Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Generative AI returns text. Agentic AI takes actions in systems of record. The shift moves the security boundary from content moderation to authorization. Most enterprise deployments still treat agentic AI as if it were a chatbot, and the audit trail collapses the first time an agent writes to a database.
Shadow AI risk lives in three separate ledgers: the per-incident breach cost, the regulatory liability that attaches to the deploying organization regardless of which employee pasted what, and the contractual liability already shifting from AI vendors to enterprises. This piece walks through each ledger with the numbers from IBM, the EU AI Act, Fannie Mae, and Gartner, and shows where the architecture closes the exposure.
Read post →Most shadow AI prevention programs ship a blocklist of AI provider domains and call the work done. The block fires for fifteen of the top tools, employees route around it through personal devices and tethered phones, and the prompt traffic the policy was meant to stop continues. This piece walks through what prevention has to do mechanically to hold up under EU AI Act and HIPAA review, and where the enforcement layer sits.
Read post →A shadow AI policy is the document a regulator reads first when something goes wrong. Most copy-paste templates fail because they list rules without the enforcement architecture behind them. This piece walks through the seven sections a defensible policy contains, the enforcement architecture each section assumes, and where most published templates fall short of what an EU AI Act reviewer or a HIPAA auditor will actually accept.
Read post →Most shadow AI monitoring stops at the DNS layer or the CASB. Both miss the actual data leaving the organization because the prompt is the data, and the prompt sits inside an encrypted POST body. This piece walks through the four monitoring layers, what each one sees, where each one is blind, and the inspection architecture that produces evidence an EU AI Act or HIPAA auditor will accept.
Read post →Most employee ChatGPT monitoring conversations get stuck on whether the organization is allowed to do it. The answer in most jurisdictions is yes, provided the disclosure language in the handbook is correct and the inspection is proportionate to the security purpose. This piece walks through the disclosure model that holds up under labor review, the inspection architecture that produces evidence, and what an employee policy actually has to say.
Read post →Most organizations that decide to address shadow AI start by buying a tool. The tool deploys, fires alerts on day one, and produces a report nobody can act on. A working discovery program is a sequenced six-week framework that begins with what the organization already has (DNS logs, expense reports, SSO data) and adds inspection only after the surface is mapped. This piece walks through the framework week by week.
Read post →IBM Cost of Data Breach data shows that organizations breached through unsanctioned AI tools pay an average of $670,000 more per incident than the cross-industry baseline, take 247 days to detect, and lose customer PII in 65% of cases.
Read post →Prompt injection is the class of attacks where adversarial content in a prompt overrides the application instructions or extracts data the model was not authorized to reveal. The attack surface includes direct user prompts, indirect injection through retrieved documents and tool results, and chained injection through agent loops. OWASP has consistently ranked prompt injection as the top LLM vulnerability. This piece walks through the attack mechanisms in production, the failure modes of model-side defenses, the request-boundary controls that produce a defensible posture, and the audit record format that holds up after an attempt is detected.
Read post →Prompt injection examples that surface in production AI systems follow a small number of repeatable patterns. The patterns appear across customer support agents, RAG pipelines, agentic browsers, and code-assist tools. Each pattern has a control point at the request boundary where an inspection layer can produce a deterministic signal the policy can act on. This piece walks through twelve patterns from production incident response, the injection text that triggers each, the inspection-layer response that holds up, and the audit record that supports the post-incident review.
Read post →OWASP LLM01 captures both direct and indirect prompt injection in a single category in the 2025 update. The architectural reason is that the control point is the same: the request boundary. Application-side defenses fail by construction because the application cannot tell which spans of the prompt the model treats as instructions. Model-side defenses fail because refusal training is probabilistic. This piece walks through the LLM01 attack surface, the inspection-layer controls that produce a defensible posture, the audit record that survives review under EU AI Act Article 12 and DORA Article 19, and the deployment pattern that fits a production AI stack.
Read post →The OWASP LLM Top 10 enumerates the application-security risks that show up when an LLM is wired into a production application. The 2025 update reorganized the list to reflect what production teams actually see: prompt injection at the top, sensitive information disclosure and supply chain risk close behind, and a new category for unbounded resource consumption. This piece walks each risk to the inspection layer control that produces a defensible posture, the gap each risk exposes in standard application-side defenses, and where the audit record series intersects EU AI Act Article 12 and DORA Article 19 evidence obligations.
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