and robust credential management
combined with proactive threat detection. Modern infostealer malware
orchestrates multi-stage style heists blending stealth, automation, and
persistence. With legitimate processes cloaking malicious operations and actual
day-to-day network traffic hiding nefarious data uploads, bad actors can
exfiltrate data right under your security team's proverbial nose, no
Hollywood-style "smash-and-grab" needed. Think of it as the digital equivalent
of a perfectly choreographed burglary.
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