Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Picture two realities for the same compliance control reaching production. Reality One: Your AppSec team writes a new rule. An engineer uses Claude Code or Cursor to generate the OPA (Open Policy Agent) Rego policy in minutes. They deploy it. It blocks a legitimate release on a missing context variable, and the on-call engineer routes around the gate to ship the code. The AI gave them fast code — but not code they could trust.

How Agentic AI Is Outpacing the Compliance Frameworks Built to Contain It.

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.