AI Security That Hits 100%: What MITRE 2025 Proves

A perfect security score sounds like marketing fluff—until it shows up in an independent evaluation that’s specifically designed to be uncomfortable.

In the 2025 MITRE ATT&CK® Enterprise Evaluations, CrowdStrike reported 100% detection, 100% protection, and zero false positives across an expanded, cross-domain test that covered endpoint, identity, and cloud. Whether you use CrowdStrike or not, the bigger story is this: AI in cybersecurity is starting to look less like a feature and more like the operating system of modern defense.

This post is part of our AI in Cybersecurity series, and I want to use the MITRE 2025 results as a practical lens: what the evaluation actually measured, why “zero false positives” matters as much as detection, and how to translate these lessons into vendor selection and SOC operations—especially heading into 2026 budget planning.

MITRE 2025 shows where attacks really happen: across domains

The most useful takeaway from MITRE 2025 is simple: the test finally looks like real incidents. Modern intrusions don’t respect tool boundaries. Attackers hop from endpoint to identity to cloud control planes because that’s where the easiest privileges are.

MITRE expanded scope in two ways that matter for security leaders:

1. Cloud tradecraft was introduced into the emulation (cloud control plane activity, not just workload telemetry).
2. Reconnaissance was added as a tested tactic, pushing vendors to detect earlier-stage activity—before the “obvious” breach signals appear.

That combination mirrors how breaches actually unfold:

• Recon and credential access start quietly.
• Identity abuse becomes the highway (valid accounts, MFA fatigue, SSO/session abuse).
• Cloud permissions and API activity become the payout (IAM role escalation, data access, persistence).

If your security stack can’t connect those dots quickly, your mean time to understand (MTTU) balloons—even if you technically “detected something.”

Why cross-domain evaluations matter more than endpoint-only scores

Plenty of products can look good in narrow tests. Cross-domain tests are harder because they expose the gaps between teams and tools:

• Endpoint sees a remote tool and flags it, but identity logs never get correlated.
• Identity sees a suspicious login, but the cloud trail of privilege escalation isn’t connected.
• Cloud sees a role creation, but the underlying endpoint session that initiated it is missing.

MITRE’s 2025 format pressures vendors to prove they can:

• Detect and prevent across domains
• Provide technique-level detail
• Keep alert volume manageable

That last point is where AI-driven cybersecurity either shines—or collapses under noise.

“Zero false positives” is the real SOC KPI hiding in plain sight

Most companies get this wrong: they treat false positives as an analyst inconvenience. In practice, false positives are a business risk multiplier.

Here’s the chain reaction I’ve seen repeatedly:

• More false positives → more triage time
• More triage time → slower investigation of real threats
• Slower investigation → higher dwell time
• Higher dwell time → higher blast radius, more systems touched, more data exposed

So when an evaluation highlights zero false positives, it’s not just a quality flex. It’s a statement about operational throughput.

The “noise test” matters because attackers hide in normal

MITRE’s Protection Test 6 (called out as a noise test in the source article) is valuable because it forces a product to do something non-trivial:

Don’t alert on benign activity—even when you’re actively hunting for bad.

Attackers increasingly rely on “looks normal” techniques:

• Living-off-the-land binaries (LOLbins)
• Admin tools
• Remote monitoring and management (RMM)
• Legitimate scripting and automation

If your tooling flags normal IT work as suspicious, you don’t just waste time—you train the SOC to ignore alerts. That’s how breaches slip through.

Good AI security doesn’t create more alerts. It creates fewer, better alerts.

What “100% detection” actually means (and what it doesn’t)

A 100% detection headline is easy to misread. The useful detail in the RSS content is that CrowdStrike reported 100% technique-level coverage—meaning detections mapped at the ATT&CK Technique or Sub-Technique level.

That’s a big deal because it changes the analyst experience:

• “Suspicious behavior” becomes “Credential Dumping” or “Valid Accounts”
• You get clearer context on how the attacker is progressing
• You can tie detections to playbooks and response automation

Detection without context is just telemetry

In real SOC work, detection quality is often defined by two questions:

1. Can an analyst understand what happened in under 5 minutes?
2. Can the SOC act without pivoting across five consoles?

Technique-level mapping pushes toward both outcomes, especially when paired with case management and correlation.

That said, one caution: MITRE evaluations aren’t a “winner list.” They’re a structured way to compare behaviors and visibility under a defined emulation. Your environment still decides whether those detections are usable at scale.

