Govern Autonomous AI Before It Governs Your Business
Autonomous agents can execute thousands of actions before a human notices a mistake. AI SAFE² adds runtime governance, tool controls, auditability, and fail-safe recovery — so you can prove your AI stays under control.
★ Validate the source on GitHub →
Claude Code
Codex
Cursor
MCP
n8n
LangGraph
LangChain
CrewAI
OpenClaw
Hermes
AntiGravity
Lovable
Make
Manus
Where is your biggest AI risk today?
Don't browse a catalog — diagnose the exposure you already have. Pick the statement that sounds like your environment, see the cost of leaving it unaddressed, and go straight to the controls that close the gap.
Your agents can execute thousands of commands before anyone reviews a single one — and there's no clean way to roll a bad one back. You need controlled, recoverable execution.
→ Sovereign RuntimesYour MCP servers act on requests they can't verify, expanding privilege invisibly. One poisoned tool call is all it takes. You need validation and trust boundaries on every call.
→ MCP Security ToolkitIf an auditor asked you to list every autonomous system running right now, could you? You can't govern what you can't inventory. You need visibility, registries, and oversight.
→ AISMIf Legal asks who approved an AI action six months ago, can you answer? Without identity and an evidence trail, the honest answer is no. You need sovereign attribution.
→ NEXUSYour competitors sell smarter AI. We make it governable.
Whoever you are, there's one door built for you. Pick your role and go straight to the controls, evidence, or oversight you're responsible for.
Adoption creates risk. Controls create trust.
You don't get trust by adopting AI. You get it by being able to prove your AI is governed.
Not categories. Deployments.
Every deployment answers the same six questions: what problem it solves, what happens if you do nothing, the outcome you gain, time to value, difficulty, and a copy-paste quick start.
Your coding agent can execute commands faster than you can review them.
Unreviewed execution, silent privilege use, and no way to roll back an autonomous mistake.
Validated, monitored, recoverable execution wrapped around the agent.
# clone the framework $ git clone https://github.com/CyberStrategyInstitute/ai-safe2-framework $ cd ai-safe2-framework/examples/hermes-sovereign-runtime # wrap your coding agent in the sovereign runtime (see README) $ ./run.sh
Your MCP servers trust tools they cannot verify.
Unauthorized actions, tool abuse, and invisible privilege expansion across your toolchain.
Governed, auditable tool execution with validation and trust boundaries.
# clone, then enter the toolkit example $ cd ai-safe2-framework/examples/mcp-security-toolkit # wrap an existing MCP server with validation + mediation (see README) $ pip install -r requirements.txt $ python guard.py --server ./server.py
You cannot govern what you cannot inventory.
Shadow agents, no audit trail, and nothing to show an auditor when they ask who is running what.
Visibility and accountability for every agentic system in your environment.
# enter the AISM governance module $ cd ai-safe2-framework/AISM # discover and inventory autonomous systems (see README) $ ./aism scan --env production $ ./aism report --format registry
Every agent can act. Few can be trusted — and fewer can prove who acted.
No identity, no attribution, no reconstruction. Legal and Compliance can't answer "who did this?"
Sovereign identity and an evidence trail across six governance layers (L1 — L6).
# install the NEXUS python SDK from the repo $ cd ai-safe2-framework/NEXUS $ pip install -e sdk/python # issue a sovereign identity and start the evidence trail (see README) $ nexus identity issue --agent my-agent $ nexus audit tail --agent my-agent
OpenClaw provides powerful autonomous execution. SlowMist provides runtime behavioral controls. Neither provides full governance — and together they still leave organizational blind spots. AI SAFE² fills them.
The questions existing controls leave open
Strong execution and behavioral controls still can't answer the organization-level questions:
- ?Can we see every deployment?
- ?Can we identify anomalous behavior?
- ?Can we perform cross-agent analysis?
- ?Can we prove governance to auditors?
- ?Can we conduct adversarial exercises?
- ?Can we generate organizational evidence?
Not exclusive — run SlowMist Overlay on top of OpenClaw for defense in depth.
AI SAFE² wraps the tools and frameworks you already run — no rip-and-replace.
Alignment isn't a launch check. It's a vital sign.
Most organizations don't. They verify alignment once, at launch, and then hope it holds. Love Equation replaces hope with measurement.
Instead of only watching inputs and outputs, it measures behavior over time — continuously scoring the five variables that drive the alignment equation.
Start Using AI SAFE² in Under Five Minutes
People don't adopt frameworks — they adopt commands. Pick your runtime, toolkit, or alignment monitor and the exact integration steps are on this screen.
$ git clone https://github.com/CyberStrategyInstitute/ai-safe2-framework $ cd ai-safe2-framework/examples/hermes-sovereign-runtime $ ./run.sh # see README for options
$ cd ai-safe2-framework/examples/claude-code-sovereign-runtime $ ./run.sh # wraps your Claude Code session
# Antigravity integrates as a plugin, not a runtime wrapper $ cd ai-safe2-framework/examples/anti-gravity-sovereign-runtime $ ./install-plugin.sh # see README
$ cd ai-safe2-framework/examples/mcp-security-toolkit $ pip install -r requirements.txt $ python guard.py --server ./server.py
$ cd ai-safe2-framework/AISM $ ./aism scan --env production $ ./aism report --format registry
$ cd ai-safe2-framework/NEXUS $ pip install -e sdk/python $ nexus identity issue --agent my-agent
Nobody wakes up looking for a framework category.
They want to solve a problem, reduce a risk, or achieve an outcome. That's why these patterns are organized by the operational capability they provide — secure execution, trusted tool usage, governance, accountability, and continuous alignment. As the ecosystem grows, new examples simply fit into the outcome they help organizations achieve.
Secure Execution
Secure Tool Usage
Governance & Oversight
Sovereign Identity & Accountability
Continuous Alignment
High-Risk Environments
Not fear. Awareness.
If you can't answer these, your challenge is not AI adoption.
It is operational trust — and AI SAFE² exists to close that gap.
Don't want GitHub yet? Start with your exposure.
Not every visitor is ready to deploy. If you need to understand your risk, build a plan, or bring controls in with expert help, start here.
AI Operational Trust Assessment
Pinpoint where your autonomous systems are ungoverned and get routed to the controls that close the gap — before you write a line of code.
Take the assessment →Implementation Workshop
A hands-on session to stand up your first deployments and a governance baseline with your team.
Request a workshop →Governance & Risk Review
An expert review of your agent estate, control gaps, and a prioritized roadmap to operational trust.
Talk to an advisor →Toolkit & Enterprise Support
The AI SAFE Implementation Toolkit plus enterprise support to operationalize controls at scale.
Get the toolkit →From adoption to operational trust.
Move From AI Security Discussions to Deployable Controls
Policies explain intent. Deployments create trust.
AI SAFE² provides the implementation patterns needed to operationalize security, governance, and resilience around autonomous systems.