What is AI for cybersecurity?
AI for cybersecurity defined
AI for cybersecurity uses AI to analyze and correlate event and cyberthreat data across multiple sources, turning it into clear and actionable insights that security professionals use for further investigation, response, and reporting. If a cyberattack meets certain criteria defined by the security team, AI can automate the response and isolate the affected assets. Generative AI takes this one step further by producing original natural language text, images, and other content based on patterns in existing data.
The evolution of AI for cybersecurity
Security communities have used AI for cybersecurity since at least the late 1980s with the following key technology advancements:
- In the beginning, security teams used rules-based systems that triggered alerts based on parameters they defined.
- Starting in the early 2000s, advances in machine learning, a subset of AI that analyzes and learns from large data sets, has allowed operations teams to understand typical traffic patterns and user actions across an organization to identify and respond when something unusual happens.
- The most recent improvement in AI is generative AI, which creates new content based on the structure of existing data. People interact with these systems using natural language, allowing security professionals to dive deep into very specific questions without using query language.
But it isn’t just security teams who are using AI. Cyberattackers, whether nation-state actors, large criminal enterprises, or individuals, may also exploit AI to their advantage. Bad actors infect AI systems, use AI to impersonate legitimate people, automate their cyberattacks, and deploy AI to help research and identify cyberattack targets. There is also a risk that people will paste sensitive data into AI prompts and accidentally leak data to the public.
Impact of generative AI in cybersecurity
Generative AI is still in the early stages and has only recently been introduced in security with the announcement of Copilot for Security. It has the potential to radically simplify security for analysts and other security professionals by:
- Synthesizing data into actionable recommendations and insights with appropriate context to help guide incident investigations.
- Creating human-readable reports and presentations that analysts can use to help others in the organization understand what’s happening.
- Answering questions about an incident or vulnerability in natural language or graphics.
As the security community builds generative AI into security products and solutions, it will be important to build it responsibly. People need to know that new systems respect privacy and are reliable and safe. Accuracy and truthfulness are known issues with current generative AI models, but as the technology improves, it will help organizations stay ahead of AI-driven cyberthreats.
How does AI for cybersecurity work?
AI for cybersecurity works by evaluating massive amounts of data across multiple sources to identify patterns of activity across an organization, such as when and where people sign in, traffic volumes, and the devices and cloud apps that employees use. Once it understands what’s typical, it can identify anomalous behavior that may need to be investigated. To maintain privacy, an organization’s data isn’t used for the AI output at other organizations. Instead, AI uses global threat intelligence synthesized from multiple organizations.
AI uses machine learning algorithms to continuously learn based on the data the system evaluates. When generative AI identifies certain known cyberthreats, such as malware, it can help contextualize threat analysis and make it easier to understand by generating new text or pictures to describe what’s happening.
People are still vitally important to cybersecurity, but AI helps them increase their skills and identify and resolve threats faster.
AI security use cases
Rather than replace security professionals, AI is most effective when it’s used to help them do their jobs more effectively. Some common uses cases for AI security are:
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Identity and access management
AI is used for identity and access management (IAM) to understand patterns in user sign-in behaviors and detect and surface anomalous behavior for security professionals to follow up on. It can also be used to automatically force two-factor authentication or a password reset when certain conditions are met. And if necessary, it can block a user from signing in if there’s reason to believe that an account has been compromised.
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Endpoint security and management
AI helps security professionals identify all of the endpoints being used within the organization and helps keep them updated with the latest operating systems and security solutions. AI can also help uncover malware and other evidence of a cyberattack against an organization’s devices.
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Cloud security
Most organizations are heavily invested in the cloud. They manage infrastructure at one or more of the cloud service providers and use cloud apps from various vendors. AI helps teams gain visibility into risks and vulnerabilities across their multicloud estate.
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Cyberthreat detection
Extended detection and response (XDR) and security information and event management (SIEM) solutions help security teams uncover cyberthreats across the entire enterprise. To do this, both solutions rely heavily on AI. XDR solutions monitor endpoints, emails, identities, and cloud apps for anomalous behavior and surface incidents to the team or respond automatically depending on the rules defined by security operations. SIEM solutions use AI to aggregate signals from across the enterprise, giving teams better visibility into what’s happening.
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Information protection
Security teams use AI to identify and label sensitive data throughout the environment, whether it’s housed on the organization’s infrastructure or in a cloud app. AI can also help detect when someone is trying to move data out of the company and either block the action or raise the issue to the security team.
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Incident investigation and response
During incident response, security professionals must sort through mountains of data to uncover potential cyberattacks. AI helps identify and correlate the most useful events across multiple data sources, saving professionals valuable time. Generative AI simplifies investigation even further by translating analysis into natural language and answering questions, also in natural language.
Benefits of AI security
With a growing number of cyberthreats, increasing amounts of data, and an expanding cyberattack surface, there are several ways that AI helps security operations teams be more effective.
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Detects critical cyberthreats faster
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 the incidents that really matter. It also helps detect behavior that may not look suspicious on its own but when correlated with other activities, indicates a potential cyberthreat.
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Simplifies reporting
Tools that use generative AI can pull information from several data sources to create easy-to-understand reports that security professionals can quickly share with others in the organization.
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Identifies vulnerabilities
AI helps detect potential risks such as unknown devices and cloud apps, outdated operating systems, or unprotected sensitive data.
