InferenceThe AI Security Playbook
Technical guides on securing AI agents, governing LLM calls, and building enterprise-ready autonomous systems.
Detecting the Lethal Trifecta: Stopping Injection-to-Exfiltration
Private data + untrusted content + an exfiltration channel: every leg looks innocent alone. How session-taint tracking blocks the third leg before the data leaves.
Read articleWho Did That? Why Every AI Event Needs a User Attached
HIPAA's first technical safeguard, NIST 800-171's audit trail, EU AI Act logging — all reduce to one question your AI stack must answer: which user caused this call?
Read articleIn-Process Runtime Governance: Enforce Before the Prompt Leaves
Dashboards observe, proxies intercept from the wrong side of the trust boundary. Why EU, healthcare, and defense compliance all point at enforcement inside the process.
Read articleThe Complete Guide to AI Agent Security in 2026
Prompt injection, data exfiltration, tool abuse, MCP attacks — and the runtime controls that actually stop them. Everything your security team needs to know before deploying agents.
Read articleThe Complete Guide to Model Context Protocol Security
MCP changes what tools agents can reach — and the attack surface that comes with it. Tool impersonation, authentication gaps, RBAC, and how to deploy MCP servers securely.
Read articlePrompt Injection Explained: How Attackers Compromise AI Agents
Direct and indirect injection, tool poisoning, retrieval attacks — with real examples, detection methods, and why filtering alone isn't enough to stop a determined attacker.
Read articleRuntime Security for AI Agents: Why Static Guardrails Are Not Enough
Static rules don't know what happened two calls ago. Runtime enforcement does — with dynamic tool control, token monitoring, human approval gates, and a real-time DLP layer.
Read articleAI Agent Governance: Building Enterprise-Ready Autonomous Systems
Governance is the layer between your AI agents and the regulators asking questions about them — approval workflows, policy engines, human-in-the-loop, and enterprise deployment patterns.
Read articleAI Compliance Checklist for Healthcare, Finance, and Enterprise AI
HIPAA, SOC 2, GDPR, PCI DSS, ISO 27001, the EU AI Act — each framework asks something specific from your AI agents. Here's what you need to satisfy all of them.
Read articleWhy SDK-Based AI Security Beats AI Proxies
Proxies are blind to local models, add latency on every call, and route your prompts through a third party. An SDK enforces from inside the process — with code examples to show the difference.
Read articleSecure AI Agent Architecture for Enterprise Applications
The full reference architecture: Agent, MCP, LLM, SDK enforcement, Policy Engine, Audit Logs, Human Approval, DLP, and Runtime Security — with diagrams for each layer.
Read articlePreventing Data Exfiltration in AI Agents
PHI, PII, source code, secrets, API keys — how each leaks through AI agents, why cloud DLP misses them, and how in-process redaction closes the gap before the prompt leaves your environment.
Read articleBuilding Complete Audit Trails for AI Agents
User prompts, tool calls, policy decisions, model responses, cost tracking, compliance logging — what a forensically complete audit trail looks like and how to build one that satisfies your auditors.
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