AI in Threat Intelligence

What is AI in threat intelligence?

The role of AI in threat intelligence is to leverage artificial intelligence, particularly machine learning, to detect and respond to cyber threats more effectively and rapidly than human capabilities allow. Due to the sheer volume of cyber threat data, threat intelligence analysts are frequently overwhelmed with information, making it difficult to separate true threats from false positives. AI-driven threat intelligence tools can boost the speed, accuracy and efficiency of threat intelligence production and enhance its effectiveness.

Which parts of the intelligence cycle can be powered by AI, how and why?

AI can enhance nearly every stage of the threat intelligence lifecycle – a step-by-step framework that helps organisations generate, manage and utilise threat intelligence effectively.

Planning and collection

 In the collection phase of the threat intelligence lifecycle, AI can automate and accelerate the data collection process, scanning vast amounts of data from various sources, including open-source intelligence, dark web forums, social media and reports, to identify relevant information. Using keyword and pattern recognition, AI can identify specific keywords, patterns and indicators of compromise (IoCs) within the collected data to filter out relevant data. Natural Language Processing (NLP) enables AI to process human language and extract meaningful threat intelligence from unstructured text sources, such as news articles and blogs.

Processing and analysis

At the processing and analysis stage of the lifecycle, AI algorithms can detect and remove duplicate or redundant entries in collected data. This enables the system to flag anomalies within data and filter out irrelevant information. AI can spot entities within the data, such as IP addresses, domains and email addresses, and correlate them with known threats or threat actors. It can also identify key information, sentiments and context to make data more understandable and actionable.

Production and dissemination

AI can be used in the production and dissemination phases of the threat intelligence lifecycle to automatically generate threat intelligence reports by aggregating, summarising and formatting relevant information into human-readable, concise and actionable threat reports. These reports can include visual representations of threat data, such as graphs, charts and heatmaps, to help analysts and decision-makers better understand complex threat trends and relationships. AI can even facilitate multi-lingual threat reporting by translating threat intelligence into different languages for a global audience.

Feedback and improvement

In the feedback stage of the threat intelligence lifecycle, AI’s training process is informed by its feedback loop, which allows AI to evolve and improve its capabilities for threat detection and intelligence collection over time.

By incorporating stakeholder concerns and requirements, AI can adapt its algorithms and processes to provide more relevant threat intelligence, helping organisations identify and respond to the specific threats more efficiently.

This enables organisations to continuously refine their threat detection capabilities, keep up with evolving threat landscapes and prioritise their efforts to protect against the most relevant and pressing security challenges.

What is AI based threat protection?

In the context of threat intelligence, AI-based threat protection is the use of artificial intelligence, including machine learning algorithms, to continuously monitor for and identify potential threats to an organisation.

AI technology can process and analyse vast amounts of data in real-time, enabling it to spot anomalies and potential threats with accuracy. AI can extract pertinent data on potential threats and equip organisations with the actionable intelligence they need to proactively and effectively manage security risks.

Automated threat response and decision making

When it comes to incident response, AI can drive automated threat responses to known or defined threats based on the intelligence gathered. This can include isolating compromised systems, blocking malicious IP addresses, or applying patches. Automated responses can be executed faster than manual intervention, reducing the window of vulnerability.

AI can aid decision making by integrating threat intelligence feeds, enabling organisations to stay updated on the latest threats and attack techniques. This integration ensures that decisions and responses are based on the most current threat data available.

Additionally, AI can correlate data from a broad range of sources to provide a more comprehensive view of a security threat. It can link seemingly unrelated cyber, geopolitical and physical security events to identify threats, informing decision-making and response strategies.

AI can also help generate intelligence on the likelihood, severity and potential impact of threats, helping security teams prioritise their responses and advise decision makers on how best to mitigate risk. This ensures that critical threats are addressed first, reducing response times and minimising damage.

Use cases of AI in threat intelligence

There are numerous use cases of AI in threat intelligence, from data collection and processing to analysis and reporting.

