Fairly AI vs Trussed: Responsible AI Controls vs Runtime Enforcement
Fairly AI represents a category of responsible AI governance platforms that operate before deployment, focused on model risk assessment and compliance documentation. Trussed represents runtime enforcement, governing agent identity, permissions, and tool-call behavior while AI agents are actively running. These are different layers of the AI governance lifecycle, not competing solutions to the same problem.
Quick Answer
Fairly AI and Trussed sit at different points in the AI agent lifecycle. Fairly AI's category operates pre-deployment, assessing model risk and producing compliance documentation. Trussed operates at runtime, enforcing agent identity, permissions, and tool-call behavior while agents are live. Most enterprises need both, not one in place of the other.
Two Layers, Two Lifecycle Stages
AI governance is not a single checkpoint. It spans the point before an agent is deployed and the entire period it is running in production.
Design-Time Governance
Model risk assessment and compliance documentation before deployment.
Runtime Enforcement
Live policy enforcement, agent identity, and permissions during execution.
Full Lifecycle Coverage
Enterprises typically require both layers to close governance gaps.
Why This Comparison Requires a Lifecycle View
Enterprise AI governance is often framed as a single decision: choose one platform to "handle compliance." In practice, governance spans multiple stages of the AI agent lifecycle, and vendors operating within each stage solve different problems. Comparing a design-time responsible AI platform to a runtime enforcement platform is not a like-for-like competitive comparison; it is a comparison of where each layer of control applies, before, during, and after an agent is deployed.
Where Responsible AI Governance Frameworks Operate
Fairly AI represents a category of responsible AI governance platforms that operate before deployment. These platforms focus on model risk assessment and compliance documentation: evaluating a model's behavior, bias, and risk profile prior to release, and producing the documentation that internal review boards or regulators expect to see. This work happens design-time, before an AI agent is given access to production systems or live data.
Where Runtime Enforcement Operates
Trussed represents runtime enforcement: governing agent identity, permissions, and tool-call behavior while AI agents are actively running. Rather than assessing a model on paper, runtime enforcement establishes who (or what) an agent is, what it is allowed to do, and produces an audit trail of what it actually did during execution. This layer applies after deployment, continuously, for as long as the agent operates.
Design-Time Governance vs Runtime Enforcement
The two layers answer different questions. Design-time governance asks whether a model is acceptable to deploy, and whether that decision can be documented. Runtime enforcement asks whether a specific agent action, in the moment, falls within its approved permissions. An organization can have thorough design-time documentation and still have no visibility into, or control over, what an agent does once it is live and calling tools or other agents.
The Gap Enterprises Should Account For
Enterprises typically require both layers to close governance gaps. Design-time responsible AI governance and runtime enforcement are not competing solutions to the same problem; they are complementary controls addressing different points in the AI agent lifecycle. Evaluating a governance stack means identifying which layer, or combination of layers, a candidate platform actually addresses, rather than assuming one category of tool covers the full lifecycle.
Evaluation Criteria for AI Governance Leaders
Use these criteria to determine whether a candidate platform, or combination of platforms, actually covers your organization's requirements.
- Confirm whether a candidate platform operates before deployment, during runtime, or both, and ask for evidence rather than category assumptions.
- Identify whether your organization needs documented model risk assessment for internal or regulatory review, independent of runtime behavior.
- Identify whether your AI agents call external tools, access sensitive systems, or communicate with other agents in ways that require live permission enforcement.
- Ask how agent identity and least privilege are established and maintained during execution, not just described in policy documents.
- Determine whether audit logging exists to reconstruct what an agent actually did, separate from what it was approved to do.
- Map these requirements against your specific regulatory obligations before selecting design-time tooling, runtime tooling, or both.
Govern AI Agents at Runtime, Not Just on Paper
Trussed provides runtime governance and enforcement for AI agents, covering agent identity, permissions, tool approval, and audit logging during live execution.
Explore Runtime Governance