Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The rapid adoption of generative AI has transformed enterprise productivity, but it’s also quietly introduced a new, sophisticated vulnerability: the AI insider threat. For years, securing the internal perimeter meant watching for data exfiltration via USB sticks or unauthorized emails. Today, the risk looks entirely different.

Every year, the Verizon Data Breach Investigations Report (DBIR) is one of the most hotly-anticipated and widely-read documents in security. And every year includes some surprising stats and reshuffles the top few threat vectors. But longtime readers will notice that the 2026 DBIR features some advice that ought to be familiar to everyone by now: get the basics right.

Your source code passed every scan. Every code review approved. Every linter ran clean. Your users just downloaded the compiled binary. Those are not the same artifact. Code-centric SAST tools analyze the code you write. Appknox analyzes what you ship. This is not a feature distinction. It is an architectural one, with direct consequences for what gets caught and what does not.

AI agents can investigate a single vulnerability brilliantly, but that is only about 20% of vulnerability remediation. This post breaks down the other 80%: the data normalization, cross-tool asset identity, SLA enforcement, exception governance, and audit evidence that turn individual agent outputs into a governed, provable remediation program, and why AI and a platform like Seemplicity work better together than apart.

Globally, companies are still preparing for a quantum revolution, and the National Institute of Standards and Technology (NIST) has made significant progress on its Post-Quantum Cryptography (PQC) Standardization Program.

Protecting minors online has become one of the most pressing, and complex, policy discussions in today’s digital landscape. As technology evolves, so too does the urgency to create safer digital environments. Regulators, platforms, and security leaders all share that objective. However, the way we attempt to achieve it is entering a new and far more intricate phase.

Attack Surface Monitoring has become a critical component of modern cybersecurity programs. As organizations scale their cloud environments, applications, APIs, and third-party services, so does their external attack surface. Every new cloud instance, API endpoint, marketing microsite, and third-party SaaS tool expands your perimeter. But there are two hard truths for security teams: You cannot protect what you don’t know exists, and you cannot secure what you don’t deeply test.

Modern IT and security teams no longer evaluate platforms in isolation. They ask how a platform fits into the architecture they run, the workflows they trust, and the outcomes they need to improve. Enterprise stacks are not isolated; they are interdependent. Identity shapes access, endpoint posture influences policy, while SIEM tools drive investigations and rely on shared data and context. AI tools introduce new layers and patterns of usage, risk, and data movement across the network.

A CVE lands in the morning. Hours later, attackers are exploiting it in the wild. The patch is not ready, the change window is days away, and the clock is already running. None of this is new. What changed is that vulnerability exploitation is now the most common path into organizations.