AI Security Benchmarks: Reading MITRE 2025 Results

Security teams don’t need another dashboard. They need fewer surprises.

That’s why the 2025 MITRE ATT&CK Enterprise Evaluation results matter—especially this year, when the test expanded beyond endpoint into identity and cloud control plane activity. CrowdStrike reported 100% detection, 100% protection, and zero false positives in the 2025 evaluation. Those are bold claims, but the bigger story isn’t the scorecard. It’s what a “perfect” run signals about where AI in cybersecurity is actually pulling its weight: high-fidelity detection across domains, plus automated containment that doesn’t create alert chaos.

I’ve found most organizations aren’t struggling with “not enough alerts.” They’re struggling with too many low-value alerts and too little time to connect identity, endpoint, and cloud signals into one coherent incident. MITRE’s cross-domain direction mirrors that reality—and it’s a useful forcing function for how you should evaluate any AI-powered security platform.

Why MITRE ATT&CK 2025 is a tougher test than most buyers assume

MITRE ATT&CK Evaluations aren’t a “winner” list; they’re a comparative, technique-driven look at how products behave under emulated adversary tradecraft. The 2025 test turned the difficulty up in three practical ways.

1) Cross-domain isn’t marketing anymore—MITRE made it mandatory

This year’s evaluation expanded across:

• Endpoint behaviors (malware and malware-free)
• Identity signals (valid account abuse, lateral movement patterns)
• Cloud (control plane actions and hybrid pivots)

That’s a big deal because modern intrusions rarely stay in one lane. A real attacker will bounce between an inbox rule, a token, an IAM role, an RMM tool, and a cloud instance launch faster than most teams can open a ticket.

2) Reconnaissance got included—and that changes how “early” you can stop attacks

MITRE added the Reconnaissance tactic for the first time in this evaluation. That moves the measuring stick earlier in the kill chain.

A detection that fires after persistence is established is better than nothing. But the real operational win is: spotting the setup steps—the enumeration, the probing, the first “is anyone watching?” moves that precede damage.

3) Alert efficiency is finally part of the conversation

Security buyers are tired of vendors claiming “we detect everything” while quietly producing thousands of noisy alerts.

MITRE’s approach includes visibility into alert volume and noise handling. One test (“Noise”) is meant to see whether tools flag benign activity. CrowdStrike stated it produced zero false positives, including not reporting benign events in the noise-focused protection test.

If you’re running a lean SOC (and most are), false positives aren’t an annoyance—they’re a budget line item.

What “100% detection” and “zero false positives” actually mean for an AI SOC

Perfect detection is attention-grabbing. Zero false positives is the part I care about.

Here’s the blunt truth: many security programs fail not because they can’t detect threats, but because they can’t operationalize detection. AI in cybersecurity only helps if it reduces human workload while improving outcomes.

High-fidelity detection is about context, not volume

CrowdStrike emphasized 100% technique-level detail, meaning detections map to specific ATT&CK Techniques/Sub-Techniques rather than vague “suspicious activity.”

That matters because technique-level context:

• speeds triage (“what’s happening?”)
• narrows scope (“where else did this technique appear?”)
• supports response decisions (“block, contain, reset credentials, isolate host?”)

AI is most valuable here when it can classify behavior correctly—especially for attacker tradecraft that looks legitimate.

Zero false positives is the difference between automation and chaos

Automated response is only safe when precision is high.

If your AI-driven platform auto-blocks the wrong thing, you’ve created a self-inflicted outage. If it can block the right things without flagging normal operations, you can confidently automate:

• identity risk actions (session revocation, step-up auth, credential lock)
• cloud containment (deny role escalation, terminate risky instances)
• endpoint isolation and process termination

In other words: accuracy is what makes AI-powered security automation politically possible inside most enterprises.

Snippet-worthy reality check: If your SOC can’t trust detections, it won’t automate responses—no matter how “AI-native” the pitch deck sounds.

Why cross-domain attacks are the real benchmark for AI security platforms

The evaluation emulated two adversary profiles that reflect what defenders are actually seeing: high-skill eCrime and state-sponsored long-dwell operations.

Scenario 1: eCrime tradecraft (SCATTERED SPIDER-style intrusion)

The emulated eCrime activity focused on social engineering plus identity abuse and cloud exploitation—techniques that are frustratingly effective because they don’t rely on obvious malware.

Key tradecraft themes included:

• MFA bypass and SSO abuse
• credential theft and session replay
• use of legitimate remote access tooling
• hybrid movement across identity and cloud

This is where “AI threat detection” either proves itself or becomes theater. A rules-only approach typically struggles because the attacker is using tools that are allowed.

