Tactical intelligence: leveraging AI to identify cyber threats

Cyber Threat Intelligence (CTI) is one of the cornerstones of proactive defense against digital attacks. At the tactical level, CTI focuses on equipping security teams with insights into malicious actors' tactics, techniques, and procedures (TTPs)-enabling faster, more accurate detection and real-time response.

AI is reshaping the way we analyze massive data sets in this space, boosting both threat pattern identification speed and precision.

Tactical yhreat intelligence and Its impact

Tactical intelligence delivers actionable insights into recent attacks' tools, tactics, and techniques. Its goal is to optimize detection rules on SIEM, EDR, and XDR platforms, enabling a more effective response and strengthening real-time defense strategies.

Key use cases

• Real-time correlation of Indicators of Compromise (IoCs).
• Development of SIEM/XDR detection rules based on attack patterns.
• Automated containment across next-generation firewalls (NGFWs) and cloud, network, and endpoint protection systems

AI in threat identification and mitigation

AI has significantly improved tactical CTI efficiency by automating the analysis of large data volumes, identifying emerging threats, and reducing false positives. Some of the most impactful applications include:

1. Machine learning for pattern recognition

Supervised and unsupervised machine learning models analyze network traffic and security events to detect anomalous behavior patterns.

2. Natural language processing (NLP) for threat analysis

NLP extracts key insights from threat intelligence reports, dark web forums, and threat feeds, automatically generating reports on new attack techniques.

3. Automated incident response

AI-powered Security Orchestration, Automation, and Response (SOAR) platforms execute predefined playbooks to mitigate threats within seconds.

Challenges to applying AI to CTI

Despite its many advantages, using AI in tactical threat intelligence also presents significant challenges:

• False positives and alert fatigue: Some models generate large volumes of alerts, many of which are benign—leading to analyst fatigue and reduced effectiveness.
• Advanced evasion techniques: Attackers are developing methods to bypass AI-based detection, such as adversarial machine learning.
• AI is in attackers' hands: Generative AI is already used to craft polymorphic malware and highly personalized, realistic phishing campaigns.
• Resource and expertise requirements: Implementing and maintaining AI solutions requires robust infrastructure, intensive processing capabilities, and specialized teams skilled in both data science and Cyber Security.

Use case: early ransomware detection with AI in a regional SOC

A regional SOC implemented a hybrid model combining supervised machine learning and behavioral analysis within its XDR platform to detect ransomware-related activity.

During the testing phase, the system analyzed 25 million daily events across endpoints and servers. The model was trained using both real-world and simulated attack data. It successfully identified early-stage ransomware behavior—such as unusual mass file access and suspicious processes like vssadmin.exe—with a 97.4% detection rate and a 42% reduction in false positives compared to traditional methods.

Thanks to automated SOAR playbooks, containment was triggered in under 60 seconds after detection, effectively halting ransomware spread and minimizing operational impact.

Recommendations

To fully harness AI potential in CTI, we recommend:

• Adopting explainable AI models to enhance threat detection transparency. Explainable AI helps Cyber Security analysts understand how and why specific decisions are made—critical for assessing alert reliability and fine-tuning models.
• Integrating threat intelligence with the MITRE ATT&CK framework to improve event correlation. This standardized framework categorizes attacker TTPs, helping analysts detect suspicious behaviors and prioritize responses more effectively.
  
  By correlating these events with a standardized framework such as MITRE ATT&CK, a clearer picture of threats can be obtained and responses prioritized more effectively.
• Leveraging SOAR platforms to automate security responses and reduce the operational burden on SOC teams. These tools execute predefined playbooks containing incidents in seconds, allowing analysts to focus on data interpretation and the continuous improvement of detection systems.

AI should be viewed as an enhancement—not a replacement—for human expertise.

Conclusion

AI integration into tactical threat intelligence has transformed the way organizations detect and mitigate cyberattacks. The ability to automatically identify, analyze, and respond to emerging threats enhances security postures and dramatically shortens incident response times.

However, AI is not a silver bullet. The role of a Cyber Security analyst remains irreplaceable. Human experience, intuition, and critical thinking are essential to interpret alerts in context, make strategic decisions, and continually refine detection models. AI should be seen as a powerful ally—not a substitute—for human judgment.

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