Cyber threat intelligence prepares organizations to prevent, detect and mitigate cyber threats. AI’s ability to analyze vast data sets, identify patterns, and predict potential risks with speed and accuracy has revolutionized how organizations detect and respond to cybersecurity threats. This article explores the essential role of AI in threat intelligence, delving into the specifics of how it's helping security teams strengthen their cybersecurity stance and stay ahead of the quickly evolving threat landscape.
Primary Types of Threat Intelligence
Threat intelligence takes several forms, each playing an important part helping organizations defend their digital assets. There are three broad categories of threat intelligence: operational, tactical and strategic.
Operational threat intelligence
The primary goal of operational threat intelligence is to thoroughly understand potential threats. This type of threat intelligence details the tactics, techniques, procedures and patterns of behavior that potential attackers are most likely to use. When security teams have an in-depth understanding of a threat actor’s modus operandi, they can make more informed decisions about which types of security controls and other safeguards are most effective for thwarting an attack.
Tactical threat intelligence
Tactical threat intelligence focuses on near-term threats. The security operations center uses indicators of compromise, including URL and IP blacklists, file hashes, malware trends and signatures, known malicious domain names, and phishing attacks to gain visibility into the organization's attack surface. Tactical threat intelligence is used by threat hunters to root out advanced persistent threats and other attackers who have already breached the network defenses.
Strategic threat intelligence
Strategic threat intelligence approaches cybersecurity at the macro level, highlighting how global events, industry-specific attack trends, government regulations or changes in industry policies are likely to impact the cybersecurity posture of the organization. This nontechnical information is intended for use by senior leadership outside the security field to ensure the organization’s broader risk management and budgetary decisions remain in sync with the current cybersecurity landscape.
How AI Is tTransforming Cyber Threat Intelligence
AI is reshaping how security teams collect, analyze and act upon threat intelligence. As the amount and diversity of security-relevant data rapidly expands, AI has become an integral part of modern threat intelligence programs.
Faster, more accurate threat detection
One of AI’s largest advantages is scale. It can analyze oceans of data quickly and efficiently, recognizing complex patterns and anomalies within the data that humans and traditional threat detection tools easily overlook. AI-enabled threat detection tools can also learn to become more effective over time, incorporating data from historical incidents, real-time network traffic and third-party threat intelligence feeds to proactively identify emerging threats. AI algorithms don’t rely on explicit rules to find threats, making them highly effective at spotting difficult-to-detect attack vectors such as zero-day attacks, insider threats and compromised credentials.
AI-assisted threat hunting
By nature, AI is autonomous and adaptive, making it ideal for accelerating threat-hunting activities. AI assists threat hunting in several important ways. First, it improves the accuracy of alerts, helping threat hunters use their time investigating actual threats rather than wasting their time chasing down false positives. Further, AI excels at automating manual processes. In the context of threat hunting. AI can automate many of the time-consuming, manual processes involved in the initial stages of data analysis, freeing threat hunters to focus their energies on higher-level tasks. Lastly, AI-enabled threat-hunting tools can help teams work in tandem. By correlating data from various sources, these tools can flag potential threat connections that may have been overlooked by threat hunters working in isolation.
Future threat prediction
AI can analyze threat intelligence data from diverse sources, using that information to identify patterns, correlations and emerging threats useful for building models that aid in the prediction of future cybersecurity threats and vulnerabilities. Examples include predicting network security outcomes or predicting blacklisted IP and port addresses. By forecasting likely future outcomes, organizations can take proactive security measures. The adaptive nature of AI allows these predictive models to continually learn from evolving threat landscapes, providing continuously updated insights into new attack methods and techniques.
Behavioral analytics
People and systems tend to behave in predictable ways. Changes in behavioral patterns can signal the presence of a cybersecurity threat. For this reason, behavioral analytics play a vital role in threat intelligence. AI-enabled behavioral analytics tools analyze user and system behavior, establishing baselines and identifying deviations from the norm that may indicate the presence of a cyber threat. These algorithms can immediately detect suspicious anomalies, automatically alerting security administrators and directing an automated response to mitigate the potential threat.
Analyzing unstructured data with NLP
Natural language processing (NLP), a branch of AI that enables machines to understand human language, allows security teams to monitor potential adversaries on the dark web, collecting and analyzing unstructured data from web forum discussions, user profiles and other forms of online communication. This data provides an invaluable source of new threat intelligence, such as the latest attack techniques, new indicators of compromise and similarities between threat actors.
Automating incident response and mitigation
Cybersecurity incidents can unfold quickly, so automating incident response can help contain a threat. AI automates and orchestrates incident response, converting threat intelligence data into concrete action. AI algorithms can assess the severity of incidents, prioritize threats for security analysts, and recommend or automatically execute response actions such as isolating an infected system. AI can also automate the security patch deployment process, discovering and applying new patches as they’re released to ensure relevant systems remain up to date.
Build Your AI-Enabled Threat Intelligence Program on Snowflake
The Snowflake's Cybersecurity Data Cloud provides you with the data infrastructure and machine learning development capabilities required to build AI applications and run AI-enabled threat intelligence. Safeguard your enterprise with unified data, near-unlimited visibility and powerful analytics. With Snowflake, security teams have the resources required to make faster, more informed decisions, taking a proactive approach to securing the organization’s digital assets. Accelerate threat hunting and investigations with dynamically updated threat intelligence data from Snowflake Marketplace, or bring contextual data into Snowflake. Deploy applications in your Snowflake account for off-the-shelf integrations, security content and pre-built interfaces — all without moving your data.
