To stay ahead of bad actors in today’s sophisticated threat landscape, companies must take a proactive approach to cybersecurity. Threat hunting is the cornerstone of a security strategy that prevents cyberattacks.
Threat hunting cuts cybercriminals off at the pass by actively searching for hidden threats and vulnerabilities before hackers have a chance to exploit them to cause harm. Artificial intelligence (AI) has revolutionized threat hunting practices by using advanced analytics to gain deep insights into potential risks. AI and machine learning (ML) improve the speed, efficiency, and accuracy of threat hunting.
AI-driven tools and techniques can strengthen cyber defense processes by enhancing threat hunting capabilities through automating threat detection and response.
Understanding AI-Driven Threat Hunting
AI-powered threat hunting uses algorithms to analyze vast amounts of data to detect anomalies in network traffic and user behavior that may signal a threat or emerging security incident. Machine learning models can carry out behavioral analysis by being trained to recognize patterns, such as typical user behavior, network traffic patterns, and threat signatures.
By understanding normal network patterns, AI can identify deviations that represent suspicious activity and as-yet unknown threats. Through automated threat detection and response, AI-powered security optimizes a security team’s ability to monitor and defend company systems.
Tools and Techniques in AI-Driven Threat Hunting
AI-powered threat hunting can be integrated into a company’s existing security frameworks by adopting security tools that are enhanced with AI capabilities. Some of the AI-based tools that are available are intrusion detection and response systems, security information and event management (SIEM), and automated threat intelligence platforms.
Intrusion Detection and Response
AI-powered intrusion detection and response uses advanced algorithms to proactively detect and mitigate threats, protecting sensitive data before it can be stolen or compromised.
Security Information and Event Management (SIEM)
AI-driven SIEM provides security analysts with threat intelligence and automation so they can work more quickly and accurately.
Managed Detection and Response
AI can also be used for managed detection and response (MDR), combining automated and manual actions for a unified approach to threat hunting that increases visibility and protects the network all the way to the edge.
Supervised and Unsupervised Learning
Machine learning may be used in AI-driven threat hunting in two main ways: supervised and unsupervised learning. In supervised learning, data sets are labeled to train algorithms to identify or predict threats. In unsupervised learning, data sets are unlabeled, allowing ML algorithms to discover hidden patterns used to analyze and organize information.
To take full advantage of AI-driven threat hunting, companies need staff members who are skilled in managing and interpreting AI-driven systems. For those companies that may lack these skill sets, working with a technology partner that has them will help them harness the security benefits of AI.
3 Best Practices for AI-Driven Threat Hunting
To fully leverage AI-driven threat hunting, organizations should follow these best practices:
- Regularly update AI models with the latest threat intelligence
- Integrate AI tools with existing security frameworks for comprehensive coverage
- Continuously train staff in AI tools and threat hunting techniques
These practices ensure optimal performance and adaptability of AI-driven security measures.
The Future of AI in Cyber Defense
As AI technology evolves, we can expect more sophisticated threat detection capabilities. Future advancements may include:
- Enhanced predictive analytics for anticipating cyber threats
- Improved NLP (natural language processing) for better threat intelligence analysis
- Increased automation in incident response to reduce reaction times
AI-Driven Threat Hunting Use Cases
Like most revolutionary technologies, AI-driven threat hunting submits companies to a learning and adoption curve. Organizations can only reach AI-driven security maturity by exploring, uncovering, and realizing opportunities within their businesses.
The IBM white paper 5 Criteria for evaluating generative AI in threat management explains how to uncover AI security use cases related to security operation center (SOC) functions, such as accelerating threat hunting, prioritizing investigations, and automating reporting.
In an SOC, AI-powered SIEM can be used to prioritize risks and eliminate false positives. These capabilities enable security analysts to focus on legitimate threats.
IBM recommends focusing on time savings when looking for AI use cases, pointing out how GenAI can be used to speed up threat hunting by using NLP for searches, developing threat detection and response playbooks, and training chatbots.
Uncovering AI-Powered Threat Hunting Opportunities
According to Security Intelligence, companies are in a race with hackers to uncover and realize the potential of generative AI. While cybercriminals are using GenAI to write better phishing emails and write malware code, organizations are looking for ways to leverage GenAI and other types of AI technology to fight the good fight by protecting data, applications, and networks.
ProActive Solutions can help your company uncover AI security opportunities and design a strategy using leading AI-powered threat hunting tools. We take a consultative approach to cybersecurity, working to understand your security challenges and providing the expertise you need to leverage AI for security defenses.
Ready to optimize your cyber defense with AI-driven threat hunting? Request a consultation from ProActive to discover how our cutting-edge AI solutions can safeguard your business from sophisticated cyber threats.
