What is AI in cybersecurity?

Artificial intelligence (AI) in cybersecurity is the application of machine learning techniques, natural language processing (NLP), data analysis, retrieval augmented generation (RAG), and other AI technologies to protect networks, systems, devices, and data from attacks and unauthorized use.

The use of AI in cybersecurity is increasing to strengthen an organization's security posture and enable proactive defenses. AI can automate routine tasks such as onboarding custom data sources, prioritizing and distilling alerts, converting detection rules, helping with SIEM migration, and more. As AI adapts, evolves, and learns from your data, its ability to identify and respond to new and emerging threats improves.

AI in cybersecurity is important because it empowers organizations to be more proactive, efficient, and adaptable in their defenses against cyber threats, many of which are now AI-driven themselves.

To face the scale of AI-driven cyber threats, traditional cybersecurity methods are proving insufficient. Threat actors enjoy the increased automation and sophistication AI provides to their attack and evasion methods. Novel attack techniques, for example, include polymorphic malware, large language model-based vulnerabilities, and deepfake-enhanced phishing. In addition to the already rampant malware, social engineering, and vulnerability exploitations, the attack landscape is expanding with AI-fueled threats, making it harder to defend.

Security teams using AI-driven security analytics to automate and accelerate incident response, enhance threat detection and prediction, and improve the accuracy of true positives can scale to the occasion.

With AI helping take on manual tasks, security practitioners can focus on more strategic objectives like threat hunting and investigating true positives. Traditional alert sifting can easily take up analysts' entire working days. With AI, it now takes minutes to distill hundreds of alerts into the handful that actually matter. Similarly, a security analyst can investigate a threat in less than an hour with the help of AI. Without AI, this task might take days.

AI works in cybersecurity through machine learning models, NLP, large language models (LLMs), and AI algorithms. AI tools analyze massive amounts of data to detect patterns and anomalies, pointing to potential threats and novel attacks.

Gathering and processing the data

AI algorithms can gather and process data from vast datasets, such as network traffic, system logs, and user behavior, in real time. AI can help security teams collect and normalize a new data source in approximately 10 minutes. It automates the development of custom data integrations and creates a fully fledged integration, including a pipeline, mappings, templates, and an integration package.

Creating or converting a detection rule

By analyzing data, AI can help security teams identify anomalies and patterns indicative of a cyber attack. Context-aware generative AI (GenAI), such as Elastic AI Assistant, can also explain alerts triggered by detection rules in easy-to-understand language.

Triaging and monitoring alerts

AI automates the time-consuming process of triage and monitoring by correlating related alerts into attack-level findings. Elastic Security’s Attack Discovery feature, for example, triages hundreds of alerts down to the few genuine threats and returns results in an intuitive interface. This allows security operations teams to quickly understand the presented attacks, prioritize threats based on severity and potential impact, and take immediate follow-up actions.

Investigating a cybersecurity threat

Whenever a threat is identified, AI can help security analysts perform key investigation steps. It provides a detailed description of the attack, summarizes hosts and users, displays related MITRE ATT&CK® adversary tactics, and more. GenAI can also create step-by-step remediation plans and streamline ad-hoc analysis and enrichment by generating or converting natural language into preferred program language queries.

Responding to a cybersecurity incident

AI enhances incident response by automatically executing predefined actions, such as isolating affected systems, blocking malicious IP addresses, or patching vulnerabilities. AI continuously learns from past incidents, improving its ability to detect and respond to future threats. GenAI can also suggest incident remediation steps to security analysts and help them document incidents.

Security practitioners use a wide range of AI techniques for cybersecurity. They include machine learning, NLP, RAG, LLMs, and behavioral analysis.

Machine learning in cybersecurity

Machine learning models identify patterns and zero in on anomalies that can indicate potential threats. For example, security teams use machine learning to monitor networks for potential breaches.

NLP

Natural language processing (NLP) enables AI systems to understand and process human language. This is crucial for tasks like analyzing threat reports, incident response, and vulnerability assessments. Threat intel analysts use NLP to analyze vast amounts of information from social media, news articles, and the dark web to identify potential threats and extract relevant details. This helps security teams to understand threat actors' motives and identify indicators of compromise (IoC), improving threat intelligence.

LLMs and generative AI in cybersecurity

Large language models (LLMs) are a type of deep learning model that powers many NLP applications, enabling them to interpret and generate human language. We increasingly see generative AI in cybersecurity to analyze threat data, help respond to incidents, and assist with documentation after a case has been resolved. In cybersecurity, LLMs also come with challenges: prompt injection attacks, data poisoning, and disclosure of sensitive data.

RAG

Retrieval augmented generation (RAG) is a technique that improves the accuracy of language models by combining document retrieval with language generation. RAG helps ensure that LLMs have the appropriate context they need to provide personalized, accurate, and relevant answers.

When a security analyst poses a question to the RAG system, it retrieves information from relevant cybersecurity sources, such as internal logs, threat intelligence feeds, vulnerability databases, internal incident reports, or other internal knowledge databases. Retrieved data is then run against the analyst’s query, providing the LLM with context and the latest relevant information. As a result, the LLM uses the augmented prompt to generate a context-aware, up-to-the-minute response.