Specifically, this includes:

• Aggregating threat data – Collecting and aggregating data from a wide range of sources, including open-source, deep and dark web, external threat feeds and reports to provide a comprehensive view of potential threats
• Natural Language Processing (NLP) – Extracting relevant information and enriching threat intelligence data from textual threat data
• Pattern recognition – Identifying patterns and anomalies within threat data, helping threat intelligence analysts spot emerging attack vectors and vulnerabilities
• Discovering IOCs – Identify IOCs, such as suspicious IP addresses, domains, or file hashes, within threat data, streamlining the IOC extraction process
• Tactics, Techniques and Procedures (TTPs) –Analysing historical attack patterns and tactics, techniques and procedures to identify the threat actors or groups behind specific attacks and better defend against specific adversary behaviours
• Dark web monitoring – Scanning the dark web for mentions of an organisation’s data, credentials or other sensitive information, providing early warnings of potential breaches
• Contextual threat analysis – Analysing threat data in the context of an organisation’s industry, geography and priorities, providing a more tailored assessment of potential risk
• Threat classification –Automatically categorising and prioritising threats based on their severity and relevance
• Threat intelligence Reporting – Generating threat intelligence reports, making it easier for security teams and leadership to understand the current threat landscape and make informed decisions

Advantages and Risks of AI in threat intelligence

AI threat intelligence offers several advantages and benefits, but it also comes with certain risks and challenges.

Advantages of AI

• Enhanced speed and efficiency – AI technology can process and analyse vast amounts of data in real-time, increasing productivity and enabling faster threat detection and response
• Improved accuracy – AI can reduce the risk of human error due to oversight, data entry errors, or other human factors and identify extract information that might be missed by humans, providing more accurate threat assessments
• Continuous monitoring – AI systems can work 24/7 without fatigue, ensuring constant vigilance and enabling timely responses to emerging threats.
• Predictive capabilities – AI and machine learning algorithms can identify trends and predict future threats based on historical data, enhancing decision-making and helping organisations proactively defend against evolving attack vectors
• Scalability – AI can handle changing volumes of data and alerts, perform complex tasks and adapt to changing needs efficiently and cost-effectively

Risks and challenges of AI

• Adversarial attacks – Sophisticated threat actors can attempt to deceive AI systems by crafting attacks specifically designed to evade detection, undermining the effectiveness of AI-based threat intelligence
• Balancing human-AI collaboration – Striking the right balance between human analysts and AI systems is challenging. While AI can provide key insights, human experts still need to provide critical thinking, creativity, context and ethical judgment to assess threats effectively. Overreliance on AI can lead to errors and missed threats.
• Bias concerns – AI models can absorb and perpetuate biases found within their training data or algorithms, which can result in distorted or deceptive assessments
• Regulatory compliance – Using AI in threat intelligence may require organisations to navigate complex and evolving regulatory frameworks, which can be time-consuming and costly

The future of AI in threat intelligence

Due the inherent ability of AI to learn and evolve, the capabilities of and use cases for AI in threat intelligence will only grow.

This may include advances in AI’s ability to analyse more sophisticated and evolving threats, including zero-day attacks and advanced persistent threats (APTs), and improvements in predictive analytics. AI-driven NLP capabilities could achieve even greater proficiency in multiple languages and better detect and evaluate emotions and sentiments in text.

However, human expertise will remain essential to interpret and provide a contextual understanding of AI-driven threat intelligence. Humans can apply critical thinking, as well as ethical, legal and practical considerations, to the evaluation of threats, which are essential for decision-making and mitigating risk. Threat intelligence often encompasses intricate, multifaceted situations and despite advancements in AI, human judgment and adaptability are still be required.

How Silobreaker enhances threat intelligence with AI

With threat intelligence, the variety of sources and formats that require analysis is simply too numerous and complex for human analysts to manually aggregate and manage within the constraints of a standard database system.

Understanding this, Silobreaker utilises artificial intelligence to make sense of vast amounts of unstructured data from countless different sources. By using machine learning for entity recognition, topic classification, data relatedness, clustering and deduplication, language detection, filtering and noise reduction, Silobreaker can provide a more comprehensive and accurate view of the threat landscape.

Applying machine learning and algorithms, the Silobreaker Relevance Engine automatically extracts and contextualises all entities including domains, IP addresses and hashes from sources. Using AI, Silobreaker quickly recognises language types; filters and clusters sources; and resolves any ambiguities in the results. The platform’s machine learning capabilities enable it to quickly identify emerging threats, risks, incidents and opportunities.

Silobreaker has also introduced plans for Silobreaker AI, a generative AI tool designed to aid threat intelligence teams in collecting, analysing and reporting on intelligence requirements. Silobreaker AI accelerates the production of high-quality intelligence reports, enabling organisations to assess and mitigate geopolitical, cyber and physical risks efficiently.