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Helps analysts grow their skills
Because generative AI helps translate cyberthreat data and analysis into natural language, analysts with fewer technical skills can be more productive. Generative AI helps identify remediation steps, enabling new team members to quickly learn how to effectively respond to cyberattacks.
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Provides cyberthreat analysis and insights
Sophisticated cyberattackers typically try to evade detection by moving across different identities, devices, apps, and infrastructure. Since AI can quickly process lots of data from various sources, it can help identify this suspicious behavior and prioritize which cyberthreats security professionals should pay attention to.
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AI security for cyberthreat detection and prevention
One of the most critical uses of AI for cybersecurity is cyberthreat detection and prevention. There are several ways that machine learning algorithms and AI help identify and prevent cyberthreats:
- Supervised learning models use labeled and classified data to help train a system. For example, certain known malware has unique signatures that make it distinct from other types of cyberattacks.
- In unsupervised learning, machine learning algorithms identify patterns in data that haven’t been labeled. This is how AI detects advanced or emerging cyberthreats that don’t have known signatures. They look for activity that falls outside the norm, or they look for patterns that mimic other cyberattacks.
- With user and entity behavior analytics, systems evaluate user traffic patterns to understand known behaviors so that they can identify when a user does something unexpected or suspicious, which could indicate account compromise.
- AI systems also use natural language processing to analyze unstructured data sources like social media to generate threat intelligence.
What are AI-powered cybersecurity tools?
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. And AI can help stop endpoint cyberattacks by isolating 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. AI helps these systems process data faster to identify and block cyberattackers before they do too much 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.
- Securing Internet of Things (IoT) devices with AI: 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 also 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.
Best practices for AI for cybersecurity
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.
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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.
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Integrate your security tools
AI for security is most effective when it’s able to analyze data across the entire organization. This is challenging if your tools operate in siloes. Invest in tools that work with your current environment and work together 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.
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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.
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Continuously test your AI systems
After implementation, testing your systems regularly will help you identify bias or quality issues as new data is generated.
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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 in instances where it may treat certain individuals unfairly because of the bias in the data it’s using. Learn more about responsible AI.
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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 is a risk that data might become public.
The future of AI for cybersecurity
The role of AI for security will only continue to grow. Over the coming years, security professionals can anticipate that:
- AI will get better at detecting cyberthreats with fewer false positives.
- Security operations teams will automate their more tedious work as AI gets better at responding to and mitigating a greater variety of cyberattack types.
- Organizations will use AI to help address vulnerabilities and improve security posture.
- Security professionals will still be in high demand.
- People will take on more strategic roles, such as addressing the most complex security incidents and proactive cyberthreat hunting.
It isn’t just the security community that will get more effective with AI. Cyberattackers are also investing in AI and will likely use this technology to:
- Crack large amounts of passwords at once.
- Create sophisticated phishing campaigns that are difficult to distinguish from genuine emails.
- Develop malware that’s incredibly difficult to detect.
As bad actors integrate more sophisticated AI into their cyberattack methods, it will become even more imperative for the security community to invest in AI to stay ahead of these cyberthreats.
AI security solutions
Organizations face a growing number of cyberthreats with an expanding cyberattack surface. Keeping up can be overwhelming for cybersecurity professionals, especially given the shortage of talent. By taking on more of the tedious, low-skill tasks, AI promises to make security professionals’ jobs more satisfying and strategic. Organizations can begin preparing for a future with more AI-driven cyberattacks by incorporating AI into security operations now. Start with a strategy and then invest in tools that are most likely to help you address your biggest security challenges today.
Learn more about Microsoft Security
Microsoft Copilot for Security
Empower security teams to detect hidden patterns and respond to incidents faster with generative AI.
Identity threat detection and response (ITDR)
Get comprehensive protection for all of your identities and identity infrastructure.
Microsoft Defender Threat Intelligence
Expose and eliminate modern cyberthreats and their infrastructure using dynamic threat intelligence.
Microsoft Defender for Cloud
Strengthen your security posture, protect workloads, and develop secure applications.
Microsoft Defender for Endpoint
Rapidly stop cyberattacks, scale security resources, and evolve defenses across network devices.
Microsoft Sentinel
See and stop cyberthreats across your entire enterprise with intelligent security analytics.
Frequently asked questions
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AI for cybersecurity uses AI to analyze and correlate event and cyberthreat data across multiple sources, turning it into clear and actionable insights that security analysts use for further investigation and cyberattack mitigation. If a cyberattack meets certain criteria defined by the security team, AI can automate the response and isolate and remove the cyberattacker or virus.
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AI is being used in many aspects of security including identity protection, endpoint protection, cloud security, data protection, cyberthreat detection, and incident investigation and response.
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One great example of AI for security is the use of machine learning algorithms to analyze user behavior to identify patterns. By understanding what’s normal, these systems can detect anomalous behavior that may be an indicator of a cyberattack. In another example, security professionals use generative AI to ask a question about a specific incident or environment and get back a diagram or natural language text that provides more context and insights from multiple data sources.
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Machine learning is a subset of AI that detects patterns from massive amounts of data. Security systems that use machine learning are, over time, able to learn what the typical traffic patterns and user actions are across an organization and identify when something unusual happens. They can also evaluate events from several different systems that may seem innocuous on their own, but together represent a risk.
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AI for security offers many benefits for businesses, including:
Decreasing incident response times.
Detecting cyberthreats sooner and with more accuracy.
Automating response for certain known cyberthreats.
Freeing up security professionals to focus on proactive tasks.
Improving security posture.
Simplifying reporting.
Helping analysts increase their skills.
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