Behavioral analysis

User behavior analytics (UBA) helps analyze user (and system) behavior to detect suspicious activities in real time. UBA gathers data from various sources such as log files, network traffic, and application usage to establish a baseline of normal behavior for each user. It uses machine learning and statistical modeling to detect deviations from this baseline. Over time, as UBA constantly updates user profiles, it learns and improves its ability to identify anomalies. This cybersecurity technique helps identify insider threats, malicious activity, and other security incidents before they have a chance to escalate.

AI greatly improves security teams’ efficiency and effectiveness. AI-driven security solutions can help security teams automate processes, adapt to evolving threats, improve proactive defense mechanisms and cyber resilience, as well as offer cost savings.

Improved threat detection

AI improves threat detection by identifying anomalies with greater accuracy and speed than human security analysts can alone.

Faster incident response time

Using AI, security analysts can automate response steps, receive context for potential incidents, and prioritize the attacks that matter most. With faster and more accurate threat detection, they can contain security breaches faster, identify root causes, and prevent future attacks.

Automation

AI can automate time-consuming tasks, freeing up security teams to focus on strategic objectives and complex cybersecurity incidents.

Reduced human error

By automating routine tasks, such as alert triage and monitoring, AI reduces the risk of human errors, improving the efficiency and accuracy of cybersecurity operations.

Improved scalability

AI significantly enhances scalability by automating time-consuming tasks, processing vast amounts of data, and continuously learning to adapt to evolving threats.

Cybersecurity teams use AI for a wide range of threat types, including phishing detection, fraud prevention, and network security.

Malware and phishing detection

AI-powered systems can detect malware and phishing attempts more effectively than traditional methods, especially when it comes to new or evolving threats (in particular, AI-fueled threats). With capabilities such as anomaly detection, contextual and behavioral analysis, and predictive intelligence, AI can identify and mitigate attacks faster and learn to distinguish between legitimate and malicious activities, minimizing false positives that can disrupt security workflows.

Endpoint security

AI can enhance endpoint security by learning the context, environment, and behaviors associated with specific devices, identifying anomalies and unusual behaviors. AI is particularly effective at detecting zero-day vulnerabilities, or potential attacks based on vulnerabilities that are not yet known to security teams.

Network and cloud security

AI is a great fit for both network and cloud security because of the large amounts of data involved. It helps to detect anomalies and threats, as well as avoid alert fatigue. AI analyzes vast amounts of data and dynamically adjusts security policies and access controls based on real-time threat assessments in a single pane of glass.

Fraud prevention

AI can be used to detect fraudulent activity, such as identity theft, payment fraud, and account takeovers. Similar to other cybersecurity applications, AI can reduce the number of false positive alerts teams receive and help with cost savings by reducing the need for lengthy, manual investigations and preventing fraud losses and reputational damage. AI can also identify complex fraud patterns that are difficult for traditional rule-based systems to detect.

Security operations

The wide-ranging implementation of AI technologies into virtually every aspect of the security stack is helping security teams work more efficiently to mitigate threats. AI is providing security practitioners access to insights they otherwise would never have had and profoundly changing their jobs, for the better.

Security admins, engineers, and analysts can more easily prioritize critical incidents, reduce alert fatigue, and accelerate investigations through real-time integrated threat intelligence, automated triage, and LLM-enhanced workflows. By automating away many of the time-intensive and mundane tasks, security teams can now focus on the priorities that truly matter and further strengthen their organization’s overall security posture.

With seemingly every vendor offering their own AI product, it can be hard to separate the artificial from the intelligent.

The first step is to understand the extent to which the AI offering will help your team and your security operations center (SOC). Start by answering these questions:

• Where in your existing security landscape can AI offer the most value?
• Which risks should you monitor based on the AI use case(s) you’ve identified?
• What are your specific goals for AI adoption?

Next, choose the right AI tools that fit your objectives, goals, security landscape, and current infrastructure, making sure your security team can sustain their new workloads. Finally, ensure data quality and privacy, compliance with regulations, and security.

Some vendors offer help switching from legacy systems to AI-driven products. Here, too, AI comes in handy with the migration process, helping migrate legacy detection rules and onboard custom data types in minutes — tasks which have traditionally taken security admins days or months to achieve. Elastic reduces the time and expertise needed to switch SIEMs with AI features like Automatic Import and Automatic Migration. Elastic AI Assistant helps lower the learning curve for security analysts and admins with guided workflow suggestions and complex query conversion.

Elastic Security’s AI-driven security analytics, built on the Search AI Platform and including RAG, is notable for its industry-leading AI features:

• Automatic Migration provides an AI-driven workflow for migrating legacy SIEM detection rules to Elastic Security.
• Attack Discovery holistically assesses incoming alerts to reveal advancing attacks and guides analysts to stop them.
• Automatic Import builds custom data integrations in minutes, including from any REST API.
• Elastic AI Assistant boosts security teams with context-aware guidance on alert triage, incident response, administrative tasks, and more.

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