The engineers who build our runtime security work on your agent estate

Warden is Odingard's services organization. Same people, same research, applied to the systems you are already running — red-teaming autonomous agents, constraining what they can reach, and turning the results into evidence an auditor accepts.

Request a briefing Explore the practices

Assurance built for software that waits is the wrong tool here

A traditional assessment describes an application that does what it is told. An agent chooses its next action from context it was handed at runtime, which means the interesting question is not what the code can do but what the agent can be persuaded to do with the credentials you gave it.

The boundary is the tool call, not the perimeterNetwork controls do not see an agent reading a document and then calling an API with what it just read.
Findings expire faster than reportsA point-in-time assessment of a system whose behavior depends on its prompt is stale before it is formatted.
Regulators want evidence, not assertionsThe EU AI Act, ISO/IEC 42001 and NIST AI RMF all ask what you can show, not what you have documented.

The five practices

Agentic Assurance

Find out what your agents can actually be made to do, before someone else does.

Agent Readiness Assessment →Establish what your agents can reach today, and whether that is defensible.
Agentic Red Teaming →Attack your agents the way an adversary would, then prove the fix.
Tool-Boundary Review →Right-size what each agent can reach, and make the limit enforceable.
Memory & Provenance Audit →Establish where an agent's beliefs came from, and whether they can be trusted.

AI Governance

Get an AI program that satisfies the EU AI Act, ISO/IEC 42001 and NIST AI RMF on evidence rather than intent.

EU AI Act Readiness →Classify your AI systems, find the high-risk ones, and build the file the Act asks for.
ISO 42001 Alignment →Build an AI management system that a certification body will accept.
NIST AI RMF Alignment →Adopt the Govern, Map, Measure, Manage functions with evidence behind each one.
AI Inventory & Discovery →Find the AI already in use, including everything nobody registered.
Model Risk Governance →Extend model risk management to systems that do not behave like models.

Continuous Compliance

Run the control monitoring and evidence operation without hiring a team to do it.

Managed Compliance →We run the control monitoring and evidence program so you do not have to hire for it.
Evidence Operations →Turn evidence collection from an audit-season project into a background process.
Auditor Room Management →Run the audit exchange so your engineers answer questions once, not five times.
Control Crosswalk Build-out →Map one control set across every framework you are held to, so evidence is produced once.

Deployment

Get Cerberus and TraceLock into production, integrated with what you already run.

Cerberus Runtime Deployment →Get the runtime enforcing real policy in production, without breaking the agents.
Implementation & Migration →Stand up the platform and move off what you are replacing, without a gap in coverage.
Integration Engineering →Connect the platform to the systems that hold your evidence, including the bespoke ones.
Enablement & Training →Leave your teams able to run this without us, which is the point.

Advisory

Standing access to the people who do this work, for the decisions that do not wait for an engagement.

AI Governance Advisory →Standing access to the people doing this work, for decisions that will not wait.
Virtual AI Risk Officer →A fractional AI risk function for organizations not ready to hire one.
Board & Regulator Briefings →Prepare the AI risk conversation for the audiences that will not accept hand-waving.
Policy Program Design →Write AI policy people can follow, and prove they acknowledged it.

How a Warden engagement runs

Every practice follows the same shape, because the value is in the sequence: find out what is true, prove it adversarially, fix the boundary, then leave the evidence running.

Establish the estate

Inventory the agents, models, tools and data paths actually in use — including the ones nobody registered.

Test it adversarially

Attack the agents with the same techniques we research, so the failure mode surfaces in an exercise rather than in production.

Constrain the boundary

Put the enforceable control where the action happens, then confirm the attack that worked no longer does.

Leave evidence behind

Wire the result into continuous monitoring so the next auditor question is answered by a system, not by a person remembering.

Start with a briefing, not a statement of work

Tell us what your agents can reach today. We will tell you which of these engagements is worth doing first, and which ones you do not need yet.

Request a briefing See how we secure agents