Silverfort brings identity controls to AI agents and their actions

Silverfort treats AI agents as actors with identities, permissions and accountable owners. Its AI Agent Security offering connects discovery and posture information with controls applied when an agent tries to access a resource or use a tool. The central buying question is whether the required action passes through a supported enforcement point before it can affect the business.

01 / ProductAn identity platform extended to autonomous actors

The Silverfort platform describes Runtime Access Protection, or RAP, as an inline layer integrated with identity infrastructure. It evaluates an access request and returns a security decision to the underlying identity system. The wider platform covers workforce, privileged and non-human identities, so its AI work builds on an existing identity-security context rather than starting with prompt filtering alone.

The AI Agent Security page adds agent discovery, links to responsible human owners and controls on agent actions before execution. These functions should be distinguished during procurement. Knowing that an agent exists can support governance, but it does not establish that every tool the agent uses is intercepted. The execution route determines where a policy can actually take effect.

Non-Human Identity Security addresses service accounts and other machine identities. Those identities may support an AI agent, but they can also belong to conventional automation. An effective inventory should preserve that relationship: an agent, the person responsible for it and the credentials it uses are connected entities with different lifecycles and security questions.

02 / AudienceFor organisations that need agents to act within existing authority

A useful audience is a company allowing an internal agent to query a business system while prohibiting certain changes. The agent may have a sensible purpose but inherit credentials with wider capabilities than the task requires. The security team needs an enforceable boundary around the actual operation, not merely an instruction in the agent’s prompt.

Another fit is an identity team discovering agents created by several business departments. Before approving more autonomy, it needs to know who owns each agent, which service accounts it touches and what information it can reach. Ownership should lead to a real decision-maker who can explain the task and accept responsibility for its permissions.

Noma Security is a relevant comparison for the broader AI application and agent security problem. Okta provides context for identity infrastructure and access. The systems may meet at an integration boundary, but their controls should be compared by what they inspect and where they can stop an action, rather than by a shared claim to secure AI.

03 / WorkflowProposed workflow: permit a lookup and stop an unauthorised change

Select a non-production agent with a clearly defined owner and a small test dataset. Give it one intended operation, such as retrieving an approved record, and one operation it should not perform, such as modifying a protected field. Use benign test data and an agreed policy. The point is to evaluate the permission boundary, not to stage an uncontrolled attack.

Confirm the route between the agent and the resource. If it uses MCP, identify the gateway and the tools it exposes. If it uses a native platform integration, identify the supported runtime hook. Trace the request through that path so the team knows whether the control evaluates the actual action or merely observes a related event afterwards.

Silverfort's Copilot Studio integration article describes native inline controls alongside its MCP approach. It says allow and block decisions can be applied before execution. The same article marks human step-up approval as coming soon, so this blueprint does not assume that feature is available in that integration. Confirm the current tenant release before making approval part of a required workflow.

Run the allowed lookup, inspect the returned information and record the identity context. Then submit the benign prohibited change and confirm both the policy decision and the unchanged target record. An alert alone does not prove prevention. The useful evidence is a permitted operation completing as intended and a forbidden operation failing before the protected state changes.

This is a proposed evaluation based on public sources, not a Sequenced test. Repeat the exercise after a supported identity or credential change and inspect ownership records. That shows whether the policy continues to represent the task as the agent evolves. Keep unsupported routes explicit instead of treating partial coverage as a universal guarantee.

04 / PricingAI Agent Security appears in the Plus package and above

The current pricing page describes four packages priced according to organisation size: Core, Plus, Advanced and Enterprise. Plus adds Non-Human Identity Security and AI Agent Security to Core. Advanced and Enterprise build on that scope with further capabilities. The page requests a quote rather than publishing a universal per-identity or per-agent rate.

The commercial page also says included modules can vary by environment type and offers an à la carte route. Those qualifications matter when comparing on-premises, cloud and hybrid deployments. Ask the vendor to identify the actual AI Agent Security modules, supported platforms and operational scope in the proposed order rather than relying only on the package name.

For a practical comparison, describe the agent population, identity systems and enforcement routes that matter to the business. Keep the agent platform and model provider’s charges separate from Silverfort’s quote. A security package is not automatically an entitlement to the external agent platform, and the cost of connecting or maintaining that platform remains part of the implementation decision.

05 / DistinctionsOwnership and execution context are the useful combination

Silverfort's original agent-security explanation emphasises tying agent activity to the people behind it. That is valuable when responsibility can otherwise become blurred between an employee, a developer, a service account and an agent. A meaningful ownership link should help the security team find the person who understands the intended task and can approve or revoke access.

The runtime emphasis adds a separate question: is this action allowed now? That differs from a periodic review of an account’s entitlements. The two are complementary; an account can pass an access review and still attempt an operation outside the narrow task assigned to the agent. A buyer should ask how policy expresses that task-level distinction for the supported tools.

The company's agent-security glossary separates discovery, ownership mapping, posture management and runtime enforcement. It also distinguishes recommended policy decisions from more autonomous enforcement. Use those categories to structure a demonstration, while retaining the narrower product and integration documentation as the source for what is actually available. A conceptual capability list is not an entitlement schedule.

06 / QuestionsCheck where the control stops and what changes over time

The first unresolved question is coverage. An agent can reach a system through more than one route, and a policy enforced through one gateway does not by itself establish protection of an unrelated connection. Ask the implementation team to enumerate the approved routes, supported integrations and any paths that remain governed by the resource’s native permissions.

The second is ownership quality. A mapped human owner is only useful if the person is still responsible and understands the workload. Test what happens when a project changes team, a service account is rotated or an agent is retired. The inventory and the operational access need to reflect the same reality; a historical owner label should not become permanent authorisation.

The third is the difference between a detected risk and a confirmed prevention. Keep logs, policy decisions and resulting resource state together in the pilot record. This makes it possible to establish what actually happened without accepting a vendor description of seamless protection as measured evidence. No latency, accuracy or breach-prevention result was independently tested for this article.

Finally, resolve conflicting availability language narrowly. A newer overview can describe human approval while an integration-specific article still marks it as forthcoming. Do not silently promote the broader claim into a promise for Copilot Studio or another platform. Ask for the current supported feature list and demonstrate that exact route before relying on it.

07 / DecisionChoose an action whose boundary can be demonstrated

Silverfort is relevant to AI adoption because it addresses the identities and authority behind agents that do real work. Its combination of discovery, ownership and runtime controls gives security teams a concrete evaluation path. The strength of the fit depends on whether the agent’s important actions pass through supported controls.

Begin with one allowed lookup, one prohibited change and an accountable owner. Confirm the commercial package, integration and current feature state, then inspect the target system as well as the security console. Expand only when the team can explain the scope of protection and the routes that still need separate controls.