Forged at USCYBERCOM. We replace "Detect & Respond" with Engineered Certainty. Architects of: 🛡️ Warden 🔗 Digital Shield 🤖 AI SAFE² 👇 Get the Blueprint
Baltimore - Washington DC
Joined January 2021
- Tweets24.7K
- Following556
- Followers1.3K
- Likes33.8K
Pinned Tweet
The Death of Governance & Compliance in an AI Era..
This statement is obvious for some, obscure for others and for many it is down right confusing.
Most "AI Security" advice is focused on governance and compliance guidance...aka how to create an AI policy or program.
It’s vague.
It’s theoretical.
It’s useless to an engineer.
But most of all it misses the point with AI.
We've been lead to believe if...
We document, monitor or detect risk, we can manage it and everything will be OK.
Reality is...
AI risk is executed in milliseconds & Documentation Does NOT STOP Execution.
♟️Strategic Truth
Most AI security advice from a policy standpoint:
🚫Optimizes for regulatory defensibility
🚫Assumes post-event containment
🚫Treats AI as a governance problem
But AI exploitation is an engineering problem.
Thus, until policy mandates preventive, runtime-enforced controls, AI security guidance will remain structurally misaligned with the threat landscape.
This is where we stepped in when we built the architecture standard back in Jun 25.
Been working hard on AI SAFE² ever since but accelerated after the explosion of risk last quarter by turning this holiday season into go-mode.
We Finalized a v2.0 then quickly finalized a v2.1 both are massive upgrades & industry must haves!
Introducing the AI SAFE² Framework (v2.1):
☑️The Universal GRC Standard for Agentic AI & ISO 42001 Compliance.
📂The open-source protocol for governing Agentic AI.
- Dropped our Github Repo
Let's unpack what all this actually means for AI Governance & Compliance moving forward...🧵
Star the repo to show your support👇
Policy in the system prompt weakens with every turn, every jailbreak, every "ignore previous instructions." Policy as code applies the same rule the same way every time, and the rules ship with tests that run in CI. Untested policy drifts quietly.
How to turn written policy into enforced, tested code: cyberstrategyinstitute.com/s…
Foundational. Imagine what vibe coding looks like.
Insecure by default means exposed before the customer starts.
Fatal flaw #3: shipping the features and making the buyer discover the danger.
Demand secure defaults. Limit privileges. Disable unnecessary services.
What does your product expose on day one?
Before your AI agent buys something, sends an email, or connects an app, it can get a second look.
Agency Check returns one card: what checks out, what doesn't, and the one thing to verify. It never acts on its own.
Free: 20 checks a month, no card.
agency-check.app
Your AI policy is a PDF, and your agent has never read it. A system prompt is a request the model can be talked out of. Enforcement means a decision point in the execution path: each consequential action checked, allowed or denied, and logged.
Policy as code for AI agents with OPA, Cedar and approvals: cyberstrategyinstitute.com/s…
Insecure by default means exposed before the customer starts.
Fatal flaw #3: shipping the features and making the buyer discover the danger.
Demand secure defaults. Limit privileges. Disable unnecessary services.
What does your product expose on day one?
Your code review assumes a human wrote the change. For a growing share of commits, that's no longer true. Gate AI-written changes on four records: scan results, dependency checks, what the agent ran, and who approved what.
The four-layer merge gate, layer by layer: cyberstrategyinstitute.com/s…
The gap between IT and OT has physical consequences.
Fatal flaw #4: separate teams, separate plans, shared exposure.
Map dependencies. Control access. Exercise safe containment together.
Can security and operations execute the same response?
Agent harnesses get bypassed at the seams: tool description injection, gaps in approval flows, delegation that hands a sub-agent more authority than its task needs. Each one is testable. When an assessment names a bypass, ask to see the test that proves it.
The bypass paths, and the tests that catch each one: cyberstrategyinstitute.com/s…
Claude Code's own docs say bypassPermissions mode skips permission prompts, including writes to .git and .claude, and to use it only in containers or VMs. That's a harness setting. Map what your harness allows before you spend another week evaluating the model.
Agent harness security: architecture, controls and assessment: cyberstrategyinstitute.com/s…
Five places AI development breaks, five free tools: a coding agent baseline, an AI code debt estimator, a harness risk grader, a sandbox escape and leak test plan, and a Rego and Cedar policy pack. Each control tested with positive and negative cases.
Your patch queue is the attacker’s waiting room.
Fatal flaw #5: waiting for a fix without containment in place.
A remediation deadline does not stop an attack.
What protects your highest-risk asset before the patch lands?
Model safety training is a property of the model. It isn't a security boundary. Every consequential agent action needs a control outside the model: the harness, the tool boundary, a policy decision point. Put the decision where the model can't argue with it.
The next prompt injection may arrive as another agent’s finished work.
A2A messages, task artifacts and shared memory cross trust boundaries too.
AI SAFE²: verify the sender, inspect the content, constrain delegation, authorize the action.
What governs your handoffs?
Test the handoff with clean and tampered artifacts.
Does a forged approval reach a tool? Does legitimate work still complete?
Keep evidence of the principal, delegated scope, artifact and decision.
github.com/CyberStrategyInst…
