AI agent security, identity, governance, cost, and the engineering behind the control plane.
Texas's TRAIGA is already in force and applies broadly, with no size threshold — a practical checklist for teams operating AI agents that reach Texas users.
How PCI DSS's core requirements apply when an AI agent touches cardholder data, and the controls that keep it out of scope where possible.
Human-in-the-loop approvals pause AI agents before high-risk actions, preserve throughput with async queues, and build an auditable approval trail.
How the EU's NIS2 Directive's risk management, incident reporting, and supply-chain duties apply to organizations operating AI agent platforms.
New York's RAISE Act targets frontier-model developers with safety-framework and incident-reporting duties — who it reaches and what to build regardless.
Where LLM costs hide in agentic applications, how to attribute them per agent and run, and the reservation-based enforcement that stops overspend at dispatch.
Illinois regulates AI in employment decisions and AI-analyzed video interviews — what hiring agents need to satisfy notice, bias, and human-review duties.
How HIPAA's Security Rule and BAA requirements apply to AI agents that read, generate, or transmit protected health information.
How logs, metrics, and distributed traces apply to AI agents—what to instrument, where costs hide, and how to connect all three for fast incident triage.
How the GLBA Safeguards Rule's written information security program requirements apply when AI agents access customer financial data.
How FERPA's consent, disclosure, and recordkeeping requirements apply when AI agents access student education records in K-12 or higher education.
Request counts alone don't protect AI APIs. The layered controls that work: per-connection limits, spend caps, tool allow-lists, and trust gates.
California's SB 53 requires frontier-model developers to publish safety frameworks and report incidents — here is the scope and the control work it implies.
Token Security discovers and secures non-human identities across the enterprise estate. What that covers, and what agent runtime governance adds.
Agentic AI creates novel data exfiltration paths via over-broad tool access, chatty outputs, and prompt injection. Learn how to contain each risk layer.
How to plan a SPIFFE/SPIRE deployment for agent workloads: trust domains, attestation policy, federation, and the operational pitfalls that show up first.
Ping Identity's enterprise IdP stack extends to agent authentication like its peers. What that coverage includes, and what runtime governance still owns.
Prompt injection hides malicious instructions in content AI agents process. How direct and indirect variants work, and what defenses reduce the risk.
Keycloak's realms, clients, and token exchange cover agent authentication cleanly. What agent delegation and per-tool scoping still require you to build.
Vault's dynamic secrets and leases fit agent credentials well. The patterns that work, and where lease-based secrets stop being enough for agent behavior.
Zero trust for AI agents means verifying every identity, enforcing least-privilege policy at every hop, and using behavioral trust scores at runtime.
Entra's managed identities and federated credentials remove standing secrets for Azure workloads. What that covers for agents, and what Entra Agent ID adds.
A centralized secrets vault removes scattered credentials from agent deployments. What it fixes for AI agents, and what it leaves for another layer.
SOC 2 auditors scrutinize AI platforms harder than traditional SaaS—learn which controls matter most, from tamper-evident audit trails to agent access.
