4 ways to reduce cybersecurity risks with artificial intelligence and machine learning

As Artificial Intelligence (AI) and Machine Learning (ML) technologies become increasingly intertwined within our personal and professional lives, our cybersecurity threat risks and attack surface both inherently rise. It’s crucial to increase protection against such dangers by understanding the potential risks that these evolving technologies introduce—especially to critical infrastructure such as energy grids, hospitals, and water utilities. 

Cybersecurity professionals have understandably focused their attention on how AI and ML can expose networks to data breaches, malware and phishing attacks, and other forms of vulnerability exploitation. The good news is that those same technologies can also be used to safeguard networks, quantify and identify potential risks, and automatically scale cyber defenses. 

Key use cases for AI and ML cybersecurity tools

Organizations looking to capitalize on these benefits can consider using AI and ML technologies to:

1. Classify cyber vulnerabilities and identify critical assets. Supervised ML technologies are powerful tools that can be used to classify data points into discrete outcomes. The models can help discover patterns and relationships between input data and output variables more rapidly and with greater accuracy than humans. For example, classification models can:

• Identify malicious files
• Confirm the existence of phishing traps
• Classify assets as critical to U.S.-based infrastructure
• Provide predictive insights into which categories specific cyber vulnerabilities belong

The outputs of the classification models not only protect networks from cyberattacks but also enable organizations to proactively prioritize assets to protect and vulnerabilities to mitigate.

2. Identify threats and vulnerabilities through anomaly detection. Anomaly detection tools can be used to identify suspicious or abnormal data points (e.g., logins at odd times of the day); these tools can be supervised or unsupervised, depending on the use case. When these tools identify anomalous cyber behavior, they can alert system operators about this behavior or automatically block access to the network. By using these tools to analyze network traffic, organizations can more rapidly identify, classify anomalous cyber activity – such as denial-of-service or malware attacks – speeding their ability to assess and take action when their network perimeter has been compromised. 

3. Summarize cyber text or scanner data with natural language processing (NLP). NLP converts text and unstructured data into a computer-readable format so that it can be analyzed for content, subjects, and trends. NLP techniques such as topic modeling and keyword extraction can be applied to vulnerability descriptions or data from port/vulnerability scanners to distill unwieldly volumes of data or identify key topics. Using NLP to make sense of vast data volumes quickly, efficiently, and accurately gives organizations ways to more easily manage the influx of cyber threats and attacks. As an example, by using NLP, organizations can rapidly summarize information, eliminating manual processing and reviews, and efficiently identifying threats, vulnerabilities, or indicators of compromise from large amounts of data. Organizations can also use NLP to prioritize threats and accelerate response times for triaging exploitable or high-risk vulnerabilities. 

4. Quantify risk by simulating different scenarios. Cyber risk simulation models provide organizations with a structured methodology and tool to predict future outcomes of various scenarios and quantify potential network consequences from specific risks. Simulating different scenarios helps organizations assess a variety of risks – from high thread but low consequence to low vulnerability but high consequence.  By assessing the range of threats, vulnerabilities, and consequences, organizations can prioritize their risks for mitigation and protection. 

Being strategic and responsible with AI and ML

When implementing AI and ML, organizations must develop and enforce secure, responsible, and ethical AI policies. To prevent and mitigate cyber risks, Guidehouse recommends that organizations develop a strategic roadmap that includes strong data management and governance. We also recommend that organizations engage knowledgeable professionals and adhere to industry-recognized standards when developing their protection strategies. With the right guidance, organizations can implement AI and ML systems safely and responsibly to secure their networks and proactively protect vital assets.