What Is AI Threat Detection?

How does AI threat detection work?

AI threat detection is a broad term that can refer to a variety of different threat identification and mitigation techniques that use AI to improve cybersecurity operations.

Security data analysis via AI

The most common is using machine learning to identify patterns that would be challenging for humans to detect through manual analysis. For instance, an AI tool could parse hundreds of authentication log files, and then correlate the data across them with information from past security incidents. It might discover that data associated with failed login attacks is similar to data from a successful breach that occurred in the past. This insight would tip off the organization to the likelihood that the same group of threat actors is trying to breach it again.

Threat summarization and consolidation

AI threat detection could also involve using AI to summarize or consolidate security vulnerability data. For instance, an AI tool could help identify which vulnerabilities are likely to be the most serious to a particular organization based on its application and environment configurations. Likewise, AI could identify related vulnerabilities, which would help developers plan an efficient strategy for mitigating the risks.

AI phishing detection

AI is also adept at analyzing data sources that traditional security tools typically can’t handle as well – such as text written in natural language, as opposed to log files.

A key use case for AI in this context is AI phishing detection. By parsing emails, text messages, or other content that threat actors might use to launch phishing campaigns, AI can identify patterns – such as the urgency of a message’s tone or an instruction from an employer to an employee to share sensitive information – that correlate with phishing attacks.

AI-generated threat context and remediation guidance

Finally, in advanced use cases, AI threat detection tools could use generative AI technology to provide context about threats. They might offer prescriptive guidance about how to remediate a threat, for example, or describe how the threat is exploited. This insight would also assist teams in determining how best to mitigate a newly discovered threat.

Benefits of AI in threat detection

AI offers three core benefits when used to help detect cybersecurity threats:

• Faster detection: Because AI tools can correlate and analyze data much faster than humans, they are able to detect many threats more quickly. This is a critical advantage when dealing with zero-day risks, meaning those that threat actors can exploit at any time – and that organizations should therefore mitigate as rapidly as possible.
• Higher threat volume: Because AI can detect threats faster and more efficiently, it can help organizations to maximize the number of threats they are able to identify and address. Identifying every potential threat against your organization is often not possible because some threats are very challenging to identify, but AI can bring teams closer to that goal of maximum threat detection.
• More accurate detection: Although AI threat detection tools are not foolproof, they may lead – in some cases – to more accurate threat detection. This is due primarily to their ability to correlate and analyze a larger volume of information. Humans who are working to detect threats manually typically aren’t able to draw on as much data, leading to false positives and false negatives in some cases.
• Faster and more efficient threat mitigation: AI tools that generate guidance about how to mitigate threats can help teams remediate the threats faster and with less effort. This benefit not only reduces the risk of a successful exploit, but also helps cybersecurity and development teams to do more with less personnel.

Challenges and limitations of using AI to detect threats

As with any type of AI-based solution, AI threat detection is imperfect. It’s subject to the following challenges and limitations:

• AI threat detection tools may require extensive effort to set up and configure, especially if they need to be trained using a company’s private data to detect threats and risks unique to that company.
• The security alerts and reports produced by AI tools sometimes lack transparency, meaning it’s difficult or impossible to understand why the tools drew certain conclusions. This can make it challenging to know how much faith to place in an AI-generated recommendation.
• Flaws in AI algorithms and models (or in the data used to train models, in cases where the models require training) may lead to inaccurate results, such as false positive reports about vulnerabilities that don’t actually exist.
• As with any type of analysis, the effectiveness of AI-based threat detection depends on the quality of the data fed to AI tools. Data about threats that is incomplete or inaccurate is likely to lead to problems such as false positive and false negative alerts.

The future of AI in cybersecurity

AI won’t solve every cybersecurity risk, but it’s poised to play an important role in helping organizations move the needle in the right direction as they contend with ever-increasing volumes of threats.
That’s why capabilities like AI-guided remediation, which is now available on the Aqua platform, bring so much value to modern cybersecurity operations. By automatically helping teams understand how vulnerabilities work and how to remediate them, Aqua’s AI features maximize efficiency, enable faster threat mitigation, and minimize the risk of experiencing a breach due to a vulnerability that took too long to fix.