Discover how an agent identity management tool resolves effective authority, detects orphaned agents, and enforces deterministic guardrails across 8+ AI platforms.
Security teams already manage human identities through IAM platforms and non-human identities through secrets managers and service account governance. Neither discipline covers AI agents adequately, and the gap is widening fast.
Agent identity management is the practice of assigning, tracking, and governing the identities of AI agents as distinct principals inside an enterprise environment. It answers four questions for every agent in the environment: who created it, who owns it, what platforms and data sources it connects to, and what it can actually do at runtime.
This discipline is distinct from human IAM for a structural reason. Human IAM assumes lifecycle events, quarterly access reviews, and interactive authentication. Agents do not trigger those events. They operate continuously, inherit credentials from their creators, and take actions without human review at each step. Traditional IAM programs have no mechanism to catch that behavior.
It is also distinct from traditional non-human identity management, which focuses on service accounts, API keys, and OAuth tokens as static credential objects. Agents are not static. They are probabilistic systems acting as machine insiders, making decisions and taking sequences of actions across multiple SaaS applications in ways that compound access and blast radius with every step. Managing a service account means rotating a credential. Managing an agent identity means understanding what that agent can do, on whose behalf, across which downstream systems, and whether any of that is policy-aligned.
The financial services sector reports the highest rate of AI-related incidents across industries precisely because its agents operate with administrative-level access to regulated data. Recognizing AI agents as distinct identities, enforcing least privilege access, and ensuring comprehensive visibility of agent actions are now baseline requirements for any serious AI agent security program.
Security teams evaluating agent identity management tools in 2026 are comparing against a market that includes human IAM vendors adding agent tabs to existing dashboards and point solutions that inventory one platform at a time. Neither approach produces the cross-platform, runtime-aware picture that agent security requires.
The capabilities that matter fall into five categories.
CapabilityWhat It DoesWhy It MattersMulti-platform agent inventoryDiscovers every agent across Copilot Studio, Agentforce, Bedrock, Vertex AI, n8n, ChatGPT Enterprise, and more from a single interfaceYou cannot govern what you cannot see. Shadow agents on unmonitored platforms carry the same risk as sanctioned ones.Named ownership and attributionMaps each agent to its creator and current owner, including disabled accountsOrphaned agents keep running after their creator leaves. No owner means no accountability and no remediation path.Effective authority mappingResolves what the agent can actually do inside each connected SaaS application, not just what its configuration saysConfiguration is not reality. The gap between theoretical configuration and effective authority is where privilege escalation lives.Runtime visibilityCaptures what agents actually do at runtime via native platform APIs and webhooks, not retroactive log reviewPosture signals tell you what could happen. Runtime truth tells you what did happen, what data was accessed, and whether it succeeded.Knowledge graph correlationCorrelates agent configuration, SaaS entitlements, identity provider data, and MCP server connections into a single authority mapNo single data source produces this picture. The knowledge graph is the prerequisite for toxic combination detection and blast radius scoring.
The knowledge graph is the mechanical advantage that separates a true agent identity management tool from a basic inventory list. It produces a living map of every agent's effective authority across the entire enterprise SaaS fabric, updated continuously as configurations, entitlements, and usage patterns change.
One enterprise discovered 377 Microsoft Copilot agents running in its environment through a security assessment. None of those agents were tracked by the security team before that assessment. Another enterprise had 2,500 agents created across platforms before any inventory existed. These are not edge cases. They are the current baseline condition for organizations that have deployed AI agent builders without a parallel governance program.
Complete agent inventory across platforms is the starting point. An effective agent identity management tool discovers agents across Copilot Studio, Salesforce Agentforce, Amazon Bedrock, Google Vertex AI, n8n, ChatGPT Enterprise, Azure AI Foundry, and others from a single interface. Agents built on platforms outside that inventory remain invisible, and invisible agents carry the same blast radius as visible ones.
Attribution to creator and owner resolves the accountability question. Every agent was built by someone. That person's credential level at the time of creation determines the agent's inherited permissions. Knowing who built an agent, and whether that person still works at the organization, is not an administrative detail. It is a security-critical data point.
Sanctioned versus unsanctioned classification separates agents that security and IT have reviewed from shadow agents that business users deployed without oversight. Shadow AI agents are not inherently malicious. They are simply unreviewed, which means their permissions, connections, and data access have never been evaluated against policy.
