Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
DeepInspect intercepts HTTP AI traffic between authenticated users or agents and any LLM, enforces identity-bound policy at the request layer, and writes a per-decision audit log. Aim Security sits primarily in the browser and DLP layer. This comparison walks through where each tool can and cannot enforce, what the audit trail looks like, and which one a deployer chasing the EU AI Act August 2 deadline should pick.
Read post →EU AI Act Article 12 takes effect August 2, 2026 and changes what regulators ask of high-risk AI systems. Compliance teams that came from GDPR are familiar with management-level documentation regimes. The Act asks for operational-level per-decision evidence. This piece walks through the four mindset shifts a security and compliance organization has to make: from policy documents to live audit records, from quarterly reviews to per-request decisions, from third-party attestation to first-party evidence, and from boundary controls to per-route enforcement.
Read post →The "best AI guardrails platform" question collapses without a clear set of architectural criteria. The criteria that hold up under regulator review are inspection boundary, write-path independence, policy versioning, audit field set, integrity stamping, model-agnosticism, and fail-closed behavior. This piece walks through the criteria, the questions a buyer asks of each vendor, and the architectural pattern that satisfies all seven, so the evaluation matrix the buyer uses produces a defensible decision the security team and the audit reviewer accept.
Microsoft, SAP, Oracle, Salesforce, ServiceNow, and Workday all sell AI agents under enterprise contracts. When The Register asked who is liable for the decisions those agents make, Microsoft and SAP declined to comment and the other four did not respond. The contract language already places the risk on the deployer. This piece walks through what the regulators say, what the contracts say, and what a deployer must produce on its own to discharge the obligation.
Read post →An AI security proxy intercepts HTTP traffic between authenticated users or agents and LLM APIs, evaluates each request against identity-bound policy, and writes a per-decision audit record before the response returns. The pattern differs from the traditional forward proxy at four architectural points: prompt-level data classification, identity binding at the request layer, fail-closed policy evaluation, and tamper-evident audit independence. I walk through the architecture and where it fits in the 2026 enterprise AI stack.
Read post →Procurement teams now use LLM workflows to read vendor questionnaires, summarize SOC 2 reports, draft RFP scoring rationales, and evaluate vendor risk packages. The boundary between the procurement officer identity, the vendor data, the diligence prompt, and the resulting recommendation is where the security and audit obligations sit. This piece walks through the data a procurement LLM workflow reads, the identity-aware policy decisions the deployment commits, the audit record that satisfies EU AI Act Article 12 obligations, and the architectural pattern that closes the post-authentication gap.
Read post →Marketing teams now draft a large share of campaign copy, ad variants, and landing-page hero blocks through LLM workflows. The boundary between the marketer identity, the brief, the brand guideline retrieval, and the generated draft is where the security and audit obligations sit. This piece walks through the data a marketing LLM workflow reads, the identity-aware policy decisions the deployment commits, the audit record that satisfies EU AI Act Article 12 obligations for high-risk marketing claims, and the architectural pattern that closes the gap most content-generation pipelines leave open.
Read post →The AI security vendor landscape in 2026 splits across model-side guardrails, browser extensions, CASB integrations, ML observability, and identity-aware proxies. Each category solves a different problem and produces different evidence. This buying guide walks through the ten questions a CISO or compliance lead should ask any AI security vendor before purchase. The questions reflect the EU AI Act, NIST AI RMF, ISO 42001, and sector frameworks the buyer is buying against. The aim is an architectural fit decision, not a feature-checklist comparison.
Read post →Banks handle AI traffic that touches credit decisions, fraud screening, customer service transcripts, internal research copilots, and increasingly model-assisted regulatory reporting. Each route carries a different supervisory expectation. This piece walks through the regulatory regimes a US or EU bank operates under, the inspection target the gateway covers per route, the audit record format that satisfies OCC SR 11-7, FFIEC AIO guidance, EU AI Act Article 12, and the deployment topology that fits a bank-grade environment.
Read post →AI audit software is not one product category. It splits into governance and GRC platforms, model evaluation and testing tools, bias and fairness testers, AI traffic audit and enforcement layers, and audit-log integrity tooling. Each proves a different thing, and a team buying "AI audit software" without knowing which category it needs ends up with a policy binder and no request-level evidence. This piece breaks down the five categories, what each one produces for an auditor, and the evaluation criteria that separate real evidence from a dashboard.
The NIST AI RMF GOVERN function, EU AI Act Articles 12 and 26, ISO/IEC 42001, and OMB agency guidance all reduce accountability to one operational test: who authorized which AI action, and can you produce the record that proves it. This is a comparison of what each framework demands, and where a policy gateway supplies the evidence layer without standing in for the governance program itself.
Read post →The EU AI Act assigns AI systems to four risk tiers (prohibited, high-risk, limited-risk, minimal-risk). The classification determines which obligations apply and when they take effect. This page walks through the classifier the DeepInspect team built to score your AI system against the Annex III high-risk categories, the supporting articles, and the inputs the classifier needs to produce a defensible verdict.
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