Cybersecurity Insights & Expert Analysis
‍

A crosswalk is not evidence. What artifact actually satisfies each NIST AI RMF function, which subcategories a penetration test answers, and why aligned, certified and tested are three different states.

The methodology behind $5,000+ in XSS bounties and 80+ enterprise pentests, plus 11 hands-on reflected XSS labs covering reflection contexts, filter bypasses, and hidden-parameter discovery.

Most AI risk registers collapse “untested” and “tested and failing” into the same amber, which inverts the priority a defender should have. Why a proven failure is a bounded, demonstrated path, why an unknown still deserves respect, and how a state field fixes the ranking.

Agent and service identities now outnumber humans in most environments, and almost none go through joiner-mover-leaver. Six tests for non-human identity scope, credential lifecycle, and privilege propagation across an agent-to-tool chain.

The OWASP Top 10 for LLM Applications was reranked for 2026 — Excessive Agency jumped to #3 and System Prompt Leakage was renamed. Here's the current list, what changed, and how ioSENTRIX tests against it.

Change one ID and the server returns someone else's data. A penetration tester's field guide to finding IDOR behind predictable IDs and UUIDs — and the server-side fix that actually holds.

Most shadow AI was never bought. It was switched on inside software you already approved — invisible to policy lists, procurement, and the proxy. Four reasons it escapes your inventory, and the three questions that find it.

With an authorized-testing context, capable open models answered our offensive-security questions with zero refusals — and still gave the wrong Hashcat mode for Kerberoasting. Why compliant is not correct, and the three axes a security-AI benchmark must measure separately.

Boards ask four questions about AI agents, and the honest answer is usually “we believe so.” Here is how each one maps to a concrete assessment domain — what gets tested, what the tester does, and what evidence comes back.

Governance asks whether a control exists. Assurance proves it works. Why an AI security assessment built on questionnaires tells you almost nothing, and what evidence looks like.

Your AI policy names a tool, but your exposure is a data path. Five ways customer data reaches a model anyway, and how to test each one with canary data.

Autonomy makes integrity the hard problem. Why proving an AI action is genuine, authorized, and untampered now matters more than controlling access, and how to test for it.