Generative AI for Cybersecurity
Discriminative, Generative & Agentic AI
Understanding three important approaches to artificial intelligence and how they can support cybersecurity detection, analysis, automation, investigation, and decision-making.
Understanding AI Approaches
Artificial intelligence can support cybersecurity in different ways. Some AI systems focus on identifying patterns and classifying security events, while others generate new content or assist with multi-step tasks and workflows.
Discriminative AI, Generative AI, and Agentic AI represent useful ways of thinking about these capabilities. Understanding the differences helps cybersecurity professionals identify where AI can provide useful assistance while maintaining appropriate human oversight.
Three Approaches to AI
Although these approaches can overlap in modern AI systems, they emphasize different capabilities and can support different cybersecurity tasks.
Discriminative AI
Focuses on distinguishing between categories or predicting outcomes from existing data. In cybersecurity, it can help classify alerts, detect anomalies, identify malicious activity, and prioritize security events.
Generative AI
Focuses on generating new content such as text, explanations, summaries, code, or structured reports. In cybersecurity, it can assist with log analysis, threat explanations, incident summaries, and security reporting.
Agentic AI
Focuses on completing multi-step tasks by reasoning, planning, using tools, and working toward a defined objective. In cybersecurity, agents can assist with investigation workflows and other authorized security operations.
What Is the Difference?
A simple way to understand the distinction is to consider the primary task each approach performs.
Discriminative AI in Cybersecurity
Discriminative AI learns patterns that help distinguish between different classes or outcomes. This makes it useful for security detection and classification tasks where the system needs to decide whether an observed event belongs to a particular category.
Threat Detection
Classify network or system activity to help identify potentially malicious behavior.
Anomaly Detection
Identify activity that differs from expected patterns and may require further investigation.
Malware Classification
Assist in distinguishing potentially malicious files or behaviors from benign activity.
Alert Prioritization
Help security teams prioritize alerts based on predicted characteristics or risk indicators.
Generative AI in Cybersecurity
Generative AI can produce new content based on patterns learned from existing information. Large language models are particularly useful for working with natural language and can help cybersecurity professionals understand, summarize, explain, and transform security-related information.
Security Log Analysis
Assist analysts in interpreting large or complex collections of security logs and highlighting potentially relevant events.
Threat Intelligence
Summarize and explain threat intelligence information to help analysts understand potential threats and indicators.
Incident Reporting
Transform technical investigation findings into structured and understandable incident reports.
Security Knowledge Assistant
Provide interactive explanations of cybersecurity concepts, technologies, vulnerabilities, and investigation techniques.
Agentic AI in Cybersecurity
Agentic AI refers to systems designed to work through multi-step objectives. Rather than only generating an answer, an AI agent may plan a sequence of tasks, use authorized tools, evaluate results, and continue working toward a defined goal.
In cybersecurity, this approach could support investigation workflows where multiple sources of evidence need to be examined before an analyst can reach a conclusion.
Example: AI-Assisted Investigation
In a controlled environment, an agent could help gather relevant information, analyze available evidence, and prepare findings for analyst review. Any actions that could affect systems should be appropriately authorized and subject to human oversight.
AI Across the Security Workflow
Different AI approaches can complement one another within a cybersecurity workflow.
Human Expertise + AI
AI can enhance cybersecurity workflows, but effective security requires appropriate human judgment. AI-generated analysis should be validated, particularly when decisions could affect systems, users, investigations, or organizational risk.
AI
Analyze, summarize, correlate
Analyst
Validate and interpret
Security Team
Make informed decisions
Organization
Manage security risk
Practical Applications
The combination of these AI approaches can support several areas of cybersecurity practice.
Security Operations
Assist SOC analysts with alert classification, investigation, summarization, and security reporting.
Threat Analysis
Analyze security events and identify relationships between potentially suspicious activities.
Vulnerability Assessment
Assist with vulnerability interpretation, risk analysis, prioritization, and remediation guidance.
Cybersecurity Education
Support cybersecurity capacity building through interactive explanations, scenarios, and learning assistants.
Key Takeaways
- Discriminative AI is primarily focused on classification, prediction, and distinguishing between different outcomes.
- Generative AI can create, summarize, explain, and transform security-related information.
- Agentic AI focuses on multi-step tasks, planning, tool use, and workflow execution toward defined objectives.
- These approaches can complement one another across cybersecurity detection, analysis, investigation, automation, and learning.
- AI-generated results should be validated and used with appropriate human oversight.
- The most useful cybersecurity applications focus on augmenting security professionals rather than removing appropriate human judgment.