AI Security Proof: 100% MITRE, Zero False Positives

Security teams don’t lose sleep over more alerts. They lose sleep over the ones they miss—because the signal was buried under noise, or because the attack hopped from endpoint to identity to cloud faster than the SOC could connect the dots.

That’s why a “100% detection” headline only matters when it comes with two other numbers that are harder to earn: 100% protection and zero false positives. In the 2025 MITRE ATT&CK® Enterprise Evaluations, CrowdStrike reported exactly that—across a cross-domain test that intentionally mirrors how modern intrusions really work.

For this AI in Cybersecurity series, I care less about vendor victory laps and more about what the result means operationally: why AI-native detection can produce high-fidelity outcomes, where teams misread MITRE results, and what to ask for in your own environment before you trust any “100%” claim.

Why the 2025 MITRE ATT&CK evaluation mattered more than usual

MITRE evaluations are useful because they don’t test a single malware file or a single endpoint event—they measure how a product behaves across real attack techniques mapped to ATT&CK tactics and techniques.

The 2025 edition raised the bar in a way that should grab every CISO and SOC lead:

• Cross-domain scope: endpoint + identity + cloud, across hybrid environments
• Cloud control plane tradecraft included for the first time (a big deal because many breaches now pivot through IAM and API actions)
• Reconnaissance added as a tested tactic, pushing detection earlier in the attack chain

Here’s the key operational point: cross-domain attacks punish tool silos. If your identity signals live in one console, endpoint telemetry in another, and cloud logs in a third, you can still detect each event—but you’ll struggle to prove it’s one coordinated intrusion until it’s too late.

MITRE’s expanded approach is essentially a public stress test for a modern SOC reality: Can you spot and stop an attacker who blends into “normal” behavior while moving between domains?

“100% detection” is nice. “Zero false positives” changes the economics.

Zero false positives sounds like marketing until you’ve run a SOC. Then you realize it’s a budget line item.

Every false positive has a cost:

• Triage time (often 10–30 minutes per alert, sometimes more)
• Context switching and analyst fatigue
• Slower response on genuine incidents
• Reduced trust in detections (“just another alert” syndrome)

MITRE’s evaluation includes noise tests—benign activity that should not generate alerts. In the source results, CrowdStrike highlighted that it did not report benign activity in the noise test, aligning with what customers actually want: security controls that don’t punish normal operations.

Here’s my take: AI in cybersecurity succeeds when it reduces human workload without reducing coverage. If AI improves detection but increases alert volume, the SOC doesn’t become more effective—it becomes more stressed.

A security tool that’s “accurate” but noisy is still operationally wrong.

What “technique-level detail” really buys your SOC

The MITRE results also called out 100% technique-level detail—identifying activity at the ATT&CK Technique or Sub-Technique level.

This matters because it turns “something suspicious happened” into:

• What the attacker did (the technique)
• How they did it (execution context)
• What it impacts (risk and blast radius)
• What you should do next (response path)

In AI-driven security operations, this is where automation becomes safe. High-quality labels and context are what allow playbooks to run without breaking your environment.

Cross-domain attacks: what MITRE simulated (and why it maps to 2025 reality)

MITRE’s 2025 emulation focused on two adversary styles that reflect where many enterprises are getting hit right now:

• SCATTERED SPIDER-style eCrime: social engineering, identity abuse, MFA bypass, remote access tooling, cloud exploitation
• MUSTANG PANDA-style espionage: long-dwell intrusion, living-off-the-land behavior, stealthy persistence, custom malware

If that sounds familiar, it’s because the most damaging incidents in 2025 rarely start with “a virus.” They start with:

• Valid credentials
• A session token
• A trusted tool used badly
• An API call that looks legitimate unless you correlate it

Why “living-off-the-land” is where defenders bleed

Attackers love LOLbins and dual-use tools because the tooling is legitimate—PowerShell, WMI, remote management software, administrative utilities. Blocking them outright breaks business.

So the real requirement isn’t “detect the tool.” It’s detect the intent.

That’s exactly where AI-driven behavioral analytics tends to outperform brittle rule stacks:

• It can model baseline behavior by host, user, role, time, and peer group
• It can score sequences (not just single events)
• It can correlate weak signals into a strong conclusion

Put plainly: rules are good at known bad. AI is good at “bad that looks normal.”

