C1.ai’s agentic control plane links discovery, vault, runtime, and response

At a glance

What this is: C1.ai argues that AI agent governance needs a single control plane spanning discovery, secrets, runtime permissions, and remediation.

Why it matters: IAM, IGA, PAM, and NHI teams need to see how discovery and lifecycle controls intersect with agent runtime behaviour before autonomous access spreads faster than governance can track it.

By the numbers:

• Only 5.7% of organisations have full visibility into their service accounts.
• 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).

👉 Read C1.ai's roundup of agentic control plane launches

Context

AI agent governance is moving from a point-solution problem to a lifecycle problem. Once agents can be discovered, provisioned, scoped, approved, and remediated as identities, the control question shifts from isolated tooling to whether IAM, IGA, and PAM can operate across the full agent lifecycle without leaving gaps.

C1.ai’s launch frames that gap directly. The article says organisations can no longer rely on separate scans, vaults, policy gates, and ticketing flows if they want to manage AI agents at scale. That is a useful signal for teams that still treat agent access as an extension of human access reviews rather than a distinct identity class with different runtime behaviour.

The same logic applies to NHI programmes already struggling with service accounts, API keys, and secrets sprawl. The operational pattern is familiar: find the identity, bound the credential, constrain the action, and close the loop when risk appears. The article’s starting position is typical for organisations trying to unify these controls, even if their tooling architecture is not yet there.

Key questions

Q: How should security teams implement AI agent credential management?

A: Security teams should issue short-lived, task-scoped credentials tied to the specific agent, tool, and resource involved, rather than sharing human sessions or static API keys. The control objective is to limit what the agent can do, preserve attribution, and make revocation practical when behaviour changes or becomes suspicious.

Q: Why do AI agents create more governance risk than ordinary integrations?

A: AI agents can connect quickly, run continuously, and accumulate broad permissions across multiple services. That combination makes ownership blur and scope drift more likely, so the real risk is not the tool itself but the uncontrolled access path it creates across enterprise systems.

Q: What breaks when discovery does not lead to ownership for AI agents?

A: You get visibility without accountability. A discovered agent can still operate with exposed credentials, unclear entitlement scope, or no lifecycle owner to approve removal. That leaves the organisation with an inventory that looks complete but does not actually reduce exposure or support audit evidence.

Q: Who should be accountable when an AI agent causes a security incident?

A: Accountability should sit with the human owner, platform team, or business function that granted and operated the agent. The identity may act independently, but governance cannot detach responsibility from the delegation chain. Programs should define ownership, escalation, and remediation paths before deployment so responsibility is clear when the agent's behaviour changes.

How it works in practice

Shadow AI discovery across endpoints and cloud

Shadow AI discovery is essentially identity discovery for unsanctioned AI tools, exposed credentials, and agent runtimes. On endpoints, that means finding local tools, their MCP servers, and leftover credential files. In cloud environments, it means enumerating agents plus the service principals, managed identities, app registrations, and service accounts they depend on. The technical shift is important because discovery becomes an inventory of actionable identities, not a passive scan. Once the artefacts are tied to owners and access paths, they can enter the same governance workflow as human identities and other NHIs.

Practical implication: build a discovery process that maps AI tools to owners, credentials, and connected identities before you attempt policy enforcement.

Agentic vaulting and short-lived credential exchange

Agentic vaulting changes how secrets are presented to agents. Instead of handing an agent the underlying key, the system exchanges a workload identity for a short-lived credential that is scoped to the task and kept separate from the sealed secret. That reduces secret persistence and narrows blast radius, but the deeper point is lifecycle alignment: the credential can be requested, approved, audited, and revoked using the same governance path as other access. Controls such as proof of possession and network scoping add binding between identity, location, and session.

Practical implication: prefer short-lived, scoped exchanges over persistent secret distribution for agent workloads and adjacent service accounts.

Runtime governance at the tool-call layer

Runtime governance is not the same as static policy or prompt filtering. The article describes an identity-aware gateway that evaluates each tool call against what data is reachable, whether untrusted content entered the session, and whether the action creates an exfiltration path. That is a control at the execution boundary, where the agent actually makes a request to use a tool. The significance is that authorisation becomes contextual and session-aware, which is closer to how agent behaviour changes in practice than traditional up-front entitlement checks.

Practical implication: enforce tool-call policy at runtime, not just at onboarding, especially where agents can reach private data or destructive actions.

NHI Mgmt Group analysis

Agentic identity governance is now a control-plane problem, not a point-product problem. The article is describing a model where discovery, secrets, runtime authorisation, and remediation all sit in one operational path. That matters because the failure mode in agentic environments is not a missing checkbox, but a broken handoff between inventory, access, and response. Practitioners should read this as a signal that AI agent governance will increasingly be judged by control continuity, not tool count.

