Quote for the day:
"Every great story on the planet happened when someone decided not to give up, but kept going no matter what." -- Spryte Loriano
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Why AI Confidence Is Not Validation
Artificial intelligence tools are rapidly becoming integrated into digital
forensics and incident response, but these systems are growing confident in
their outputs faster than human operators can verify them. Without proper
validation standards, unverified AI findings can end up in critical legal or
forensic reports. To address this, SANS introduced two specialized AI frameworks
mapped to industry standards, emphasizing that while AI can speed up
investigations, a human examiner must remain the final authority. An AI tool
that provides a confident answer without showing its underlying reasoning or
sources is essentially unreliable. Practitioners cannot blindly trust an AI
claim just because a high confidence score is attached; they must independently
confirm the source data. Furthermore, different disciplines require entirely
different policies. Incident response teams prioritize speed to detect and
contain threats, whereas digital forensics demands strict validation of every
file, often making AI use more labor-intensive. Ultimately, the effectiveness of
AI depends heavily on the foundational skills of the human practitioner. AI
should never automatically execute actions that cannot be reversed, such as
collecting evidence or accessing cloud accounts without explicit authority. An
inexperienced person relying solely on AI presents a significant risk to the
integrity of any investigation.CISO Conversations: James Robinson – Many Jobs, One Journey to Netskope CISO
James Robinson’s path to becoming the Chief Information Security Officer at Netskope reveals that a successful career in cybersecurity does not always follow a traditional or planned route. Initially focused more on sports than academics, Robinson discovered his passion for networking through a vocational program while in high school. This early exposure led him to acquire advanced certifications and eventually land at State Farm Insurance. When the dot-com crash disrupted his plans to join Cisco, he pivoted into the emerging field of information security, moving through roles at several major organizations and continually advancing his expertise. Throughout his journey, Robinson has relied heavily on the guidance of mentors and emphasizes the importance of building strong professional relationships. He believes that continuous learning, genuine curiosity, and enjoying the process are far more important than just reaching a specific destination or focusing solely on salary. As a leader, his primary focus is on cultivating a supportive team culture and ensuring his team members feel recognized and connected. When hiring, he values persistence, dedication, and the right mindset over formal degrees, proving that resilience and a willingness to put in the hard work are the true cornerstones of security leadership.No click required: How the ad industry became a pipeline for mercenary spyware
Researchers from The Citizen Lab have discovered that the online advertising
ecosystem is being used to deliver zero-click spyware to targeted individuals
without requiring them to click on anything. A company known as Discover-Tech
successfully posed as a legitimate advertising vendor, gaining approval from
major ad networks like Google and IAB Europe. By participating in real-time
automated ad auctions, the company accessed detailed user data, such as physical
locations and device identifiers, allowing them to precisely profile millions of
people worldwide. When Discover-Tech won a bid to display an ad, it mostly
delivered harmless banners, which allowed it to pass routine security
verification scanners. However, for specific targets, such as journalists at Al
Jazeera, the ad silently delivered malicious code in the background. Because the
ad auction system automatically shares user data to determine targeting, bad
actors can track individuals across various networks undetected. To defend
against this invisible threat, security experts advise organizations to
implement strict ad-blocking policies at the network level rather than relying
on users themselves. They also strongly recommend disabling mobile advertising
identifiers on corporate devices and treating all ad networks as untrusted
vendors, since every digital ad essentially runs external code on a user's
device.
Governance Is Failing: Why Converged Digital Risk Is Outpacing Every Control We Have
Organizations today are struggling to manage modern digital threats because their governance models are outdated. Traditional structures treat cybersecurity, privacy, artificial intelligence, and compliance as separate issues managed by different departments. However, modern risks no longer stay in these neat categories. A single incident, such as a compromised identity, can instantly trigger a massive chain reaction that causes data leaks, disrupts supply chains, corrupts artificial intelligence systems, and results in severe regulatory and reputational damage. This interconnected system of threats is known as Converged Digital Risk, and it moves much faster than isolated governance frameworks can handle. To solve this, businesses must stop treating risk in silos and start viewing it as a unified system. This means shifting focus from merely protecting internal assets to understanding the massive dependencies organizations have on external tools, like cloud providers and third-party models. Ultimately, managing this complexity might require the creation of a new executive role—a Digital Risk Officer—who can oversee the entire landscape, ensure unified visibility, and make rapid decisions when multiple risk areas collide. The organizations that thrive will be those that prioritize system-wide resilience and act quickly before failures cascade.AI Slop Isn't Just for Social Media; It's Ruining Cyberattacks, Too
Despite the alarmist headlines claiming artificial intelligence is supercharging
cyber threats, security researchers are finding that AI is actually making many
attackers worse at their jobs. While threat actors are using AI to scale their
operations and generate content, the result is often low quality output that
exposes their campaigns. Previously sophisticated cybercrime groups and even
state funded operations are adopting AI to write code, design phishing lures,
and create malware, but these efforts frequently backfire. The attacks are
riddled with obvious mistakes, such as broken QR codes, blank emails, mismatched
languages, and logical errors in malware that prevent it from functioning
properly. Some groups are even exposing their own infrastructure by accidentally
leaving their AI generated control panels open to researchers. Ultimately, top
tier espionage and hacking operations avoid relying heavily on AI because it is
too noisy and unpredictable, which dramatically increases their chances of being
caught. Instead of a new era of highly advanced artificial intelligence
breaches, the industry is mostly seeing a flood of sloppy, poorly executed
attacks. The consensus among researchers is that the most dangerous and
effective cyber threats still rely on traditional methods, making the current
narrative around automated attacks largely exaggerated and unsupported by real
world data.
