AI Agent Visibility: The New Identity Security Layer

In 2024, 79% of detections were malware-free—a blunt reminder that attackers don’t need payloads when they can steal a session, hijack an OAuth grant, or socially engineer an MFA reset. That shift is why identity security has become the practical center of gravity in enterprise defense.

Now add a 2025 reality: AI agents are showing up everywhere—inside SaaS apps, copilots, workflow automations, code assistants, chat-based IT helpers, and “quick experiments” that quietly become production dependencies. These agents act like users, but they don’t behave like humans. They run nonstop, call APIs at machine speed, and often end up with privileges nobody can clearly explain two weeks later.

This post is part of our AI in Cybersecurity series, and it makes a simple argument: if your SOC can’t see AI agents as identities, you’re building detection on a blind spot. Falcon Shield’s new AI agent visibility and its native streaming of SaaS telemetry into a next-gen SIEM points toward a better operating model—one where identity, SaaS, and agent behavior are analyzed together instead of in separate tools.

AI agents are “non-human identities” with real privileges

AI agents should be treated as identities because they authenticate, access data, and take actions—often with more consistency and scale than a human user. If you’re already managing service accounts and API tokens as non-human identities (NHIs), AI agents belong in that same governance bucket.

The problem is that AI agents don’t arrive through the usual IAM front door. They’re created inside SaaS platforms, spun up by power users, granted access via connectors, and authorized through OAuth or app permissions that look “normal” in isolation.

Here’s what makes AI agents uniquely risky compared to typical automation:

• They’re permission magnets. People over-grant access to “get it working,” then forget to roll back.
• They bridge domains by design. A single agent might connect email, file storage, ticketing, CI/CD, and a CRM.
• They’re hard to inventory. Even mature organizations can’t answer: “How many agents do we have, and what can they touch?”
• They create new lateral movement paths. An attacker doesn’t have to land on an endpoint if they can commandeer an agent that already has the keys.

Snippet-worthy stance: If an AI agent can read sensitive data and trigger workflows, it’s not “just an app.” It’s a privileged identity.

What “AI agent visibility” needs to look like in practice

Visibility only matters if it’s operational—meaning it answers who, what, where, and why in a way your SOC can act on. The RSS update highlights Falcon Shield’s direction: automatically discovering and classifying agents, mapping creator and access, identifying risky configurations, and correlating agent privileges to a human or service identity.

That’s the right shape of solution, because the day-to-day questions security teams actually need answered are concrete:

1) Inventory: “What agents exist across our SaaS stack?”

A real inventory is continuous, not quarterly. New agents appear during product launches, end-of-year workflow crunches, and M&A integrations. And yes—December is when a lot of “temporary” automations get rushed in to close the year.

A useful inventory includes:

• The platform where the agent lives (and where it’s deployed)
• Owner/creator and the group responsible
• The identity context (linked user/service account)
• The connectors it uses (drive, email, repo, ticketing, etc.)

2) Scope: “What data can it access and what actions can it take?”

The highest-risk pattern I see in enterprises is an agent with:

• access to a code repository
• permission to read/write files in shared drives
• ability to send messages or emails
• access to secrets-adjacent systems (CI variables, ticket attachments, knowledge bases)

Not because any one permission is catastrophic, but because the combination becomes a breach workflow.

3) Behavior: “Is it acting outside expected parameters?”

This is where AI in cybersecurity stops being a buzzword and becomes a control. Agent behavior lends itself to anomaly detection because agents are repeatable: frequency, timing, target resources, API patterns, and data destinations.

Falcon Shield’s mention of agent-to-agent activity visibility is especially relevant. If agents can call other agents (or trigger workflows that trigger other workflows), the result is a fast-moving chain that needs correlation—humans can’t reconstruct it manually in time.

4) Response: “Can we contain it fast without guessing?”

Containment has to be automated at the identity layer:

• Suspend the agent
• Disable or step-up auth on the associated identity
• Revoke tokens / invalidate sessions
• Alert the agent owner with the exact risky setting and recommended fix

The RSS content calls out suspending agents and notifying owners through SOAR workflows. That’s the difference between “we saw something weird” and “we reduced blast radius in 90 seconds.”

Why SaaS telemetry belongs inside your next-gen SIEM

If your SIEM can’t ingest and correlate first-party SaaS telemetry with identity and endpoint signals, it will miss cross-domain attacks. Modern intrusions rarely stay in one place. They hop from:

• SaaS identity provider →
• email tenant →
• file sharing →
• cloud control plane →
• endpoint access

The update’s core operational move is streaming first-party SaaS telemetry from SSPM into a next-gen SIEM so detections, investigations, and threat hunting happen on a unified dataset.

