Cybersecurity Insights & Expert Analysis
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We self-hosted an open model for an autonomous pentest agent and it passed 63% of a 295-test compatibility suite. Moved to a managed serving path, the same model family hit 99%. The model was never the problem — the serving stack was.

“Test the customer-facing AI assistant” is one sentence, and the whole engagement will be argued from it. The seven surfaces an AI penetration test scope must name, what belongs in the rules of engagement, and what a finding must carry.

Two firms quote the same AI system and the numbers differ fourfold. Both are honest. The seven scope decisions that set the price of an AI penetration test, and the three questions that separate a scoped proposal from a priced guess.

Enterprise buyers have moved from attestation to evidence on AI. The premium does not show up as price — it shows up as sales cycle length, contract terms, and the deals you are never invited into. Four decisions for a board.

Canary data is easy to describe and easy to get wrong. A practitioner guide to designing canary strings that survive normalization and truncation, placing them without polluting production, building the detection pipeline, and knowing why a clean result usually means a broken test.

Your test report names one model. Your gateway can reach six. Routers and cascades make the execution path probabilistic — what that does to a test plan, and how to sample representative paths honestly.

Your AI inventory survives "what are we running?" and fails "how do you know that's all of it?" Six discovery signals, the blind spot in each, and how to rate an inventory's confidence honestly.

Your agent telemetry is better than it has ever been, and it still cannot certify that your controls held under attack. Three limits of the signal layer, each with a test attached.

Three proposals, all called an AI security assessment, priced five-fold apart. What actually drives the number, the four conditions that justify adversarial testing, what each deliverable contains, and the five questions to ask before you sign.

Every workflow you are automating had a person doing integrity work nobody logged as a control. Here is what has to replace it — and how to test that it holds.

MCP turned the agent tool-chain into a supply chain. Tool descriptions are instructions a model reads, scopes are granted in bundles, and chained agents lose provenance. Three boundaries, each with a test attached.

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.