Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
A bearer key in front of an AI gateway proves possession of a secret, not the identity of the service that holds it, and a leaked key is indistinguishable from the real caller. Mutual TLS binds each calling service or agent to a certificate the gateway verifies on every connection. This piece shows what mTLS establishes for AI traffic, what it does not, and how it feeds the authorization decision.
Read post →Lawyers paste privileged client material into AI tools to draft and summarize, and a firm carrying a duty of confidentiality usually has no record of which matter that data belonged to or where it went. This piece shows how an AI gateway puts model traffic under matter-aware policy and produces the per-decision record a firm needs to show client data stayed inside its obligations.
Read post →Public-sector AI use carries obligations most agency deployments cannot yet meet: zero-trust access, a defensible record of automated decisions, and evidence for oversight and public-records requests. This piece shows how an AI gateway extends identity-aware access control and per-decision logging to model traffic, so an agency can govern AI use and produce the record its accountability rules assume.
Read post →Dynamo AI came out of MIT research on privacy and federated learning, and its platform now spans DynamoEval for adversarial testing, DynamoEnhance for remediation, and DynamoGuard for real-time guardrails. Teams look at alternatives when they want deeper agent evaluation, an observability tie-in, or identity-bound authorization on live model calls. This lays out three alternatives and where a runtime enforcement layer sits, with honest notes on what each is built to do.
Read post →Patronus AI runs automated LLM and agent evaluation, with an open research line (Lynx hallucination detection, Glider evaluator, Percival agent copilot) and a June 2026 Series B aimed at simulated worlds that stress-test agents. Teams look at alternatives when they want eval-as-code, an observability tie-in, or identity-bound authorization on live model calls. This lays out three alternatives and where a runtime enforcement layer sits, with honest notes on what each is built to do.
Robust Intelligence pioneered the AI Firewall and an algorithmic validation engine, now the core of Cisco AI Defense after a reported $400M acquisition. Teams look at alternatives when they want model security without Cisco fabric, or a different control point. This covers CalypsoAI, Lakera, and Protect AI, and where identity-bound runtime enforcement and per-decision audit fit.
Read post →Non-human identities, service accounts, API keys, workloads, and now AI agents, outnumber humans by more than 100 to 1 in the average enterprise (Palo Alto Networks, 2026). They carry static long-lived credentials, skip MFA, and share keys, which means the audit trail names a credential rather than an actor. This piece explains what an NHI is, why it resists the controls built for people, the specific attribution problem AI agents add on a shared key, and where identity-bound authorization on each AI call fits.
Read post →Non-human identities now outnumber humans by more than 80 to 1 in the average enterprise, and every one of those tokens can call an LLM. This overview walks through what an AI agent identity is, the four-stage lifecycle from issuance to authorization to recorded action lineage, and where NIST puts the control boundary between the application and the enforcement layer.
Read post →The Model Context Protocol gives a remote MCP server one job in OAuth terms: validate access tokens issued for it and reject the rest. That leaves the harder question, whether a given caller may reach a given tool, to a layer the spec does not define. This is an overview of how authorization works across MCP, where the OAuth 2.1 resource-server model helps, and why the HTTP path needs identity-aware enforcement and an independent audit record.
Agent identity governance is the policy layer that decides which AI agents may exist, what each is scoped to touch, and how an organization proves that oversight to an auditor. This walks through the lifecycle (provisioning, scoping, deprovisioning), the gap between policy on paper and enforcement at the request line, and how the discipline maps to the NIST three-pillar model, EU AI Act Article 12, and ISO/IEC 42001.
AI agent identity has blurred into one marketing message, but the tools underneath do distinct jobs. This buyer guide sorts the field by function: non-human identity security platforms (Astrix, Oasis, Token Security), workload identity brokers and cloud-native IAM (Aembit and the cloud providers), and runtime enforcement plus per-decision audit on the live AI call. Each entry is honest about what it secures and where its boundary sits.
The MCP specification runs a tool through one JSON-RPC method, tools/call, carrying a name and an arguments object, and it calls tools model-controlled. The OAuth token that lets a client reach the server authorizes the connection, and per-tool granularity is left to the implementation. This walks through the tool-call flow, the gap between a valid session and a permitted invocation, and what per-tool, per-caller, per-argument authorization with a per-decision record requires at the HTTP call boundary.