AI Safe Ops Toolkit

🥱 An integrated Risk Management Framework for the boring-but-critical AI challenges: Governance, Security, Compliance, and Operational Resilience.

Vision & Mission

Vision: To become the standard framework for managing the risks of AI agentic systems.

Mission: We provide developers and organizations with a comprehensive, open-source toolkit to systematically identify, assess, control, and monitor the unique risks associated with AI agents, enabling safe and compliant innovation.

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Target Audience

AI Engineers developing AI agent systems with frameworks like:

• langchain & langgraph
• Google Agent Development Kit
• crewAI
• agno

seeking for a solution covering all "boring" topics like:

• Governance
• Security
• Guardrails
• Compliance
• Regulation

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The Challenge: From Powerful Tools to Trusted Systems

AI agent frameworks (Google ADK, LangChain, CrewAI) provide incredible power, but this power creates a new class of risks. How do you prevent agents from leaking data? How do you ensure they comply with laws like the EU AI Act? How do you stop them from failing in unpredictable ways?

AI Safe Ops replaces ad-hoc checklists with a structured, continuous risk management lifecycle.

The Framework: A Continuous Cycle for Agent Safety

Our toolkit is built around the four established phases of risk management, applied to the specific domains of AI agents.

graph TD
    subgraph " "
        subgraph Risk Domains
            direction LR
            D1[Security]
            D2[Compliance]
            D3[Governance]
            D4[Operations]
        end
    end

    subgraph Risk Management Cycle
        A("1. Identify & Analyze") --> B("2. Assess & Score");
        B --> C("3. Control & Mitigate");
        C --> D("4. Monitor & Report");
        D -- Continuous Feedback --> A;
    end

    D1 -- are inputs for --> A;
    D2 -- are inputs for --> A;
    D3 -- are inputs for --> A;
    D4 -- are inputs for --> A;

    classDef domains fill:#FFF,stroke:#444,stroke-width:2px,color:#333;
    classDef cycle fill:#E3F2FD,stroke:#0277BD,stroke-width:2px,color:#01579B;

    class D1,D2,D3,D4 domains;
    class A,B,C,D cycle;

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1. Identify & Analyze: Proactively uncover risks in your codebase, configuration, and documentation.
2. Assess & Score: Evaluate the severity of identified risks to prioritize action.
3. Control & Mitigate: Implement active measures to prevent risks at runtime and fix vulnerabilities at the source.
4. Monitor & Report: Continuously observe agent behavior and provide a verifiable audit trail.

We apply this cycle across four key risk domains: Security, Compliance, Governance, and Operations.

The AI Agent Risk Matrix

This matrix is the heart of our toolkit. It shows how our features systematically address risks across the entire lifecycle.

Phasen → Domänen ↓	1. Analyse (Checkups & Audits)	2. Bewertung (Scoring)	3. Steuerung (Guardrails & Recommendations)	4. Überwachung (Monitoring)
Security	bandit Code-Scan detect-secrets pip-audit Dependencies	Kritikalität von CVEs & Secrets (High/Medium/Low)	Guardrail: Block SQL-Injection Recommendation: Upgrade package X to Y	OTel: Alert on repeated attack patterns
Compliance	AI Audit vs. eu-ai-act.yml	Report: Fulfilled vs. unfulfilled legal requirements	Guardrail: Block users from restricted regions Recommendation: Add specific disclaimer text	OTel: Log every compliance-relevant action (Audit Trail)
Governance	Scan for PII in logs Scan for biased language Check README & LICENSE	"High" risk score for PII exposure	Guardrail: Redact PII in real-time Recommendation: Add a Model Card	OTel: Dashboard "PII Redactions per Hour"
Operational	Analyze for error patterns & prompt injection vulnerabilities	Frequency of errors and hallucinations	Guardrail: Filter prompt injections Recommendation: Implement human-in-the-loop for specific tasks	OTel: Trace agent execution flow for debugging

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Toolkit Capabilities

Our framework is implemented through a set of powerful, integrated engines, all accessible via our interactive CLI.

• 🖥️ The CLI (ai-safe-ops-cli): The single, interactive entry point to the entire risk management framework.
• 🔍 Audit & Checkup Engine: The core of the Analysis phase. Runs fast, deterministic Blueprint Checkups and deep, AI-powered Policy Audits to identify risks.
• 🛡️ Guardrails Library: The heart of the Control phase. A lightweight library that integrates directly into your agent's code to block, redact, and manage behavior in real-time.
• 🧠 Recommendation Engine: The intelligence layer between Assessment and Control. It generates actionable recommendations based on findings, best practices, and real-time threat intelligence (e.g., new exploits, new prompt injection techniques).
• 📊 Reporting Engine: The output of the framework, serving the Monitoring phase. It generates reports in multiple formats – starting with machine-readable JSON for tool integration, human-readable Markdown for developers, and audit-ready PDF for stakeholders.
• 📡 Observability (via OpenTelemetry): The foundation of the Monitoring phase. All components, especially Guardrails, emit structured logs and traces, integrating seamlessly into your existing monitoring platforms (Datadog, Honeycomb, etc.).

🗺️ Roadmap: Filling the Matrix

Our development is focused on systematically building out the capabilities in our risk matrix.

Phase 1: Foundation - Analysis & Assessment

Goal: Provide a robust, offline-first engine for baseline risk identification and scoring.

• CLI: Interactive Go-based TUI.
• Checkup Engine: Deterministic scans for Security, Governance, and Operational risks (bandit, detect-secrets, pip-audit, PII scans).
• Scoring: Basic risk classification (High, Medium, Low).
• Reporting: Generation of Markdown reports.

Phase 2: Active Mitigation - The Guardrail System

Goal: Implement the real-time risk control mechanisms.

• Guardrails Library: Create the core GuardrailManager with spaCy integration for rule-based filtering (PII, Keywords).
• guardrails.yml Schema: Define the configuration standard for runtime rules.
• OpenTelemetry Integration: Instrument Guardrails to provide a live feed for the Monitoring phase.
• Guardrail Templates: Provide pre-built rules for common Security and Governance risks.

Phase 3: Advanced Intelligence - AI Audits & Recommendations

Goal: Introduce adaptive and intelligent capabilities to the framework.

• AI Audit Engine: Build the PydanticAI agent for flexible Policy Audits, starting with Compliance risks (EU AI Act).
• policy.yml Schema: Define the "Compliance-as-Code" standard.
• Recommendation Engine (v1): Integrate static recommendations into the report based on scan results.
• Advanced Reporting: Add JSON output to the Reporting Engine.

Phase 4: Full Lifecycle Integration

Goal: Complete the risk management cycle with advanced monitoring and dynamic intelligence.

• Recommendation Engine (v2): Connect the engine to external threat intelligence feeds for dynamic recommendations.
• CI/CD Integration: Provide templates for GitHub Actions to automate the Analysis and Assessment phases.
• Advanced Reporting: Add PDF generation capabilities.

🤝 Contributing

We are building the standard for AI Agent risk management and welcome contributions. Please see CONTRIBUTING.md for guidelines.

📄 License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.