What is AI for cybersecurity?

AI is an overarching term that refers to computer systems that perform cognitive functions such as recognizing speech, making predictions, and analyzing complex data. Several branches of AI are used in cybersecurity.

Machine learning is a subset of AI that uses algorithms to learn from data and make predictions. This capability is put to use in cybersecurity to uncover and automatically respond to potential threats across devices, users, and networks.

In deep learning, a more sophisticated branch of machine learning, AI systems process complex data structures using multilayer neural networks, which mimic the human brain’s neural pathways. Deep learning and neural networks tend to be more effective than traditional machine learning at analyzing large sets of high-dimensional data and are used in cybersecurity to detect and respond to sophisticated threats.

Security professionals also use generative AI tools to assist in investigation and response. Because these tools use natural language processing technology, individuals can interact with them using human language, instead of code. As the name suggests, these tools are also capable of generating content, so they can help produce reports, summarize security insights and findings, and provide detailed responses to questions.

AI-powered agents autonomously manage high-volume security and IT tasks, empowering people to focus on proactive security. These agents can triage phishing, data loss prevention, and insider risk alerts, which are extremely time-consuming tasks for humans. Agents can also optimize conditional access policies based on user data. And many teams use AI-powered agents to identify and prioritize vulnerabilities and threats that need to be addressed.

It's important to distinguish between two related but different concepts: AI for cybersecurity and  security for AI.

AI for cybersecurity refers to the use of AI tools to improve an organization's ability to detect, respond to, and mitigate threats to all its environment. Because AI for cybersecurity can analyze and correlate events across multiple sources, it helps organizations identify patterns that indicate potential threats.

AI security, on the other hand, focuses on the protection of AI systems themselves. It encompasses strategies, tools, and practices aimed at safeguarding AI models, data, and algorithms from threats. This includes ensuring that AI systems function as intended and that attackers cannot exploit vulnerabilities to manipulate outputs or steal sensitive information.

In summary, AI for cybersecurity refers to the use of AI systems to enhance an organization’s overall  security posture, while AI security is about protecting AI systems.

AI has really been a game changer in cybersecurity, making it easier for security professionals to respond to a growing number of cyberthreats, increasing amounts of data, and an expanding cyberattack surface. Here are some of the ways AI for cybersecurity helps teams be more effective:

Faster threat detection
Many security solutions, such as SIEM or XDR, log thousands and thousands of events that indicate potentially anomalous behavior. Although the vast majority of these events are innocuous, some aren't, and the risk of missing a potential cyberthreat can be enormous. AI helps identify incidents that really matter. It also correlates seemingly unrelated activities into incidents that indicate a potential cyberthreat.

Simplified reporting
Tools that use generative AI can correlate and analyze information from several data sources to create easy-to-understand reports that security professionals can quickly share with others in the organization.

Vulnerability identification
AI helps detect weaknesses in the overall environment, such as unknown devices and cloud apps, outdated operating systems, or unprotected sensitive data.

Skills enhancement
Because generative AI helps translate cyberthreat data and analysis into natural language, analysts don’t need to know how to write queries to be productive. This helps junior analysts take on more complex tasks. Plus, generative AI provides remediation steps and other recommendations that help new team members quickly learn how to effectively respond to cyberattacks.

Actionable insights
By aggregating and analyzing data from diverse sources like security logs, network traffic, and external threat feeds, AI provides a comprehensive view of the security landscape and reveals hidden patterns of attack.

Reduction of false positives and false negatives.
AI helps reduce false positives and false negatives by using advanced techniques like pattern recognition, anomaly detection, contextual awareness, and continuous learning. These systems provide more nuanced decision-making and avoid overloading security teams with irrelevant alerts.

Scalability
AI significantly enhances scalability in cybersecurity by automating tasks, processing large amounts of data in real time, and continuously learning. As the volume and complexity of cyberthreats grow, AI’s ability to scale and adapt ensures that cybersecurity systems remain resilient, efficient, and capable of handling the demands of modern IT infrastructures.

AI has been integrated into several cybersecurity tools to help improve their effectiveness. A few examples are:
 

• Next-generation firewalls and AI. Traditional firewalls make decisions about allowing or blocking traffic based on rules defined by an administrator. Next-generation firewalls go beyond these capabilities, using AI to tap into threat intelligence data to help identify novel cyberthreats.

• AI-enhanced endpoint security solutions. Endpoint security solutions use AI to identify endpoint vulnerabilities, such as an outdated operating system. AI can also help detect whether malware has been installed on a device or if unusual amounts of data are being exfiltrated to or from an endpoint. During an ongoing attack, AI can automatically isolate the endpoint from the rest of the digital environment.

• AI-driven network intrusion detection and prevention systems. These tools monitor network traffic to uncover unauthorized users who are trying to infiltrate the organization through the network. Using AI, these systems quickly process large volumes of data to identify and block cyberattackers before they cause damage.

• AI and cloud security solutions. Because so many organizations use multiple clouds for their infrastructure and apps, it can be hard to track cyberthreats that move across different clouds and apps. AI helps with cloud security by analyzing data from all of these sources to identify vulnerabilities and potential cyberattacks.

• Internet of Things (IoT) security. Much like endpoints and apps, organizations typically have many IoT devices that are potential cyberattack vectors. AI helps detect cyberthreats against any single IoT device and uncovers patterns of suspicious activity across multiple IoT devices.

• XDR and SIEM. XDR and SIEM solutions pull information from multiple security products, log files, and external sources to help analysts make sense of what’s happening in their environment. AI helps synthesize all of this data into clear insights.

Using AI to support security operations takes careful planning and implementation, but with the right approach, you can introduce tools that make meaningful improvements in operational effectiveness and your team’s wellbeing.

Develop a strategy
There are numerous AI products and solutions for use in security, but not all of them will be right for your organization. It’s important that your AI solutions integrate well with each other and your security architecture, or they may end up creating more work for your team. Consider your biggest security challenges first and then identify AI solutions that will help you solve those issues. Take time to develop a plan for integrating AI into your current processes and systems.

Integrate your security tools
AI for cybersecurity is most effective when it’s able to analyze data across the entire organization. This is challenging if your tools operate in silos. Invest in tools that work together and with your current environment seamlessly, such as integrated XDR and SIEM solutions. Or, if necessary, allocate time and resources for your team to integrate tools, so that you get complete visibility across your entire digital estate.

Manage data privacy and quality
AI systems make decisions and provide insights based on the data used to train and operate them. If there are errors in the data or it’s corrupted, AI will deliver poor insights and make bad decisions. During your planning, make sure you have processes in place to clean up data and protect privacy.

Use AI ethically
A lot of the data that’s accumulated over the years is inaccurate, biased, or outdated. On top of that, AI algorithms and logic aren’t always transparent, making it difficult to know exactly how it generates insights and results. It’s important to ensure that AI is not the final decision maker if there is a risk that it will treat certain individuals unfairly because of biased data. Learn more about responsible AI.

Continuously test your AI systems
After implementation, test your systems regularly to identify bias or quality issues as new data is generated.

Define policies for using generative AI
Ensure that employees and partners understand your organization’s policies for using generative AI tools. It’s especially important that people don’t paste confidential and sensitive data into generative AI prompts because there’s a risk that data might become public.