AI Agent Visibility for Identity Attacks: A Practical Playbook

In 2024, 79% of CrowdStrike detections were malware-free. That number matters because it points to what most SOCs are already feeling: attackers don’t need custom malware if they can take over identities, abuse SaaS permissions, and move across domains faster than humans can piece together the story.

Now add a new complication: AI agents. They’re getting deployed inside SaaS apps, DevOps pipelines, and internal workflows with privileges that look a lot like a power user (or a service account)… except they can act autonomously at machine speed. Most organizations can’t even answer the basic question: How many agents do we have, who owns them, and what can they touch?

This post is part of our AI in Cybersecurity series, where we focus on how AI detects threats, analyzes anomalies, and automates security operations. Here, the focus is practical: AI agent visibility and SIEM integration as the foundation for stopping identity-driven, cross-domain attacks.

Identity attacks win when defenders can’t see the “who”

Identity-led intrusions succeed for a simple reason: defenders lose the thread. One compromised account becomes a chain—OAuth grants, mailbox rules, cloud API calls, file shares, repository access—and every step looks “valid” if you’re only watching one system at a time.

Attack crews like SCATTERED SPIDER have made this painfully familiar: compromise a user, bypass or manipulate MFA, pivot into SaaS admin surfaces, then expand access laterally. The attacker’s best friend is fragmentation—separate tools, separate logs, separate teams.

AI agents widen that exact gap. They create new entities in your environment that:

• Authenticate (sometimes with long-lived tokens)
• Call APIs across multiple systems
• Access sensitive repositories and knowledge bases
• Trigger workflows that touch production

If you’re treating agents like “just another app integration,” you’re underestimating them. An AI agent is closer to a privileged identity with automation attached.

The myth: “We’ll secure agents once they’re mature”

Most companies get this wrong: they wait for agent programs to “stabilize,” and only then add governance. But security debt compounds quickly in identity land. Once agents proliferate across teams and tools, pulling them back into control becomes expensive and political.

The right approach is the opposite: inventory first, normalize visibility, then enforce guardrails as agents scale.

What “AI agent visibility” actually means (and what it should include)

AI agent visibility isn’t a dashboard that counts agents. It’s the ability to explain and control agent behavior the same way you expect to for human identities.

CrowdStrike’s latest Falcon Shield enhancements point to a model that’s worth copying even if you’re not using their stack: continuous discovery, classification, and correlation of agents across platforms, plus enforcement through automation.

A useful definition

AI agent visibility is the capability to continuously discover AI agents, map their privileges and actions, attribute ownership, and monitor agent-to-agent and agent-to-system behavior for anomalies.

For defenders, the “minimum viable visibility” should answer:

• Who created the agent? (owner and accountable human identity)
• Where is it deployed? (tenant, workspace, repo, environment)
• What can it access? (files, mailboxes, repos, tickets, customer data)
• What actions can it take? (write, delete, share externally, create OAuth apps)
• How does it authenticate? (token type, rotation, scope)
• What does “normal” look like? (baseline activity and peer comparisons)

Falcon Shield’s direction here is sensible: classify agents, map permissions, flag risky configurations (internet exposure, over-permissioning, repo access), and connect agent privileges back to the underlying identity for accountability.

Why agent-to-agent activity is the next real risk

The most overlooked problem is agent-to-agent chaining.

Once one agent can trigger another agent (or a workflow that triggers an agent), you can get a cascade that looks like legitimate automation. That’s great for productivity—and great for attackers.

A practical example I’ve seen in the field:

1. A “documentation” agent has read access to internal wikis and incident postmortems.
2. A “deployment” agent can open PRs and trigger CI/CD actions.
3. A “support” agent can access customer tickets and attachments.

Individually, each seems reasonable. In combination, a compromised token or abused OAuth grant can create a full pipeline for data access, code modification, and operational impact—without malware.

Visibility must include agent-to-agent interactions, not just “agent did X.”

Governance that works: detect risk, notify owners, suspend fast

Visibility without response becomes a reporting exercise. The whole point is to shorten the time from “this is weird” to “this is contained.”

Falcon Shield’s model—alerting owners and using automated workflows (via SOAR) to suspend risky agents—maps to what high-performing security programs do:

1) Treat agent owners like service owners

If an agent is risky, the owner should be paged the same way you page an on-call engineer for production issues. Agents are operational.

