Threat feeds, vulnerability reports, malware research, industry sharing groups, and government alerts generate a constant stream of data about the threat landscape. A single enterprise SOC may ingest dozens of commercial intelligence feeds, open-source indicators, vulnerability advisories, and industry ISAC alerts every day, often totaling millions of indicators of compromise per week.
The gap between translating threat intelligence into timely action is increasingly visible. According to the 2025 Verizon Data Breach Investigations Report, exploitation of edge devices and VPNs accounted for 22% of vulnerability exploitation actions, up dramatically from 3% the year before. At the same time, organizations remediated only 54% of those vulnerabilities, with a median remediation time of 32 days.
Meanwhile, attackers continue to move quickly once they gain access. Mandiant’s M-Trends 2025 report found the global median attacker dwell time was 11 days, and incidents discovered externally had a median dwell time of 26 days.
These numbers point to a growing reality: the problem is no longer simply discovering threats. It is acting on intelligence fast enough to matter.
Threat Intelligence Is Often Descriptive, Not Operational
Threat intelligence typically enters organizations in two forms.
The first is machine-readable intelligence, including indicators of compromise such as IP addresses, domains, file hashes, URLs, and registry keys. These artifacts can signal malicious activity and are often used by automated detection or blocking systems.
The second is human-readable intelligence, which includes reports on threat actors, campaigns, malware families, and vulnerabilities. These reports provide context about how attackers operate and what tactics they may use.
Both forms are valuable, but they often remain disconnected from operational workflows.
Indicators may arrive through multiple feeds. Reports may live in analyst portals or research PDFs. Analysts must manually correlate intelligence with telemetry and security controls. In many SOCs, analysts still copy indicators from threat reports into SIEM searches, manually pivot across logs, and build ad hoc queries to determine whether infrastructure associated with a threat campaign has appeared in their environment.
As Parthi Sankar, Technical Director at Anomali, explains, intelligence only becomes valuable when it can influence decisions.
“Intelligence isn't just something that's descriptive. It needs to be executable.”
Without the ability to operationalize intelligence, organizations end up with more data but little change in security outcomes.
The Challenge of Trusting Intelligence
One of the biggest barriers to operationalizing intelligence is confidence.
Threat feeds often include stale indicators, duplicated data, or false positives. Open-source intelligence sources can be especially noisy. Even high-quality intelligence may lack the context necessary to determine whether it matters to a specific organization. For security teams, the result is hesitation. Acting too quickly on unreliable intelligence risks blocking legitimate traffic or overwhelming analysts with alerts. Sankar notes:
“In order for it to be executable, we need to trust the data. It needs to come with context.”
Modern threat intelligence platforms increasingly address this challenge by applying machine learning to incoming intelligence streams. Indicators can be evaluated against multiple behavioral signals, including geolocation history, WHOIS records, passive DNS activity, and known malicious infrastructure patterns. These signals help determine whether an indicator is likely malicious or benign.
For example, benign domains typically generate large numbers of DNS requests over long periods of time. Malicious domains, by contrast, often show short bursts of activity before being abandoned.
By identifying patterns like these and assigning confidence scores to intelligence, organizations can prioritize high-confidence indicators for automated enforcement. The result is intelligence that security teams can trust enough to act on.
Intelligence Still Moves Too Slowly
Even when intelligence is accurate, operational workflows can still slow down the path to action.
Security analysts often need to construct complex queries across multiple tools to answer basic investigative questions:
- Which vulnerabilities are being exploited in my environment?
- Which threat actors are targeting my industry?
- What infrastructure is associated with a specific campaign?
Traditional security platforms require analysts to learn specialized query languages and navigate multiple interfaces to retrieve answers.
Natural language interfaces are beginning to change this dynamic. Instead of constructing complex queries, analysts can simply ask the platform questions and receive immediate results, summaries, or reports. This reduces the time required to move from investigation to response. It also allows analysts to spend less time on platform mechanics and more time on higher-level reasoning and decision-making.
Intelligence Must Reach the Entire Security Organization
Another operational challenge is organizational silos.
Historically, threat intelligence has lived within specialized cyber threat intelligence (CTI) teams. These analysts track adversaries, produce reports, and curate indicators. But many of the decisions influenced by intelligence occur elsewhere in the organization.
Vulnerability management teams need intelligence to prioritize patching. Red teams need intelligence to emulate real attacker techniques. Security operations teams need intelligence to guide investigations. Artificial intelligence and natural language interfaces are helping distribute intelligence across these teams. According to Sankar:
“AI to me is more about democratization, democratizing outcomes… allowing the power for intelligence to be utilized by other teams rather than the core CTI analyst team.”
By making intelligence easier to access and understand, organizations can embed threat insights into a wider set of security workflows. This shift turns intelligence from a specialized research function into a shared operational resource.
Decision Speed Is Becoming the Real Security Metric
The broader threat landscape reinforces why this shift matters.
Ransomware remains one of the most disruptive threats facing organizations. Verizon’s latest DBIR found ransomware present in 44% of breaches, a sharp increase from previous years.
At the same time, attackers are increasingly using automation and AI-assisted tooling to accelerate reconnaissance, credential theft, and infrastructure generation.
IBM’s 2025 X-Force Threat Intelligence Index reported an 84% increase in emails delivering infostealers, highlighting the growing scale of credential theft campaigns.
Attackers are speeding up and defenders must respond at the same pace.
“Attackers are noticeably speeding up and using AI themselves. Defenders must also speed up.”
Threat intelligence becomes valuable when it shortens the distance between insight and action. In today’s threat environment, the organizations that succeed aren’t just collecting more intelligence. Successful security teams are building systems that convert intelligence into operational decisions quickly and reliably.
Because in modern security operations, the real gap is no longer visibility, but execution.
Find out more at our From Intelligence to Action: Accelerating Threat Intelligence with AI on-demand webinar.
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