IT reinvention requires mastering the art of translation
Technology leaders must act as translators who can clearly explain how technical systems directly benefit their companies. Rather than discussing complicated cloud architecture or artificial intelligence setups, a successful executive links technological investments to business outcomes like profitability, risk reduction, and an improved experience for customers. This approach is evident in the transformation of Deluxe Corporation, a business that successfully transitioned from a legacy check manufacturer into a modern digital payments and data company. By framing its strategy around how technology powers profitable growth, the company aligned its entire operation with clear goals like building modern platforms, using artificial intelligence responsibly, and strengthening security. True organizational reinvention requires completely changing processes, talent, and mindsets rather than just adding digital tools to old workflows. Leaders must also learn to separate practical signals from the endless noise surrounding emerging trends. Instead of chasing every new technological development, they should focus on core business functions that rarely change while tracking how shifts in areas like cybersecurity, agentic commerce, and employee roles affect those functions. By continuously learning, challenging their own assumptions, and pressure-testing ideas with peers, technology leaders can guide their organizations through continuous reinvention with confidence.How Should Security Be Architected for Human Error?
When architecting enterprise cybersecurity, organizations must acknowledge that
human error is inevitable and stop building programs that expect perfect
behavior. Attackers actively manipulate employees into making mistakes, so
relying solely on human judgment or even automated systems acting alone is
insufficient. Instead, security programs should blend human oversight with
automation, allowing each to audit the other. Security should be treated as an
operational capability focused on resilience rather than a mere product feature.
Instead of adopting a heavy-handed approach that completely blocks access until
users complain, organizations need intelligent "default deny" or allow-listing
strategies that preserve usability. Assuming that a malicious link will
eventually be clicked, the focus must shift to minimizing the blast radius. This
requires aggressive segmentation, strict minimization of privileges, and inline
rate limits that act as "speed bumps." For example, systems should flag and
pause actions that fall outside the norm, like a user attempting to delete
millions of database rows or send tens of thousands of emails simultaneously.
Ultimately, companies should routinely practice for failures through tabletop
exercises and chaos engineering. By putting guardrails in place and testing
recovery protocols before prevention fails, businesses can ensure that a single
human mistake doesn't escalate into a massive crisis.
8 insights from LogicMonitor’s Elevate event: AI-driven observability moves toward autonomous IT
At the recent LogicMonitor Elevate event, the focus was on how artificial intelligence is shifting IT monitoring from passive observation to active, autonomous problem-solving. As business infrastructure becomes more complex, traditional alerts and dashboards are no longer enough. To manage this, LogicMonitor is emphasizing AI-driven observability that combines performance data with automated actions and strict governance. The results are significant: GlobalFoundries, for example, used LogicMonitor’s Edwin AI to reduce distracting alert noise by 98% in just six months, allowing them to spot potential failures in their semiconductor manufacturing before they disrupt production. A central theme of the event was the need to build trust in these automated systems. Before organizations let artificial intelligence resolve issues automatically, they need to know the AI operates within strict rules and understands the context of the business. By connecting operational data with clear guardrails, LogicMonitor aims to help IT teams resolve incidents faster and reduce the number of tools they need to manage. Ultimately, the goal is to free up engineering teams to focus on higher-value work, moving operations from simply fixing problems to actively preventing them.Why Chief Data Officers Need to Go Beyond Data Catalogs to Deliver Value
Chief Data Officers are finding that traditional data catalogs are no longer
enough to meet the demands of modern business, especially as artificial
intelligence adoption increases. While companies collect vast amounts of data,
it often remains locked in departmental silos or stuck in technical tools that
everyday employees cannot easily use. This lack of accessible, high-quality
information not only frustrates staff but also limits the effectiveness of
artificial intelligence models, which require reliable inputs to function
properly. To solve this, organizations must shift toward creating data
products—packaged, business-focused assets designed for specific needs. However,
these products only deliver value if people can easily find and use them. This
is driving the adoption of data marketplaces, which function much like
e-commerce storefronts, allowing non-technical users to search for, understand,
and access reliable information through intuitive interfaces. A successful
transition requires more than just new technology; companies must foster a
culture of data literacy and trust by ensuring the experience is straightforward
and the initial offerings are highly relevant. By moving past reactive data
catalogs and embracing user-friendly marketplaces, businesses can effectively
share information across teams and artificial intelligence systems, turning
their data into tangible, long-term value.
