AI Threat Intelligence: What 520 Leaders Trust Now

Most security teams don’t have a “lack of data” problem. They have a lack of time problem.

By late 2025, it’s normal for a mid-size enterprise SOC to juggle thousands of alerts a day, dozens of intelligence feeds, and a vulnerability queue that never really shrinks. Add year-end change freezes, holiday staffing gaps, and attackers who don’t take vacation, and you get a familiar outcome: good intelligence arrives… after it would’ve mattered.

That’s why a survey of 520+ security leaders caught my attention. The numbers are blunt: 93% say AI and automation matter to their threat intelligence strategy, 75% are already using AI in the process, and 85% say those implementations meet or exceed expectations. This isn’t hype-fueled experimentation anymore. It’s operational.

This post sits inside our AI in Cybersecurity series, where we focus on practical uses of AI to detect threats, spot anomalies, and automate security operations in enterprises and government environments. Here’s what the survey signals, where AI threat intelligence actually helps, and the checks you need in place so “AI-powered” doesn’t turn into “AI-sourced risk.”

What the survey really proves (and what it doesn’t)

The clearest signal from the survey is trust is rising faster than governance. Respondents reported 86% trust AI-generated output, and 67% believe AI will reduce analyst workloads by 25% or more. That’s a major cultural shift from the “never trust the model” posture that dominated early genAI adoption.

But trust can mean two different things:

• Trust to draft: Summaries, first-pass triage notes, translations, report templates.
• Trust to act: Auto-blocking, auto-escalation, automated containment, policy changes.

Most programs are successful because they’re using AI heavily in the first category and selectively in the second. If you’re trying to jump straight to “AI auto-remediates everything,” you’ll burn political capital the first time an automated action disrupts a critical business workflow.

Here’s the stance I recommend: AI should be trusted to accelerate decisions, not replace accountability for them.

Where AI threat intelligence helps the most (the “high ROI” use cases)

AI delivers value when it reduces the most expensive resource in security: expert attention.

The survey highlights common wins—report summarization, threat scoring, and recommended actions—because these are the tasks that soak up analyst hours without always improving outcomes.

1) Report summarization that actually supports operations

A pile of threat reports isn’t intelligence until someone maps it to your environment. Modern AI threat intelligence workflows can summarize:

• What’s new (tactics, infrastructure, tooling)
• Who’s likely targeted (industry, region, tech stack)
• What to do next (detections to add, controls to validate)

The best teams go one step further: they require AI to produce structured outputs that can be used immediately, like:

• “Top 5 indicators with confidence + source type”
• “Mapped ATT&CK techniques and likely initial access paths”
• “Detection ideas in Sigma-style pseudo logic”

If you only ask for a paragraph summary, you’ll get something readable—but not operational.

2) Threat scoring that’s tied to your reality, not a generic number

Threat scoring is useful only when the score reflects your exposure.

AI can help by fusing:

• External signals (actor chatter, exploitation trends, infrastructure reuse)
• Internal signals (asset criticality, identity posture, EDR telemetry)
• Time sensitivity (active exploitation vs. theoretical risk)

A practical scoring model many enterprises can implement quickly:

1. Exploit momentum (is exploitation observed in the wild?)
2. Environmental match (do we run the affected tech?)
3. Blast radius (what happens if it’s compromised?)
4. Control coverage (do we already have detection/prevention?)

AI doesn’t “decide” priority. It justifies priority with a traceable rationale—so humans can agree, disagree, and tune.

3) Recommended actions that close the loop

“Recommended actions” becomes a real capability when it’s connected to workflows.

For example, when AI identifies a relevant emerging threat, it should produce:

• A recommended action
• The owner (SOC, vuln mgmt, IAM, networking)
• The system to execute in (SIEM, SOAR, ticketing)
• A measurable success condition

If your AI can’t name an owner and a success condition, it’s not recommending an action—it’s writing advice.

The real benefit isn’t efficiency. It’s earlier detection.

Efficiency is the first win everyone talks about because it’s easy to measure: fewer hours writing reports, faster enrichment, quicker triage.

