Threat Intelligence Gets Strategic—AI Makes It Work

Most companies still treat threat intelligence like a “security team” tool: something you buy, pipe into a dashboard, and hope your analysts have time to use. That mindset is getting expensive.

The 2025 State of Threat Intelligence findings are blunt: 43% of security decision-makers now use threat intelligence to guide strategic planning and business investment, and 91% plan to increase threat intelligence spending in 2026. At the same time, data breaches involving third parties doubled from 2024 to 2025, and nearly half of teams cite poor integration as a major barrier. Translation: enterprises want intelligence to shape decisions, but many can’t operationalize it fast enough.

This post is part of our AI in Cybersecurity series, and here’s my take: the shift from defensive threat intel to strategic threat intel is real—and AI is the only practical way to scale it without hiring an army of analysts. Not because AI is magic, but because modern threat intelligence is too high-volume, too cross-functional, and too time-sensitive to run on manual workflows.

Threat intelligence is now a boardroom input

Threat intelligence has become a planning signal, not just an alert feed. When nearly half of leaders use it for investment and strategy, it’s no longer limited to IOC enrichment or SOC triage. It’s influencing:

• Which business units get security budget first
• Whether to accelerate identity modernization or network segmentation
• What third parties need stricter controls (or replacement)
• Which geographies and products carry higher cyber risk

Why this shift happened (and why it’s accelerating)

Three forces are pushing threat intelligence into the boardroom:

1. The blast radius expanded. Cloud, SaaS sprawl, and vendor ecosystems mean incidents aren’t contained to “IT.” They hit revenue operations, legal, brand, and customer trust.
2. Adversaries are faster. Cybercrime groups reuse successful playbooks quickly, and state-aligned campaigns don’t wait for quarterly planning cycles.
3. Executives want defensible decisions. When budgets are large—and getting larger—leaders need evidence for why they’re investing in one control over another.

Here’s a quote-worthy way to frame it internally: “Threat intelligence is the evidence layer for security strategy.”

Spending is rising, but ROI is still measured the wrong way

Enterprises are spending real money on threat intelligence—and they’re watching efficiency metrics to justify it. The report data points are telling: 76% invest $250k+ per year in external threat intelligence products, and 14% spend more than $1M. That’s before services.

The most common ROI measures tend to be “speed and efficiency” gains: faster investigations, faster triage, reduced analyst time. Those are valid, but incomplete.

A better ROI model: decision impact, not just SOC throughput

If threat intelligence is being used for strategic planning, then ROI has to include strategic outcomes. I’ve found it useful to measure threat intel value across three tiers:

1. Operational outcomes (SOC/SecOps):
   • Mean time to detect (MTTD) and mean time to respond (MTTR)
   • Alert reduction from improved prioritization
   • Analyst hours saved through automation
2. Risk outcomes (Security leadership):
   • Reduced exposure windows on critical assets
   • Reduction in repeat incident types (credential stuffing, BEC, ransomware precursors)
   • Control effectiveness improvements (e.g., phishing-resistant MFA adoption)
3. Business outcomes (CIO/CISO/Board):
   • Fewer material disruptions to revenue operations
   • Vendor risk reduction (fewer high-risk suppliers; tighter contractual controls)
   • Better capital allocation (spend aligned to highest-likelihood/highest-impact threats)

If your threat intelligence can’t change a decision, it’s just trivia with a subscription fee.

The integration problem is the real maturity gap

Maturity is improving, but integration is still the choke point. The report indicates 49% consider their threat intelligence maturity “advanced,” yet more than half still see themselves as less than advanced, and almost half cite poor integration.

That sounds contradictory until you see the common pattern inside enterprises:

• Threat intel is purchased by security leadership
• It’s consumed by a small group (often one team)
• It’s not embedded into workflows where decisions happen

What “poor integration” looks like in practice

Poor integration isn’t just “the API is hard.” It shows up as:

• TI feeds that don’t map cleanly to your SIEM detections
• High-confidence intelligence that never reaches IT, IAM, or procurement
• Analysts copying context from portals into tickets by hand
• Duplicate vendors doing overlapping things because nobody unified requirements

And that’s where AI in cybersecurity stops being a buzzword and becomes an operational necessity.

How AI fixes integration (when used correctly)

AI helps by translating threat intelligence into actions across tools and teams. Specifically:

• Entity resolution and correlation: Linking a threat actor, infrastructure, malware family, and targeted industry to your assets and telemetry.
• Automated summarization with guardrails: Turning long reports into task-ready briefs (what’s relevant, what changed, what to do next).
• Triage and prioritization: Ranking intel by business impact (critical apps, crown-jewel data, privileged identity exposure).
• Workflow automation: Creating detections, enriching alerts, opening tickets, and routing them to the right owner with context.

