Authority
What exactly is this AI allowed to do, for what purpose, on which systems and within what limits?
AI agent governance: authority, approval and evidence.
As AI moves from answering questions to taking actions, the question for a regulated organisation changes from “is the model good?” to “was this action authorized, who approved it, and can we prove it?”. CloudSeals is an AI governance platform built around that question: a policy boundary applied before an action, a person with authority who approves it, and evidence that survives afterwards. AI recommends; people and policy decide.
What AI agent governance means
AI agent governance is the set of controls that decide what an AI agent may do, check each consequential action against policy before it happens, involve a person where policy requires, and keep a record of the decision and its outcome. It is the operational layer of AI governance: not only listing and classifying AI systems, but controlling and evidencing what they do when they act.
Five questions every organisation should be able to answer
Identity
Which model or agent produced this recommendation, and who is the accountable owner?
Policy
Was the applicable rule, procedure or approval threshold applied before the action, not only documented in a register?
Human approval
Which actions needed a named person to approve, and did that person approve, reject or override?
Evidence
Can someone who was not there reconstruct the source, the policy, the approval, the action and the result?
How CloudSeals approaches it
The TrustOps loop is the same in every CloudSeals application: observe the data already in the systems in place; reason to an exception and a proposed next action with its context; apply the policy boundary; assess the risk in operational context; route to a person with authority to approve; execute only what was approved; retain the evidence in Evidence Fabric. The boundary and the approval are configured for each deployment — which rules apply, who may approve what, and within which limits.
Policy boundary before action
The rule, threshold or procedure is applied before a recommendation can become an action, so an out-of-policy proposal is stopped or escalated rather than explained afterwards.
Human approval where policy demands it
Recommendations are reviewed and approved, rejected or overridden by someone with the authority to do so. The application does not act on its own.
Evidence from decision to outcome
Source context, model or agent and purpose, policy and authority context, approvals and overrides, downstream actions and final results are recorded as the work happens.
Where it runs today
LedgerSight — finance operations
Exceptions and next actions are proposed, reviewed and approved before any business action, with the evidence retained.
CompliSight — industrial safety
Configured PPE and site-safety detections from approved CCTV feeds; a supervisor reviews and closes every alert.
CarbonSight — carbon MRV
Source data, approved method, exception review and evidence an assurer can check. The assurance opinion comes from the assurer.
What the standards bodies are working on
Agent identity and authorization are moving from concept to implementation. On 29 September 2026 NIST’s National Cybersecurity Center of Excellence published a summary of comments on its software and agentic AI identity concept paper (NIST, accessed 2026-10-05). In the EU, the AI Office’s enforcement powers under the AI Act are active for applicable obligations (European Commission, accessed 2026-10-05); see our EU AI Act guidance. NIST work is voluntary guidance, and the EU AI Act is not UK law. This is information, not legal advice.
How this fits with other controls
AI agent governance does not replace identity and access management, security monitoring or model security. Those controls establish who the agent is and protect the systems around it. CloudSeals addresses the layer above them: whether a specific business action was authorized by policy, approved by a person and evidenced. Many AI governance platforms also inventory and classify an organisation’s AI systems; for a neutral view of the kinds of platform, see Evaluating AI governance platforms.
Where CloudSeals is not a fit
A registry for all your AI
CloudSeals does not inventory or classify third-party models and agents across an enterprise.
Governing third-party agents
It governs the AI-assisted decisions inside its own applications, not agents built on other platforms.
A compliance guarantee
It records what happened. Whether that satisfies a regulation is judged by your auditors and regulators.
Autonomous action
If you want AI that acts without a person approving, this is the wrong platform by design.
Questions we get asked
What is AI agent governance?
AI agent governance is the set of controls that decide what an AI agent is allowed to do, check each consequential action against policy before it happens, route it to a person with authority where required, and keep a record of the decision and what followed. CloudSeals implements this around AI-assisted decisions in finance, industrial safety and carbon workflows.
Is an AI governance platform the same as AI agent governance?
Not exactly. AI governance platforms usually inventory models and agents, classify their risk and map them to frameworks. AI agent governance is the narrower, operational part: controlling and evidencing what an agent does at the moment it acts. CloudSeals focuses on that operational part.
What is the difference between agent identity and agent authority?
Identity tells you who or what the agent is. Authority is whether the organisation has authorized this specific action, for this purpose, within these limits. CloudSeals records the policy and authority context behind each decision in Evidence Fabric so the question can be answered afterwards.
Does CloudSeals let AI agents act on their own?
No. In CloudSeals applications AI recommends and people and policy decide. A policy boundary is applied before an action, a person with authority approves it, and only what was approved is carried out.
What evidence does CloudSeals keep for an AI agent decision?
Source context, the model or agent and its purpose, the policy and authority context, the proposed action, approvals and overrides, downstream actions and the final result, written to Evidence Fabric as the work happens.
Can CloudSeals govern agents built on other platforms?
Not today. CloudSeals applications govern the AI-assisted decisions inside their own workflows (LedgerSight, CompliSight, CarbonSight). They are not an inventory or monitoring layer for third-party agents; buyers who need that should compare platforms built for it.
Does AI agent governance replace identity and access management or model security?
No. Identity and access management, security monitoring and model security are separate controls that a governed workflow depends on. CloudSeals addresses the layer above them: whether a specific business action was authorized, approved and evidenced.
Related
What an accountable AI platform is · Evaluating AI governance platforms · TrustOps · Evidence Fabric · Security · Book a walkthrough