But the strategic advantage is different: AI makes threat intelligence usable earlier in the attack lifecycle. That’s where the payoff is biggest.

When AI reduces analyst workload, teams can spend that freed time on:

• Predictive threat hunting (searching for early signals before incidents)
• Pre-attack validation (confirming controls and detections before exploitation spikes)
• Exposure management (prioritizing internet-facing risk based on current adversary behavior)

One quote from the discussion around the survey nails the intent: freed-up time should go to being more strategic, not shrinking teams. I agree. If AI reduces toil, the right move is to reinvest those hours into proactive security.

A line I use internally: If automation only makes you faster at being reactive, you’ve missed the point.

A practical implementation blueprint (so you don’t create “AI theater”)

Plenty of organizations “add AI” and still feel stuck. That’s usually because they deploy a chatbot, not a system.

Here’s a straightforward blueprint that works in enterprise and government environments.

Step 1: Pick one workflow and measure it

Start with one: intel-to-detection, vuln prioritization, or alert triage.

Define baseline metrics such as:

• Time from intel receipt to internal distribution
• Time from intel to new/updated detection content
• Analyst time spent per intel item
• False positive rate for AI-generated enrichment

If you can’t measure before/after, you can’t defend the budget when procurement season hits.

Step 2: Force structured outputs

Require AI outputs in consistent fields. Example schema:

• Threat / campaign name
• Relevance score (with explanation)
• Affected technologies
• Likely attacker objectives
• Observables (IOCs) vs. behaviors (TTPs)
• Recommended detections
• Recommended mitigations
• Confidence + why

Structure turns AI from “helpful text” into actionable threat intelligence.

Step 3: Put humans in the approval path—on purpose

A strong model for trust is tiered automation:

• Tier 1 (auto): Summaries, tagging, deduplication, translation, enrichment.
• Tier 2 (approve): Ticket creation, watchlist updates, detection drafts.
• Tier 3 (human-only): Blocking actions, containment, policy changes.

This reduces risk without slowing everything down.

Step 4: Keep provenance and auditability

If you want AI in cybersecurity to survive internal audit, you need to preserve:

• What inputs were used (sources, timestamps)
• What the model generated
• What the analyst changed
• What actions were taken downstream

This matters even more in government and regulated industries where explainability isn’t optional.

The trust problem nobody talks about: “confidently wrong” intel

The survey’s 86% trust figure is encouraging, but it also raises a red flag: models can be persuasive even when they’re wrong.

In threat intelligence, “wrong” has consequences:

• You block legitimate infrastructure and disrupt operations.
• You chase the wrong actor and miss the real intrusion.
• You drown in indicators that aren’t relevant to your environment.

Three safeguards that work in practice:

1. Grounding rules: AI must separate “observed facts” from “inferred hypotheses.”
2. Confidence must be justified: Not “high confidence,” but “high confidence because the infrastructure overlaps with X and Y.”
3. Behavior-first bias: Prefer TTP-driven detections over IOC-only lists, since indicators expire and get reused.

Trust isn’t a feeling. In a mature threat intelligence program, trust is the result of repeatable validation.

People also ask: What should we automate first?

Automate first what’s repetitive, high-volume, and low-regret. In most security operations teams, that means:

• Summarizing and routing threat reports
• Enriching alerts with context (asset criticality, geo, known bad infrastructure)
• Normalizing and deduplicating intelligence items
• Drafting detection logic for analyst review

Save “high-regret” actions—blocking, quarantining, disabling accounts—for later phases with clear guardrails.

What to do next (especially heading into 2026 planning)

Budget conversations happen fast at year-end, and security leaders are under pressure to show ROI. The survey offers solid social proof: AI in threat intelligence is already mainstream, and most implementations are meeting expectations.

Your next step shouldn’t be “buy an AI tool.” It should be: choose one threat intelligence workflow, define success metrics, and implement tiered automation with auditability. That’s how you turn AI from a pilot into an operational advantage.

The bigger question for 2026 is simple: when attackers speed up, do your intelligence and response workflows speed up too—or do they stay dependent on human bottlenecks? If you’re still relying on manual summarization and ad-hoc prioritization, you already know the answer.