Good governance patterns include:

• Owner required (no orphan agents)
• Business purpose tag (why it exists)
• Approved scopes (what it’s allowed to access)
• Expiry dates for high-privilege tokens

2) Make “safe defaults” non-negotiable

Common configurations that should be blocked or heavily reviewed:

• Internet-exposed agents with broad scopes
• Agents with write access to code repositories and access to secrets/knowledge bases
• Agents allowed to create OAuth apps or grant permissions
• Agents with persistent tokens that don’t rotate

3) Automate containment for clear policy violations

If your policy says “no external file sharing from privileged agents,” don’t route it to a queue.

Automate it:

1. Suspend the agent
2. Revoke its tokens/sessions
3. Disable the associated user or service identity if needed
4. Create an incident with the timeline and evidence

This is where AI in cybersecurity becomes real: AI helps identify anomalies, and automation turns that into prevention.

Why SIEM integration matters more than another alert

Most SIEM problems aren’t about storage. They’re about context.

Falcon Shield’s integration with Falcon Next-Gen SIEM (streaming first-party SaaS telemetry into the SIEM) highlights a shift the industry needs: SaaS posture and SaaS events should live in the same investigation space as endpoint, identity, cloud, and network telemetry.

Here’s the operational win: cross-domain attacks stop being a scavenger hunt.

What improves when SaaS telemetry is native in the SIEM

When SaaS telemetry is directly available alongside other security domains, you can:

• Correlate OAuth consent events with suspicious endpoints or impossible travel logins
• Tie unusual file sharing to a known compromised identity pattern
• Spot SaaS admin changes right after MFA resets or helpdesk social engineering
• Hunt across domains using one timeline instead of stitching exports and screenshots

A single timeline is not a “nice to have.” It’s what keeps you from missing the pivot.

A concrete correlation scenario (what your SOC should detect)

Say your SIEM sees:

1. A user authenticates from a new location and device fingerprint.
2. Minutes later, the same identity grants a high-privilege OAuth permission to a new app.
3. That app (or agent) begins enumerating mailboxes and sharing files externally.
4. An endpoint alert shows token theft or suspicious browser session reuse.

If those signals are split across separate SaaS security tooling, endpoint tooling, and identity tooling, you’ll likely catch them late. If they’re correlated in one SIEM investigation layer, you can detect the pattern early and respond with confidence.

A 30-day rollout plan for AI agent security (what I’d do first)

If you’re responsible for security operations or identity security and you want progress in a month, focus on visibility + control, not perfection.

Week 1: Inventory and ownership

• Build or enable continuous AI agent discovery across your main SaaS platforms
• Require an owner and purpose field for every agent
• Identify agents with access to: source control, cloud drives, email, ticketing

Deliverable: a living inventory that your SOC can search.

Week 2: Privilege and scope baselining

• Categorize agents into tiers (low/medium/high privilege)
• Baseline “normal” behavior (time of day, volume of actions, target systems)
• Flag:
  • Over-permissioning
  • Internet exposure
  • External sharing capability

Deliverable: a prioritized “top 20 risky agents” list with owners.

Week 3: SIEM correlation and detections

• Stream SaaS telemetry into your SIEM with normalized fields
• Build 5–10 correlation rules focused on identity abuse patterns:
  • OAuth grant + new device + high-volume access
  • Admin role changes + MFA reset + new mailbox rules
  • Agent access spikes + external sharing + anomalous IP

Deliverable: detections that produce incidents, not noise.

Week 4: Automated containment

• Implement SOAR playbooks for clear violations:
  • Suspend agent
  • Revoke token/session
  • Disable associated identity (conditional)
  • Notify owner + open ticket

Deliverable: measured reduction in mean time to contain (MTTC) for SaaS/identity incidents.

Snippet-worthy stance: If your SIEM can’t correlate SaaS, identity, endpoint, and cloud signals in one place, you’re running an investigation with missing pages.

The bigger trend: AI needs governance as much as detection

Organizations are adopting agentic workflows because they work—especially as we head into year-end planning cycles and 2026 roadmaps where automation budgets tend to loosen. Attackers know this. They’ll target the gaps: tokens, OAuth grants, shadow agents, and poorly governed non-human identities.

The practical path forward looks like this:

• Discover agents continuously (don’t rely on self-reporting)
• Normalize the view across platforms (so investigations don’t stall)
• Tie every agent to an accountable identity (so ownership is real)
• Stream SaaS telemetry into your SIEM (so correlation is fast)
• Automate containment for policy violations (so response is consistent)

If you’re building your 2026 security roadmap, make AI agent visibility and SIEM correlation a first-class initiative—right next to endpoint hardening and MFA improvements. Identity is where modern intrusions scale, and agents are becoming the fastest-moving identities in the enterprise.

Where is your organization most exposed right now: unknown AI agents, over-permissioned SaaS identities, or gaps in cross-domain SIEM visibility?