How AI-native security produces high accuracy without flooding alerts

Getting to 100% detection and zero false positives isn’t about one clever model. It’s usually the outcome of system design.

Here’s the architecture pattern I’ve found works in real enterprises—regardless of vendor:

1) Correlate across domains early, not after escalation

Many SOCs correlate late (after an incident is opened). Cross-domain correlation needs to happen at detection time.

Example sequence that often slips through siloed tools:

1. Unusual sign-in pattern (identity)
2. Privilege change or role assumption (cloud)
3. New instance launched or suspicious admin action (cloud)
4. Lateral movement or credential dumping attempt (endpoint)

Each domain alone might look “medium.” Together it’s critical.

2) Use prevention where it’s fastest: stop control-plane abuse in real time

Cloud control plane actions happen fast. If an attacker creates privileged roles, launches workloads, or alters firewall rules, minutes matter.

The MITRE scenario described real-time containment actions like denying further use of compromised credentials and shutting down an instance while preserving disk for forensics.

That’s the model to copy: automated containment with forensic preservation. If your automation destroys evidence, you’ll regret it during incident response.

3) Automate enrichment and triage, not just response

The “agentic SOC” trend is real, but most teams get it wrong by automating the last step (containment) before they’ve automated the boring middle (enrichment).

High-value triage automation includes:

• Asset criticality + owner lookup
• User risk scoring and recent auth history
• Cloud change history (who changed what, when)
• Process lineage on endpoints (parent/child chain)
• “Similar events” clustering across the fleet

This is where AI can shave hours off investigations—without taking risky actions.

How to evaluate MITRE results like a buyer (not a fan)

MITRE results can be genuinely helpful, but only if you interpret them correctly.

Here’s a practical checklist I recommend using in procurement or annual security reviews:

Ask these five questions internally

1. Which domains are we weakest in today—endpoint, identity, or cloud? Your gap matters more than a vendor’s overall score.
2. What’s our current false positive rate by category? If you can’t measure it, you can’t improve it.
3. How long does it take us to correlate identity + cloud + endpoint for one incident? If the answer is “manually,” you’re paying a hidden tax.
4. What percentage of detections arrive with enough context to act? “Alert with no story” is noise.
5. What do we auto-contain today, and what do we refuse to auto-contain? Define this before you buy.

Ask these four questions to any vendor claiming “AI-powered”

• What training/evaluation approach reduces false positives without suppressing true positives? (If they hand-wave, expect noise.)
• Can you show technique-level mapping with plain-English reasoning? Analysts need explainability, not just labels.
• How do you handle unmanaged hosts and lateral movement attempts? This is where identity attacks get traction.
• What happens during a cloud control-plane takeover attempt—what’s blocked, what’s alerted, what evidence is preserved?

My stance: “AI-powered” only counts if it improves outcomes you can feel—less triage, faster containment, fewer missed intrusions.

Practical next steps: make “zero false positives” your 2026 operating goal

December is planning season. If you’re setting 2026 SOC priorities right now, don’t frame the objective as “more alerts” or “better coverage.” Frame it as better coverage with less noise.

A simple operational plan:

1. Pick three alert types that generate the most churn (common picks: suspicious PowerShell, impossible travel, cloud admin actions).
2. Measure baseline triage time and escalation rate for each.
3. Add correlation requirements (identity + endpoint + cloud context must attach automatically).
4. Introduce two levels of automation:
   • Level 1: enrichment + case assembly
   • Level 2: containment only when confidence threshold is met
5. Review outcomes monthly: did mean time to acknowledge drop? Did escalations become more accurate? Did analyst satisfaction improve?

If you do only one thing: treat false positives as a security risk, not just an annoyance. They directly increase the odds of missing a real intrusion.

Where AI in cybersecurity is heading next

The MITRE 2025 results point to a direction I’m comfortable betting on: cross-domain, AI-native platforms that prioritize high-fidelity detections and fast containment.

The next frontier isn’t whether AI can find threats—it can. The frontier is whether AI can assemble a coherent investigation narrative across identity, endpoint, and cloud quickly enough that a small team can stop a fast attacker.

If you’re evaluating tools or modernizing your SOC, focus less on dashboards and more on this single question: When identity and cloud signals are weak individually, can your stack still call the attack early—and do it without drowning you in noise?