That matters because most “SaaS investigations” still look like this:

1. Analyst checks a SaaS admin portal
2. Copies timestamps into a spreadsheet
3. Tries to align them with SIEM logs
4. Discovers log gaps or mismatched identities
5. Spends hours rebuilding a timeline

Attackers love that workflow. It’s slow, and it creates uncertainty.

A concrete example: OAuth abuse that doesn’t touch malware

A common SaaS pattern:

• A user authorizes a malicious or overly broad OAuth app
• The app reads mailboxes, exports files, or creates inbox rules
• The attacker maintains persistence via tokens

If your SIEM sees only endpoint events, you’ll miss the key actions. If your SaaS posture tool sees only configuration drift, you’ll miss the live exploitation.

A unified SIEM view changes the question from “Did anything bad happen on the endpoint?” to “What sequence of identity + SaaS + data actions indicates account takeover or consent hijacking?”

A practical hunting mindset for 2025

When SaaS telemetry is searchable alongside other domains, your hunts can be more specific and less noisy:

• Unusual file sharing immediately after a new token grant
• Impossible travel plus privileged SaaS actions plus new automation creation
• Agent created by a user who has never built automations before
• Repo access spikes initiated through SaaS connectors rather than developer tooling

This is also where AI-assisted detection actually helps: it can cluster behavior across thousands of identities and agents and highlight the “this doesn’t fit” patterns without writing 200 brittle correlation rules.

AI-driven identity security: the operating model that scales

Identity security in 2025 isn’t a single tool—it’s a loop: discover → baseline → detect → respond → audit. Falcon Shield’s direction aligns with what works in practice: treat every identity (human, non-human, AI agent) as governable and observable.

Here’s a workable approach you can apply even if you’re not using CrowdStrike—use it as a checklist for evaluating any vendor stack.

Step 1: Build an “agent-as-identity” policy

Write it down. Make it enforceable. Include:

• Ownership required for every agent
• Approved connector list (and a process for exceptions)
• Permission tiers (least privilege by default)
• Token lifetime and re-auth requirements

If your program can’t answer “who owns this agent,” you don’t have an AI governance program—you have hope.

Step 2: Define 5 high-signal detections you actually want

Skip the 300 “maybe interesting” alerts. Start with a small set you’ll respond to every time:

1. Agent gains new privileged connector (drive/email/repo)
2. Agent is internet-exposed or publicly reachable
3. Agent created by a user with no prior automation history
4. Agent accesses code repositories outside its team scope
5. Sudden data egress behavior (bulk reads, exports, unusual sharing)

Step 3: Automate containment for the top two scenarios

Pick two scenarios where speed matters most:

• Suspected token theft / session hijack
• Suspected over-permissioned agent misuse

Your SOAR action should be boring and reliable: revoke token, suspend agent, force re-auth, notify owner, open incident ticket with context.

Step 4: Audit like you mean it

Quarterly audits are too slow for agent sprawl. Do continuous controls:

• Daily review of new agents and permission changes
• Weekly review of high-privilege agents and dormant agents
• Monthly review of cross-domain connectors and data scopes

One-liner for leadership: If you can’t continuously inventory AI agents, you can’t credibly claim least privilege.

What to ask your security vendor (or internal team) right now

The best time to test AI agent visibility is before an incident, not during one. Here are questions that reveal whether you’re protected or just instrumented.

“Can we see all AI agents across SaaS platforms in one place?”

If the answer is “per platform,” expect blind spots and slow investigations.

“Can we map an agent to a human owner and to the identity it operates under?”

Accountability stops shadow automation.

“Can we stream SaaS telemetry into the SIEM with enough fidelity to investigate?”

If the SIEM only gets summaries, you’ll still be pivoting between consoles.

“Can we automatically suspend an agent and revoke the associated sessions/tokens?”

Response without identity actions is mostly paperwork.

“Do we get agent-to-agent activity visibility?”

Agent chains are where small misconfigurations become big incidents.

Where AI in cybersecurity is heading next

AI agents are going to multiply in 2026 because the business case is straightforward: they reduce manual work and speed up decisions. Security can either fight that adoption (and lose), or set guardrails that make it safe.

Falcon Shield’s push toward centralized AI agent visibility and native SaaS telemetry in a next-gen SIEM fits the direction I trust: unify identity, SaaS, and behavior analytics so investigations don’t stall out across tool boundaries.

If you’re building an AI in cybersecurity roadmap for next year, make this one of your non-negotiables: treat AI agents as first-class identities, and put their telemetry where your SOC already works. The orgs that do this will be the ones that can scale AI adoption without scaling breach risk.

Where are AI agents showing up in your environment right now—and who would notice first if one started behaving like an attacker?