AI in cybersecurity: why it’s winning in identity + cloud tradecraft

The MITRE 2025 scenarios highlighted two patterns that keep showing up in incidents:

• Hybrid identity abuse (valid accounts, MFA bypass, access from unmanaged devices)
• Cloud control plane manipulation (IAM changes, backdoors, pivot instances)

These are exactly the areas where “traditional” detection struggles, because:

• Signatures don’t help much
• The tools used may be legitimate
• The sequence (behavior over time) matters more than a single event

AI works best when it models sequences, not isolated events

AI-driven detection is strongest when it does three things well:

1. Behavioral baselining: What’s normal for this user, host, role, and time?
2. Sequence awareness: What events, in what order, indicate compromise?
3. Cross-domain correlation: Do endpoint actions + identity logins + cloud API calls form an attack chain?

A concrete example (in the spirit of the MITRE cloud scenario described):

• Stolen session/credentials used to access cloud console
• IAM enumeration and privileged role creation
• New compute instance launched with elevated permissions
• Data access paths altered (exfil staging)

Individually, some of these steps can be “admin activity.” Together, they’re an intrusion.

If your SOC tooling can’t reason over the chain, you’re stuck arguing with logs instead of stopping the attacker.

How to use MITRE results in a buying decision (without getting fooled)

MITRE evaluation results are useful if you treat them like a technical due diligence input—not a purchasing shortcut.

Here’s a practical way to do it.

Step 1: Translate MITRE coverage into your top 10 attack paths

Most organizations don’t need “everything.” They need consistent coverage across the handful of paths that actually hit them:

• Identity-based lateral movement
• Ransomware staging via remote tools
• SaaS token theft
• Cloud role escalation
• Data access anomalies

Build a list of your top 10 attack paths and map them to ATT&CK techniques. Then evaluate whether the product demonstrates strong coverage and usable fidelity in those areas.

Step 2: Ask for alert volume and triage workflow evidence

“Zero false positives” is great in a test. Your environment is messier.

In demos and POCs, ask for specifics:

• How many alerts per day per 1,000 endpoints/users?
• What percentage of alerts are auto-closed or auto-correlated into cases?
• What does an analyst see first: raw events, or a case narrative?

If the vendor can’t answer with numbers (even ranges), assume your SOC will pay the price.

Step 3: Validate cross-domain correlation with an unmanaged device scenario

The RSS content highlights access attempts from an unmanaged host. That’s a realistic bypass route.

In your POC, simulate:

• Login from unmanaged device
• Access to a sensitive app or admin console
• Privilege escalation attempt or unusual admin action

Then measure:

• Time to detect
• Clarity of alert/case context
• Ability to contain (account lock, session revocation, host isolation, cloud response)

If the product only detects after the attacker lands on a managed endpoint, you have a visibility gap.

What this means for 2026 SOC strategy

MITRE 2025 is a strong signal that the SOC is shifting from “more tools” to “more decisions per minute.” That only happens when AI reduces noise and accelerates correlation.

Three operational moves matter most:

1. Prioritize prevention where it’s safe. If you trust the signal quality, automated containment in cloud and identity can stop breaches early.
2. Standardize on technique-level language. Align detections, playbooks, and reporting to ATT&CK techniques to reduce confusion and speed handoffs.
3. Measure efficiency, not just detection. Track alerts per analyst, time-to-triage, and percentage of incidents with full kill-chain context.

If you’re building an “AI in cybersecurity” roadmap, this is the point: AI should make security teams faster and calmer—not busier.

A practical next step: run a “noise audit” in your SOC

Want a quick way to apply the lesson behind zero false positives? Run a simple audit over the next 10 business days:

• Count total alerts generated
• Count alerts that required human triage
• Count incidents that led to action (containment, ticket, escalation)

Then calculate:

• Action rate = actioned incidents / total alerts
• Analyst load = triage alerts / analyst / day

If your action rate is low, your AI security conversation shouldn’t start with “Can we detect more?” It should start with “How do we trust the detections we already have?”

Breaches don’t win because defenders can’t detect anything. They win because defenders can’t decide fast enough.

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Forward-looking thought: As 2026 planning ramps up, the security stack that wins won’t be the one with the most dashboards. It’ll be the one that can connect endpoint, identity, and cloud behaviors into a single story—quickly enough to stop the second step, not the tenth.