What you want from an AI-enabled XDR platform in this scenario is:

1. Identity anomaly detection (unusual auth patterns, impossible travel, suspicious device context)
2. Correlation between identity events and endpoint activity
3. Cloud control plane visibility when the attacker pivots into the console
4. Containment actions that stop escalation fast (deny privileges, shut down rogue compute)

Scenario 2: state-sponsored tradecraft (MUSTANG PANDA-style long dwell)

State-aligned intrusions often blend:

• legitimate tool abuse (living-off-the-land)
• stealthy persistence
• custom or modified malware
• evasion tactics like encoded shellcode and reflective loading

AI helps here when it’s paired with strong telemetry and analysis pipelines—static analysis, sandbox detonation, and automated conversion of intel into detections.

The practical benefit isn’t “AI found malware.” It’s: AI shortened the time between suspicious artifact and enforceable detection, ideally without waiting on an analyst to handcraft rules.

What to look for in AI-powered endpoint, identity, and cloud protection

If your organization is using this MITRE news as a trigger to reassess tooling (or justify budget before year-end planning closes), focus on capabilities that translate into operational outcomes.

1) Can the platform distinguish malicious vs. legitimate dual-use tools?

Attackers love RMM tools and LOLbins because defenders can’t ban them outright. The test scenarios leaned heavily on this.

Ask vendors for proof of:

• behavioral detection for dual-use tooling
• policy controls that don’t break IT operations
• clear investigation context so analysts don’t waste cycles

2) Does identity security work even when the endpoint is unmanaged?

One of the hardest real-world problems is identity-driven lateral movement from unmanaged devices (contractors, personal devices, compromised home systems).

Your identity layer should be able to:

• detect anomalous sign-in behavior
• identify risky sessions and tokens
• enforce containment even when you can’t deploy an agent

If a platform only “sees” identity risk when the endpoint agent is present, it’s not cross-domain. It’s adjacent.

3) Can cloud containment happen in seconds—and is it forensically sane?

Cloud incidents escalate quickly because privilege changes and instance launches are instant.

The standard you should hold vendors to:

• real-time detection of suspicious IAM activity
• automated prevention of privilege escalation paths
• containment actions like disabling compromised access and stopping risky compute
• preservation of artifacts for investigation (snapshots/disks/logs)

Fast containment without evidence retention is a trap. You’ll “stop” the incident and lose the story.

4) Is AI used to reduce cases, not increase alerts?

This is the quiet KPI that matters.

A useful AI SOC approach:

• groups related signals into one case
• summarizes what happened in plain language
• provides recommended actions with confidence
• automates the boring parts (enrichment, scoping, first-line response)

If the AI feature mainly generates more narrative around the same flood of alerts, it’s not improving security operations—it’s decorating them.

How to use MITRE results in your buying (or renewal) decision

MITRE ATT&CK results are valuable, but only if you apply them correctly.

Use MITRE to test your assumptions, not to crown a champion

MITRE shows how a product performed under a defined set of emulations. Your environment has different identity providers, logging gaps, cloud configurations, and operational constraints.

What MITRE is great for:

• validating cross-domain visibility claims
• comparing detection depth (technique-level detail)
• evaluating noise handling and false positive behavior

What MITRE can’t do for you:

• guarantee your deployment will match lab conditions
• solve bad logging architecture
• replace incident response readiness

Run a “weekend test” that mirrors the MITRE cross-domain reality

If you’re serious about AI-powered threat detection and prevention, do a short, focused evaluation that answers questions that matter to your team:

1. Identity + endpoint correlation: Can we tie a risky login to endpoint behavior fast?
2. Cloud control plane coverage: Do we see privilege escalation attempts and suspicious launches?
3. Noise rate: How many alerts require human triage per day?
4. Response safety: What can be automated without breaking operations?

A platform that looks good in benchmarks but can’t reduce triage hours won’t deliver ROI.

Where this fits in the “AI in Cybersecurity” series

This MITRE milestone is a clean snapshot of where AI in cybersecurity is headed: cross-domain detection, prevention-first controls, and SOC workflows that prioritize case quality over alert quantity.

Over the past year, I’ve seen teams shift budget from “more point tools” to platforms that can connect identity, endpoint, and cloud signals and then take safe action quickly. Evaluations like MITRE 2025 are accelerating that shift because they reflect what attacks look like now—hybrid, credential-heavy, and designed to blend in.

If you’re planning your 2026 security roadmap, treat this as a practical challenge: How much of your defense still depends on humans manually stitching together identity logs, endpoint telemetry, and cloud events? The teams that answer “not much” are the ones that will move faster than attackers.