GDPR for AI Systems: Data Subject Rights and Erasure
How GDPR data subject rights apply to AI pipelines, what Article 17 erasure requires technically, and the design patterns that make compliance tractable.
AI agent security, identity, governance, cost, and the engineering behind the control plane.
How GDPR data subject rights apply to AI pipelines, what Article 17 erasure requires technically, and the design patterns that make compliance tractable.
OAuth 2.1 vs API keys for MCP servers after the Nov 2025 spec: PKCE, RFC 8707 resource indicators, token lifetimes, revocation, and when each fits.
EU AI Act for engineers after the 2026 Digital Omnibus: Annex III moves to Dec 2027, Art. 49/50 land 2 Aug 2026, plus a concrete readiness path.
Traditional IAM secures human users, not AI agents making thousands of calls per minute. Why a connection-centric model is the right foundation.
How to discover, register, and maintain every AI agent you deploy — the foundational inventory that access policies, spend caps, and audit trails depend on.
Most MCP servers ship with no authentication. Learn how to add identity verification, per-caller tool scoping, and guardrails to production servers.
The complete guide to AI agent security: identity, authorization, connection policies, content guardrails, monitoring, and incident response in one place.
Guardrails check content appropriateness; policies enforce rate limits and time windows. Both layers are required — neither substitutes for the other.
AI agent governance defines the runtime controls — identity, authorization, guardrails, budgets, and audit trails — that keep autonomous agents accountable.
Secure multi-agent workflows with authentication at every hop, scoped delegation tokens, and content guardrails on every message—three patterns explained.
Grant AI agents the minimum MCP tool access they need — no more. Learn how allow-lists, per-tool rate limits, and policy gates prevent blast-radius breaches.
Shadow AI grows faster than shadow IT. The three risk categories it creates and how a governance framework closes the visibility gap before incidents occur.
A practical MCP server security checklist covering authentication, tool-level authorization, rate limits, forensic logging, and monitoring for agents.
MCP gives AI agents a standard way to call external tools and retrieve context. Learn what it is, how it works, and the security controls it needs.
Every platform action is an API call. Learn how Praesidia's OpenAPI-described surface lets you automate governance, integrate tooling, and extend the platform.
The platform admin console gives super-admins cross-tenant visibility, DLQ triage, two-person governance controls, and GDPR erasure on a separate access plane.
A persistent, authenticated WebSocket stream replaces polling for agent tasks, workflow runs, and budget alerts — and what safe multi-tenant fan-out requires.
How to design liveness and readiness probes for AI services — what to check, how to avoid false positives, and what a production health surface looks like.
Stream AI agent events to your own systems and forward security signals to a SIEM — so agent activity is visible in the tooling your team already uses.
Issue, scope, and rotate organization API keys to give each integration only the access it needs — and limit blast radius when a credential is exposed.
Turn scattered user requests into ranked roadmap signal with a built-in feedback board that supports voting, moderation, and multi-tenant visibility.
Route AI agent budget alerts, guardrail violations, and task failures to Slack and other channels with a reliable, tenant-isolated dispatcher pattern.
Build a compliant email opt-out system with enforced suppression lists, per-category preferences, and bounce handling that protects your sender reputation.
Reliable transactional email for AI platforms: how consistent templates, authenticated sending, and delivery safeguards keep security and billing flows intact.