Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Compare Cisco AI Defense runtime content inspection, model validation, and security-stack integration with DeepInspect identity-bound LLM policy enforcement and audit records. Use the buyer questions in this guide to decide which evidence and blocking point your program needs.
Read post →COBIT AI incident reporting is an operating chain, rather than a regulator-facing deadline. This guide applies COBIT 2019 objectives for risk, service requests and incidents, managed security, performance monitoring, and internal control to authenticated HTTP traffic between users or agents and LLM endpoints. It defines the incident record, the evidence handoffs, the closure test, and the limits of an inline gateway.
Read post →China's Personal Information Protection Law applies to LLM workflows whenever prompts, retrieved context, outputs, or operating records contain personal information. Enterprises must establish an Article 13 basis, give the required notices, minimize the data, handle sensitive information under stricter rules, and meet automated-decision and cross-border conditions. Provider duties depend on the actual relationship: an entrusted party follows the handler's instructions and assists with compliance, while an independent handler carries its own PIPL obligations.
Read post →PIPL Article 55 requires a personal information protection impact assessment before sensitive-personal-information processing, automated decision-making, entrusted processing or disclosure, cross-border transfers, and other processing with a major influence on individuals. Article 56 defines the assessment content and requires the report and handling record to be preserved for at least three years. This guide turns those duties into an AI assessment workflow tied to the actual request path.
Read post →PIPL Article 57 requires a personal information handler to act immediately once a personal information leak occurs or might have occurred, and it treats distortion and loss the same way. The handler must take remedial measures and notify the relevant personal information protection authorities and individuals, with a conditional route for withholding individual notice when measures effectively avoid harm. AI incidents need request evidence, while cybersecurity triage remains outside an HTTP policy proxy.
Read post →The CPPA risk-assessment regulations require covered businesses to assess specified processing before it starts, compare privacy risks with benefits, document operational facts and safeguards, involve relevant employees, and update the assessment after material change. AI teams need to separate the broad risk triggers from the narrower ADMT rules, then connect each approved safeguard to a test and operating record.
Read post →California breach reporting for an AI incident follows the state data-breach statute and the CCPA private-action provision. The trigger depends on the personal information involved, acquisition or disclosure, encryption status, ownership, and security practices. CPPA automated-decisionmaking rules create a separate compliance track. This guide turns an LLM event into a notice decision, evidence package, and scoped response.
Read post →AIDA never became Canadian law. AI incident reporting in the private sector instead turns on PIPEDA: report to the Privacy Commissioner and notify affected individuals when a breach of security safeguards creates a real risk of significant harm, act as soon as feasible, and retain a record of every breach for 24 months. This guide applies that test to prompts, responses, and LLM destinations.
Read post →Canva AI compliance needs two evidence files. Canva documentation and account settings cover the provider service, privacy choices, team administration, and design sharing. The enterprise file covers the approved use case and acting identity. It also covers data classification and model destination. The file retains the policy decision and evidence for any AI request path the enterprise controls. Keeping the files separate prevents a provider document from being mistaken for proof of a specific prompt decision.
California SB 942 defines generative AI broadly enough to include systems that generate text, images, video, and audio. Its operative detection and disclosure duties focus on image, video, and audio content. This guide separates covered-provider applicability from content-level duties, licensee rules, later AB 853 obligations, enforcement, and the narrow evidence role available at an enterprise HTTP AI request boundary.
Read post →California SB 942 creates content-provenance duties rather than a statutory risk-assessment mandate. A useful assessment therefore starts with applicability, identifies each generative system and licensed deployment, tests manifest and latent disclosures, checks the public detection tool, and records ownership for gaps. The workflow stays narrow enough to distinguish provider duties inside the generation pipeline from request-layer evidence an enterprise can produce.
Read post →On July 7, 2026 the European Commission presented its Action Plan on Cybersecurity and Artificial Intelligence: pre-market evaluation of advanced models, an EU third-party evaluation capacity supporting the AI Office, an ENISA and JRC secure testing platform, a structured-access blueprint for advanced AI capabilities, and tighter alignment with NIS2 and the Cyber Resilience Act. I read the plan as a directional signal about evidence. This walks each pillar and maps it to the authorization and audit records an enterprise has to be able to produce on demand.
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