Ephemeral credential trust debt: The article exposes a familiar assumption that credentials can be handed to a machine and managed later through human-paced review. That assumption fails when AI agents can acquire, use, and discard access quickly enough that review happens after the relevant action already occurred. The implication is that governance models built around durable access states need to be reconsidered for agent sessions that are operationally shorter than the audit cycle.

Agent runtime governance is where least privilege becomes meaningful for AI agents. Scoping an agent at provisioning time is not enough if the real risk appears in the tool call. The article correctly places the control at execution time, where data reachability, untrusted input, and outbound paths converge. That reinforces a broader market direction: identity teams will need runtime policy, not just identity records, to make agent governance enforceable.

Discovery only matters when it produces ownership and lifecycle action. The article moves beyond visibility by tying found agents and credentials into approval, certification, and deprovisioning paths. That is the right direction because unmanaged discovery alone creates another inventory, not governance. The practitioner lesson is that every discovered agent or secret should enter a lifecycle workflow with a named owner and a removal condition.

The agentic control plane shows where the market is heading: toward convergence of NHI, PAM, and AI governance. The article combines identity discovery, scoped credentials, runtime control, and remediation into one model because those controls are now interdependent. That does not eliminate specialist tools, but it does raise the bar for integration. Security teams should expect agent governance to converge with existing identity operations rather than remain a separate AI-only discipline.

From our research:

• Only 5.7% of organisations have full visibility into their service accounts, according to Ultimate Guide to NHIs.
• From our research: 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
• For a broader breach perspective, see 52 NHI Breaches Analysis, which shows how identity exposure turns into real-world compromise.

What this signals

Ephemeral credential trust debt is the right shorthand for what many teams will now face: access is issued fast, used fast, and audited too late. When the control plane spans discovery, vaulting, runtime policy, and remediation, identity teams can no longer rely on periodic review as the primary safeguard. The operational priority becomes reducing the time between privilege creation and privilege retirement, not just improving the review spreadsheet.

C1.ai's framing also points toward a convergence model for agent governance, where NHI controls, PAM controls, and AI runtime policy need to share the same workflow. That matters because a separate AI programme will tend to fragment ownership, while a unified identity workflow can attach evidence, approvals, and enforcement to the same record. For practitioners, the next step is aligning internal process design with frameworks such as the NIST AI Risk Management Framework and the OWASP Agentic AI Top 10.

For practitioners

• Map AI agents to governance owners Inventory discovered agents, MCP servers, credentials, and cloud runtimes, then assign each item to a business owner and control owner before granting access. The goal is to turn shadow AI into accountable identity records that can enter review and deprovisioning workflows.
• Replace persistent secrets with short-lived exchanges Use workload identity-backed credential exchange so agents receive scoped, time-bound access instead of underlying keys. Apply additional binding where possible, such as proof of possession and IP range restrictions, to reduce blast radius.
• Move authorisation to the tool-call boundary Enforce policy at runtime for every agent action that touches private data, untrusted input, or outbound transfer paths. Block, step up for human approval, or redact outputs when the call creates a material risk condition.
• Route findings into the existing identity workflow Feed risky agent and service-account findings into the same request, approval, audit, and ticketing paths used for other identities. Close the loop by removing access or resolving misclassification before the finding is considered complete.
• Reassess lifecycle controls for machine identities Review whether your certification and offboarding processes actually cover AI agents, service principals, managed identities, and service accounts. If the workflow only works for humans, it will miss the identities that now carry the highest automation risk.

Key takeaways

• Agentic AI governance fails when discovery, credentials, runtime policy, and remediation sit in separate workflows.
• The practical risk is not just visibility gaps, but access that outlives ownership and review.
• Security teams should align AI agent controls with existing identity lifecycle processes before agent use becomes normalised.

Key terms

• Agentic Control-Plane Drift: The gradual expansion of an AI workflow from a narrow task into broader infrastructure authority. The risk is not simply that the agent acts quickly, but that the control plane starts absorbing tenant creation, secrets, integrations, and source control into one trust boundary.
• Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
• Runtime Governance: Runtime governance is the set of controls that verify what a system or agent is actually doing after deployment. It combines monitoring, authorization checks, and access validation so teams can detect drift, misuse, or excessive privilege in motion rather than assuming build-time policy still holds.
• Ephemeral Credentials: Ephemeral credentials are short-lived access artefacts issued for a limited task or session. They reduce the window for abuse, but they only improve security when paired with strong scope limits, telemetry, and automatic revocation at task completion.

What's in the full announcement

C1.ai's full blog covers the operational detail this post intentionally leaves for the source:

• Step-by-step discovery coverage for AI tools, MCP servers, and exposed credentials across endpoints and cloud platforms.
• Product-specific examples of scoped credential exchange, proof of possession, and decoy credential handling.
• Runtime policy conditions used to block, hold, or redact agent tool calls when private data or exfiltration paths are present.
• Routing and remediation examples for ServiceNow, Jira, and PagerDuty integrations.

👉 C1.ai's full blog covers discovery, vaulting, runtime governance, and remediation details in one place.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.