How AI is Driving the Evolution of Threat Intelligence

The widespread use of generative AI (GenAI) is transforming the field of threat intelligence, significantly accelerating capabilities for data analysis, threat prediction, and automated response. AI-driven threat analysis models can process vast amounts of data at lightning speed (petabytes in seconds!), identify subtle, understated patterns, and generate insights that would be difficult or impossible for human analysts. AI can also create realistic threat scenarios for training and testing, improving the readiness of security teams.  

AI advancements in the field of threat intelligence are currently playing out on strategic, operational and tactical levels.

AI and Strategic Threat Intelligence

AI tools can significantly enhance the value of strategic threat intelligence by automating the analysis of massive datasets to detect trends, predict future threats, and provide more nuanced insights. These models learn continuously from new data, adapting strategic intelligence outputs to reflect subtle shifts in the threat landscape and relevant geopolitical developments.  

AI can also automate the creation and summarization of threat reports, making strategic insights more accessible and timely for decision-makers. This capability reduces a 50-minute task (like summarizing a CISA report) and completes it in under ten seconds.  

Five examples of AI’s impact on strategic threat intelligence are:

• Trend analysis and forecasting: AI-driven models can analyze global threat data to identify long-term trends within massive and otherwise opaque data sets, helping organizations forecast future cyber threats and adjust their security strategies accordingly. ‍
• Threat actor profiling: AI can aggregate and analyze data on attacker behaviors, methods, and targets, creating detailed profiles of advanced threat actors. This helps security teams understand their motivations and strategies, improving defense planning. ‍
• Cross-industry intelligence sharing: AI facilitates automated sharing and analysis of threat intelligence across industries. By analyzing anonymized data from various sectors, AI helps uncover emerging attack techniques and broadens understanding of sector-specific threats.
• ‍Predictive risk assessment: AI can evaluate the risk landscape by correlating geopolitical, economic, and cybersecurity data, predicting how events like regulatory changes or conflicts might impact an organization’s risk exposure over time. ‍
• Automated strategic reports: AI can generate high-level intelligence reports that provide insights into emerging threats, global trends, and cyber threat actors, enabling C-suite executives to make informed strategic decisions about resource allocation and risk management.

AI and Operational Threat Intelligence

AI can enhance operational threat intelligence by automating the monitoring of various sources, such as dark web forums, social media, and threat actor communications. AI-driven systems can provide enriched real-time alerts about emerging threats, analyze attack patterns, and predict potential targets.  

By streamlining processes such as incident response and threat detection, AI can rapidly analyze and contextualize diverse threat data sources, helping security teams identify patterns and detect anomalies more efficiently. AI enhances automation, reducing manual workloads and accelerating decision-making. It can also aid in creating dynamic playbooks and automating report generation.  

AI can enhance operational threat intelligence in a number of ways, including:

• Automated threat correlation: AI can rapidly correlate data from various sources, such as security logs and external threat feeds, identifying real-time threats and connecting related incidents across systems, reducing detection and response times. ‍
• Incident triage: AI streamlines incident triage by automatically prioritizing alerts based on threat severity and context. This allows security teams to focus on the most critical threats first, improving overall incident response efficiency. ‍
• Threat hunting support: AI assists threat hunters by analyzing vast datasets and highlighting potential hidden threats or abnormal behaviors. It helps identify subtle indicators that human analysts may overlook (or would never even notice), enhancing proactive threat hunting efforts. ‍
• Behavioral analytics: AI applies machine learning to user and network behavior, detecting deviations that might signal insider threats or advanced persistent threats (APTs). This enhances the detection of stealthy attacks that evade traditional security measures. ‍
• Threat playbook automation: AI can generate and continuously update automated playbooks for responding to specific threats based on historical incidents and evolving threat landscapes. This ensures consistent, fast responses to known threat patterns.

AI and Tactical Threat Intelligence

Tactical threat intelligence refers to actionable, short-term insights focused on understanding and responding to immediate security threats. It includes real-time data, such as indicators of compromise (IoCs), attack signatures, malware hashes, and IP addresses, which can be directly applied to defend against ongoing or imminent attacks. Tactical threat intelligence empowers security teams to quickly identify and neutralize threats before they cause significant damage.  

Some of the tactical threat intelligence workflows impacted by AI are:  

• Automated data analysis: AI can swiftly process large volumes of threat data from multiple sources, reducing the time it takes to identify potential IoCs and enabling faster decision-making. ‍
• Pattern recognition: Machine learning models can detect patterns in attack behaviors, predict potential threats based on historical data and current trends, and adapt in real time as new attacks unfold. ‍
• Enhanced detection and response: AI systems can automate responses to detected threats, applying pre-configured remediation measures and updating security tools like firewalls or antivirus systems with new IoCs. ‍
• Real-time threat correlation: AI can correlate disparate threat data points, providing more accurate contextualization and helping security teams connect various signals that might indicate a coordinated attack. ‍
• Reduced false positives: AI’s ability to refine and learn from data helps reduce the number of false positives, enabling security teams to focus on genuine threats more effectively.

How SOC Teams Should Be Using AI

1. Integrate AI-driven threat intelligence solutions: AI-driven threat intelligence solutions automate the collection, analysis, and dissemination of threat data. While this technology is very new, it is readily available and demonstrably effective. Anomali, for example, can significantly enhance the speed and accuracy of threat detection and response, reducing the risk of cyber incidents. ‍
2. Leverage TIP, SIEM, and SOAR integration: To maximize the value of threat intelligence, organizations require seamless integration between TIP, SIEM, and SOAR platforms. These technologies were never meant to be standalone. If you’re up for contract renewal, look for an end-to-end platform, such as Anomali’s Security and IT Operations Platform, which facilitates a comprehensive approach by disseminating threat intel across your entire cybersecurity workflow and enabling automated response actions.  ‍
3. Leverage AI to accelerate analyst productivity: Security teams can use threat intelligence tools to quickly understand the context of different types of threat intelligence. A simple example might be using AI-generated NLP to facilitate queries without the need to learn a complex query language, which would empower T1 analysts to perform at the level of a T3 — and make your T3s off-the-charts effective.  

How Anomali Copilot is Accelerating Threat Intelligence

Well-managed threat intelligence can mean the difference between a safe and productive organization and a very public faceplant. It is an absolutely critical component of modern cybersecurity, providing insights into the endless array of potential threats and vulnerabilities. Look for solutions that are designed for the modern cybersecurity ecosystem and leverage production-level AI as an integral part of their cybersecurity offering.  

Anomali Copilot uses advanced AI and natural language processing to vastly accelerate lookback queries on threat intel, reducing what used to take hours (or days) to mere seconds. Summaries of threat intelligence reports that would take an experienced analyst up to an hour can now be accomplished in less than 10 seconds.

The bottom line is that having good intel is not enough: you need to be able to act on your intel,  and do so quickly enough to stop attacks before they gain traction.