Orphaned agent detection is where inventory becomes urgent. An orphaned agent is one whose creator or owner account has been disabled, but the agent continues running with the inherited credentials of that former employee or contractor. The agent did not change. The human who owned it left. But the agent still holds tokens, still connects to SaaS applications, and still executes actions. No existing offboarding workflow catches this automatically without an agent identity management tool that monitors owner account status continuously.
Knowing that an agent exists is necessary. Knowing what it can actually do is what makes identity management actionable.
Theoretical configuration is what most tools see. It is the agent's setup: which connectors are attached, which permissions are declared, which workflows are defined. Configuration is not reality. The actual access an agent exercises inside a connected SaaS application, after all entitlements resolve, is its effective authority. Those two states diverge in ways that create serious risk.
The clearest example is maker mode. When an agent is built using the creator's credentials, any user who invokes that agent executes actions at the creator's privilege level. A user without Salesforce access can invoke a Copilot Studio agent built in maker mode by a Salesforce administrator. That user now accesses CRM records, pipeline data, and customer information they have no right to see. The agent did nothing technically wrong. Your IAM controls were bypassed entirely. Effective authority mapping catches this because it correlates the invoking user's identity, the agent's inherited credentials, and the actual SaaS entitlements that resolve at runtime.
The knowledge graph produces this correlation. It connects agent configuration data, identity provider records, SaaS application entitlements, and connected data source connections into a single authority map. No single data source contains all of this. The knowledge graph is the only structure that can answer: what can this agent actually execute, on whose behalf, with what downstream reach, and whether any of that is policy-aligned.
Toxic combinations are where risk scoring becomes critical. A single medium-severity risk factor on an agent, such as org-wide accessibility, is manageable. A single medium-severity factor like a disabled creator account is also manageable in isolation. When those two factors appear on the same agent simultaneously, the combination is critical priority. The agent is publicly accessible, running with orphaned credentials, and no owner exists to remediate it. That is a toxic combination, and it requires a tool that scores compounding risk factors together, not individually.
Blast radius is the scope of damage that agent can cause given its current effective authority. An agent with read-only access to one internal knowledge base has a small blast radius. An agent with admin-level Salesforce access, connected to an external webhook, accessible org-wide, with a disabled creator account, has a blast radius that spans your entire CRM. Blast radius scoring is the output of effective authority mapping combined with toxic combination detection, and it is what enables security teams to prioritize remediation rather than chase every individual alert.
Security teams comparing agent identity management tools in 2026 need concrete requirements. These are the capabilities that separate tools worth evaluating from those that will leave visibility gaps.
Multi-platform coverage is non-negotiable: an agent identity management tool that covers Copilot Studio but not Agentforce, or Bedrock but not Vertex AI, produces a partial inventory. Partial inventories create false confidence. Require coverage across at least eight major agent platforms before any other evaluation criterion matters.
Runtime visibility, not just posture, is the core differentiator: posture-based tools show theoretical configuration. They tell you what an agent is set up to do. Runtime visibility shows what the agent actually did, which data it accessed, and whether its actions were policy-aligned. Security teams that rely on posture alone are ghost chasing, reviewing configuration signals with no evidence of what actually happened. Require that any tool you evaluate captures agent behavior at runtime via native platform APIs and webhooks.
An identity graph is required for effective authority mapping: a tool that lists agents without correlating their configuration to SaaS entitlements and identity provider data cannot produce effective authority. It can only produce theoretical configuration. The identity graph is the mechanism that closes that gap.
Deterministic guardrails enforce policy on probabilistic agents: AI agents are non-deterministic by design. They can deviate from intended behavior. Deterministic guardrails apply fixed, predictable enforcement rules at runtime, cutting off action options before damage occurs. Require that any tool you evaluate can enforce guardrails via native platform integrations, not as an inline proxy sitting between your agents and your SaaS applications.
No requirement to be an inline proxy: a tool that requires sitting inline between agents and SaaS applications introduces latency, creates a single point of failure, and requires IT sign-off for every SaaS integration. The security team should be able to deploy and operate an agent identity management tool independently, without becoming a network dependency for every AI workflow in the organization.
Where to start: begin with inventory. Before any governance conversation is possible, security teams need a single pane of glass showing every agent, its creator, its owner status, and its connected platforms. That inventory is the prerequisite for effective authority mapping, toxic combination scoring, and blast radius prioritization. An AI agent risk assessment is the fastest path to that starting inventory, and it surfaces the orphaned agents, shadow agents, and maker mode configurations that most organizations do not know they have.
Find out which agents are running in your environment right now. Obsidian's AI agent risk assessment produces a complete inventory, maps effective authority, and surfaces toxic combinations in a single pass. Start your assessment