The bar isn’t “AI generates a nice summary.” The bar is: AI reduces the time from intelligence to decision.

From reactive defense to strategic intelligence: what AI enables

The strategic shift only works if threat intelligence becomes predictive and proactive. That doesn’t mean predicting the future with certainty. It means using patterns to identify what’s most likely next and preparing ahead of time.

Practical example: third-party breach risk

We already have a clear signal: third-party breach involvement doubled from 2024 to 2025. A strategic threat intel program should respond with more than vendor questionnaires.

An AI-enabled approach ties together:

• External intelligence on vendor exposures and incidents
• Your internal dependency map (which vendors connect to what)
• Identity and access pathways (SSO, service accounts, API keys)
• Monitoring signals (anomalous access patterns, impossible travel, new OAuth grants)

Result: instead of “Vendor X is risky,” you get “Vendor X is risky to us because it touches payroll and has privileged API access; here are the top three controls to reduce the blast radius in 30 days.”

Practical example: intelligence-driven detection engineering

Threat intelligence often dies in a PDF because converting it into detections is labor-intensive. AI can accelerate the translation from intel to detections by:

• Extracting behaviors and TTPs from reports
• Mapping them to your telemetry sources (EDR, cloud logs, identity logs)
• Suggesting detection logic candidates and test cases
• Highlighting gaps where you simply don’t have coverage

This is how threat intelligence becomes a strategy engine: it exposes coverage gaps, drives logging priorities, and informs platform investments.

Vendor consolidation is coming—use it to design a smarter stack

81% of organizations plan to consolidate threat intelligence vendors. That’s not just cost pressure; it’s a recognition that too many feeds and portals create noise, not clarity.

How to evaluate threat intelligence vendors in 2026 (a blunt checklist)

When consolidation starts, teams often default to “keep the biggest brand.” I’d rather see selection driven by outcomes and integration.

Prioritize vendors that can prove they support these capabilities:

1. Integration into your daily tools (SIEM, SOAR, EDR, cloud security, ITSM)
2. Context over volume (why it matters, who it targets, what it enables)
3. Workflow-ready outputs (tickets, detection content, playbook triggers)
4. Coverage transparency (what sources, what blind spots, how confidence is scored)
5. Support for strategic use cases (risk scoring, executive reporting, vendor risk)

Where AI should sit in the consolidated model

AI works best when it sits between intelligence and operations—acting like a routing and reasoning layer:

• Ingest intelligence from fewer, higher-quality sources
• Normalize and enrich it
• Map it to your environment (assets, identities, business processes)
• Push actions into the tools people already use

This is also where governance matters. Decide up front:

• What actions AI can automate without approval
• What requires human review (block rules, vendor escalations)
• How you’ll audit AI decisions (logs, prompts, outputs, approvals)

A 90-day plan to make threat intelligence strategic (with AI)

You don’t need a multi-year transformation to get strategic value. You need a tight loop between intelligence, prioritization, and execution.

Here’s a practical 90-day sequence I’ve seen work.

Days 0–30: Pick two use cases that matter to leadership

Choose use cases with measurable outcomes and executive relevance:

• Third-party risk prioritization
• Ransomware readiness (initial access vectors, top exposed systems)
• Executive protection (VIP phishing, credential exposure monitoring)
• Cloud threat prioritization (identity abuse, exposed storage, suspicious OAuth apps)

Define success in numbers: time to triage, time to patch, number of high-risk vendors reduced, reduction in exposed privileged accounts.

Days 31–60: Fix the plumbing (integration + routing)

Do the unglamorous work:

• Normalize entities (domains, IPs, brands, vendors, threat actor names)
• Connect TI to SIEM/SOAR/ITSM so intel generates work, not “FYI”
• Implement AI summarization for intel briefs with citations to original artifacts internally

Goal: threat intelligence should land as tasks with owners.

Days 61–90: Automate one workflow end-to-end

Pick one end-to-end automation and get it working reliably:

• Intel trigger → enrich → risk score → ticket → remediation tracking

Then add governance:

• Approval gates for high-impact actions
• Audit trails for AI outputs
• Monthly review with SecOps + Risk + Procurement

That last meeting is where strategic threat intelligence becomes real: decisions get made, and follow-through is visible.

Where this is headed in 2026

Threat intelligence is shifting from “what happened” to “what we should do next.” The report’s numbers—43% using TI for strategic planning, 91% increasing spend, and 81% consolidating vendors—all point to the same pressure: leaders want fewer tools, tighter integration, and clearer decisions.

AI in cybersecurity is the multiplier that makes that possible. Not because it replaces analysts, but because it compresses time: time to understand relevance, time to route work, time to take action, time to measure impact.

If you’re planning your 2026 security roadmap, ask one forward-looking question: What would change if threat intelligence could reliably trigger action across the business within hours—not weeks?