Technical Reference | AI Engineering Playbooks

A practical control checklist for securing MCP servers across identity, tool boundaries, data handling, and auditability. Treat MCP servers as privileged integration surfaces, not simple helper services. Enforce identity, scoped permissions, input validation, and full audit trails. Use a release gate that blocks deployment until critical controls are verified. MCP can accelerate agent integration, but it also expands your attack surface. If your server can read internal documents, call business APIs, or trigger workflows, it is effectively a privileged control plane. This checklist is designed for engineering teams that need to move quickly without creating avoidable security debt. Prerequisites A clear inventory of MCP tools and connected systems. A named owner for security decisions. Basic logging and metrics in place. Environment separation for development, test, and production. 12 production controls 1) Explicit trust boundary Document what the MCP server m...

A structured threat model for AI agent systems — covering the attack surfaces specific to LLMs including prompt injection, indirect injection, and sensitive data exfiltration. A structured threat model for AI agent systems — covering the attack surfaces specific to LLMs including prompt injection, indirect injection, and sensitive data exfiltration. Security posts with threat models and actionable mitigations rank highly and are widely shared by security and engineering audiences. Includes controls, pitfalls, and a phased implementation path. A structured threat model for AI agent systems — covering the attack surfaces specific to LLMs including prompt injection, indirect injection, and sensitive data exfiltration. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for ...

A practical guide to implementing output validation, content filtering, and audit trails in AI agent pipelines — with specific attention to regulated-sector requirements. A practical guide to implementing output validation, content filtering, and audit trails in AI agent pipelines — with specific attention to regulated-sector requirements. This is a topic most engineering blogs avoid because it's hard. Includes controls, pitfalls, and a phased implementation path. A practical guide to implementing output validation, content filtering, and audit trails in AI agent pipelines — with specific attention to regulated-sector requirements. Why this matters Teams are under pressure to deliver AI capability quickly, but speed without control creates operational and governance risk. This guide focuses on practical execution patterns that hold up in production. Prerequisites Clear ownership for delivery and risk decisions. Baseline observability for model and tool behaviour...

A practical pre-release checklist for AI features covering security, misuse risk, transparency, and governance. Shipping AI features without security and ethics checks creates hidden operational risk. Use this checklist before each release. 1) Data and privacy Confirm data minimisation in prompts and context. Remove secrets and personal data from logs. Enforce retention windows for model inputs and outputs. Validate third-party processor boundaries. 2) Security controls Restrict tool permissions by role and environment. Validate all tool outputs against strict schemas. Add prompt-injection defences for external content. Require approval gates for high-impact actions. 3) Safety and misuse Define clear disallowed use cases. Add risk prompts for potentially harmful requests. Add user-visible warnings for uncertain outputs. Add abuse monitoring and escalation paths. 4) Transparency and trust Disclose where AI assistance is used. Explain known limitations...