Big Hunting Game: Utilizing Generative AI for Threat Intelligence

Threat intelligence is a crucial aspect of modern cybersecurity, focusing on the collection, analysis, and dissemination of information about potential or current threats to an organization’s information systems. This intelligence aids in anticipating, identifying, and mitigating cyber threats, thereby enhancing an organization’s security posture. As cyber threats become increasingly sophisticated, leveraging advanced technologies such as Generative Artificial Intelligence (GenAI) has emerged as a promising approach to strengthen threat intelligence efforts.

Understanding Threat Intelligence

Threat intelligence encompasses various activities aimed at understanding and countering cyber threats. These activities include:

• Data Collection: Gathering information from diverse sources, such as threat databases, social media, dark web forums, and network logs.
• Data Analysis: Processing and analyzing collected data to identify patterns, trends, and Indicators of Compromise (IOCs).
• Dissemination: Sharing actionable intelligence with relevant stakeholders to ensure informed decision-making and enhance security measures.

Effective threat intelligence provides a proactive approach to cybersecurity, enabling organizations to anticipate and mitigate potential threats before they materialize.

However, it is crucial to monitor the aspect of time. Intelligence is time-sensitive, and if not delivered promptly, it is useless to the team consuming it.

Introducing Persistent’s Threat Intelligence Copilot

The Generative AI-powered Copilot accelerates cyber threat detection by intelligently automating Threat Intelligence analysis, correlation, and reporting. The copilot mimics human security expert, compiles information from several data sources and takes care of the mundane tasks. Security professionals can now focus on the actual threat rather than the IT and data creation issues. The main value of our Copilot is to make the right information available to the right person at the right time.

Generative AI can help at each stage of the threat intelligence value chain, and we have developed Threat Intelligence Copilot to address the following key issues:

• Enhanced Detection: The Copilot will help in reducing the Mean Time To Detect (MTTD) and lead to better detection capabilities.
• Reduced Toil: The already overworked cybersecurity professionals will receive much-needed assistance from the copilot.
• Bridging Skill Gap: The Copilot utilizes NLP to provide relevant insights and can help bridge the skill gap.

The Copilot improves efficiency at each stage of the threat intelligence value chain:

• Collection Stage: AI and GenAI-powered copilot can help in the automated collection of threat data from a wide range of sources, reducing manual effort and ensuring comprehensive coverage.
• Analysis Stage: GenAI models can analyze vast amounts of data at high speed, identifying complex patterns and correlations that might be missed by human analysts. By integrating GenAI with SIEM systems and other security tools, organizations can automate routine tasks, such as alert triage and incident response. GenAI models equipped with NLP capabilities can analyze unstructured data, such as threat reports, social media posts, and dark web communications, extracting relevant insights for threat intelligence.
• Dissemination Stage: The Copilot can create human-readable reports and summaries, facilitating better communication and decision-making. It can also suggest which team to send the report to for action.

The Threat Intelligence Copilot helps security teams to improve efficiency by 55%. This allows analysts to detect potential threats rapidly and take swift actions.

Persistent is committed to evolving with the latest trends and utilizing them to deliver value. We are also exploring fine-tuned large language models (LLMs) with respect to cybersecurity use cases to enhance our offerings and solutions.

Incorporating Agentic Workflow

To further enhance the efficiency and effectiveness of our Threat Intelligence Copilot, we have incorporated agentic workflow. This workflow empowers security analysts by providing a structured and autonomous approach to handling various stages of threat intelligence. Agentic workflow is a sophisticated, iterative and multi-step process to instruct LLMs to complete tasks. Traditional LLM calls provide a prompt which is consumed, and a completion is generated. This is usually a single stateless call. With agent workflow, we incorporate the patterns of planning, reflection, memory and role-play in applications. Planning helps the agent “think” and decode what steps to take – in our case decode what combination of feeds and tools to invoke. Reflection enables multiple LLM calls to recursively build upon a more accurate solution with the proper lineage to data sources. Given that we are making multiple LLM calls, having memory to store the context is very important. Additionally, through role-playing, we can have the LLM mimic different personas in our solution. This approach has been found to be very affective at generating personalized responses.

Generative AI offers transformative potential for enhancing threat intelligence by automating data collection, enabling advanced analysis, and improving threat detection and response. Organizations can proactively anticipate and mitigate vulnerabilities by leveraging GenAI-powered cyber threat intelligence solutions. However, it is essential to address the associated challenges, such as data quality, explainability, and security, to fully realize the benefits of Generative AI in cybersecurity. As cyber threats continue to evolve, integrating GenAI into threat intelligence strategies will be instrumental in staying ahead of adversaries and safeguarding critical information assets.

To know more about how you can leverage our threat intelligence copilot to bolster your security posture, reach out to us.