Authensor is an open-source safety layer that sits between your AI agent and the tools it uses. It checks every action against a policy before it executes, scans content for threats like prompt injection and PII leaks, and creates a tamper-evident audit trail. It works with any AI framework including LangChain, CrewAI, OpenAI Agents SDK, Claude, and MCP servers.

What is an AI agent safety layer?

An AI agent safety layer is software that intercepts actions an AI agent wants to take and decides whether those actions should be allowed, blocked, or sent to a human for approval. Think of it as a firewall for AI behavior. Without one, agents can delete files, leak credentials, make unauthorized API calls, or send data to the wrong place.

Do I need Authensor if I already use Claude, ChatGPT, or LangChain?

Yes. These tools give your agent capabilities like file access, shell commands, API calls, and web browsing. They don't have built-in policies for what's allowed. Authensor adds that missing safety layer. It works alongside any AI tool or platform you already use.

How is Authensor different from prompt engineering or system prompts?

System prompts are instructions that the AI can be tricked into ignoring through prompt injection. Authensor enforces rules in code, outside the AI model. A policy that blocks 'rm -rf' cannot be overridden by a clever prompt. It is deterministic enforcement, not probabilistic guidance.

Yes. Authensor is MIT licensed and fully open source. You can self-host everything, including the control plane with PostgreSQL, at no cost using Docker Compose. There is an optional hosted tier for teams that want managed infrastructure.

How do I get started with Authensor?

Run 'npx create-authensor' in your terminal. It generates a working project with Authensor wired in. You can choose from five templates: TypeScript agent, Python agent, Claude Code hooks, MCP Gateway, or Docker self-hosted control plane.

What AI frameworks does Authensor work with?

Authensor works with LangChain, LangGraph, CrewAI, OpenAI Agents SDK, Claude Code (via hooks), any MCP server (via the MCP Gateway), and any custom agent through the TypeScript or Python SDK. It is framework-agnostic.

Does Authensor detect prompt injection?

Yes. The Aegis content scanner includes 20+ prompt injection detection patterns covering instruction override, role manipulation, delimiter injection, encoding attacks (base64, hex, unicode), and few-shot poisoning. It runs with zero dependencies and sub-millisecond latency.

Does Authensor help with EU AI Act compliance?

Yes. Authensor maps to EU AI Act requirements for high-risk AI systems including Article 12 (record-keeping via hash-chained receipts), Article 14 (human oversight via approval workflows), Article 9 (risk management via the RedTeam harness), and Article 13 (transparency via decision logging). The EU AI Act high-risk deadline is August 2, 2026.

What is the OWASP Agentic Top 10 for 2026?

The OWASP Top 10 for Agentic Applications (2026) is a risk taxonomy developed by 100+ industry experts. It covers ASI01 Agent Goal Hijacking, ASI02 Tool Misuse, ASI03 Identity & Privilege Abuse, ASI04 Supply Chain Vulnerabilities, ASI05 Unexpected Code Execution, ASI06 Memory & Context Poisoning, ASI07 Insecure Inter-Agent Communication, ASI08 Cascading Failures, ASI09 Human-Agent Trust Exploitation, and ASI10 Rogue Agents. Authensor covers all 10 through its policy engine, Aegis scanner (15+ prompt injection rules, 22 memory poisoning rules), Sentinel behavioral monitor (EWMA/CUSUM anomaly detection), and approval workflows.

What is an MCP Gateway?

An MCP Gateway is a transparent proxy between an MCP client and any MCP server that evaluates every tool call against policies before forwarding. Authensor's MCP Gateway implements the MCP SEP authorization protocol with authorization/propose, authorization/decide, and authorization/receipt message types. It addresses the fact that 32% of MCP servers have critical vulnerabilities.

How does Authensor compare to Galileo Agent Control or NeMo Guardrails?

Authensor is the only open-source framework that provides all four pillars: tool permissions, approval workflows, cryptographic audit trails, and content safety scanning. Galileo Agent Control (Apache 2.0) provides evaluation but lacks approval workflows and receipts. NeMo Guardrails focuses on conversational safety but not tool authorization. AWS AgentCore uses Cedar policies but is AWS-locked. Authensor is cloud-agnostic, self-hostable, and MIT licensed with 924+ tests.

What AI agent frameworks does Authensor integrate with?

Authensor has official adapters for LangChain/LangGraph, OpenAI Agents SDK, Vercel AI SDK, Claude Agent SDK, and CrewAI. It also integrates with Claude Code via hooks and any MCP server via the MCP Gateway. TypeScript and Python SDKs support any custom agent framework.

AI Safety Roadmap Template

An AI safety roadmap translates maturity goals into a time-bound plan with specific deliverables. This template provides a quarterly structure that can be adapted to your organization's current maturity level and priorities.

Quarter 1: Foundation

Objective: Establish baseline safety infrastructure for the highest-risk agents.

| Initiative | Deliverable | Owner | |---|---|---| | Deploy Authensor | Control plane running in production | Platform team | | Pilot policy | YAML policy for one agent, tested and deployed | Agent team + safety | | Audit trail | Hash-chained receipts recording all actions for pilot agent | Platform team | | Incident response | Documented runbook for AI agent safety incidents | Safety lead | | Baseline measurement | Current maturity level assessed and documented | Safety lead |

Quarter 2: Expansion

Objective: Extend safety controls to all production agents and add content scanning.

| Initiative | Deliverable | Owner | |---|---|---| | Policy templates | YAML templates for each agent category (customer service, internal tools, data analysis) | Safety lead | | Agent onboarding | All production agents under policy enforcement | Agent teams | | Content scanning | Aegis enabled for all agents handling user data | Platform team | | Red team exercise | First adversarial test of all production agents | Safety lead | | Approval workflows | Workflows configured for high-risk actions across all agents | Agent teams |

Quarter 3: Measurement

Objective: Establish quantitative safety metrics and behavioral monitoring.

| Initiative | Deliverable | Owner | |---|---|---| | Behavioral monitoring | Sentinel deployed and calibrated for all agents | Platform team | | Safety dashboard | Metrics dashboard showing denial rates, scan results, approval times | Platform team | | Policy tuning | Policies updated based on false positive data | Safety lead | | Compliance mapping | Regulatory requirements mapped to technical controls | Compliance | | Training | Developer training on safe agent development practices | Safety lead |

Quarter 4: Optimization

Objective: Refine and optimize based on data. Prepare for external audit.

| Initiative | Deliverable | Owner | |---|---|---| | Threshold tuning | Scanner and monitor thresholds optimized from Q3 data | Safety lead | | Audit preparation | Compliance documentation prepared for external review | Compliance | | Cross-agent analysis | Behavioral patterns analyzed across the agent fleet | Safety lead | | Red team round 2 | Second adversarial test, incorporating lessons from Q2 | Safety lead | | Roadmap refresh | Next year's roadmap drafted based on maturity assessment | Safety lead |

Adapting the Template

This template assumes a starting point of Level 1 or Level 2 maturity. If your organization is already at Level 3, shift the focus toward measurement, optimization, and advanced monitoring.

Review the roadmap monthly. Adjust timelines based on actual progress, but do not remove initiatives. If an initiative slips, it moves to the next quarter rather than being dropped.