What is generative AI?
Generative artificial intelligence (GenAI) refers to a class of artificial intelligence (AI) systems that can create entirely new content. This could come in the form of text, images, audio, video, or code. Unlike traditional AI models that classify, recommend, or predict based on existing data, GenAI takes things a step further by producing original outputs that resemble the data it was trained on.
At the heart of many GenAI systems are models called large language models (LLMs), which are trained on massive datasets to learn patterns in language. These models don’t “understand” content the way humans do, but they’re very good at predicting what comes next in a sequence – like which word, sentence, or line of code should follow another – based on context.
Some well-known examples of generative AI in action include:
- Text generation: Tools like ChatGPT can write articles, emails, or summaries in natural language.
- Image generation: Models like DALL·E can create detailed images from a text prompt.
- Code generation: Tools such as GitHub Copilot assist developers by writing or completing code.
GenAI is evolving fast, and while many of its applications are creative or productivity-focused, it's also starting to play a role in cybersecurity.
How does generative AI work in cybersecurity
GenAI is beginning to reshape the cybersecurity landscape. On the defensive side, it’s helping analysts, engineers, and researchers work faster and smarter. On the offensive side, it’s enabling more convincing social engineering and automated attack techniques. As the technology matures, security teams are learning how to harness its power while staying alert to its risks.
Threat detection and analysis
Security analysts deal with massive volumes of alerts and logs – far more than any human team can reasonably handle. GenAI can help by summarizing, correlating, and even hypothesizing about suspicious activity. For example, an LLM might read through a set of security information and event management (SIEM) logs, identify patterns that resemble a known attack, and summarize the findings in plain language. This kind of assistance doesn’t replace the analyst, but it can accelerate triage.
Incident response support
In the middle of a security incident, speed and clarity are everything. GenAI can assist by generating response plans, drafting internal comms, or even scripting remediation commands based on incident details. Some tools use GenAI to walk responders through playbooks, adjusting based on the evolving situation. This helps teams act more quickly and consistently under pressure.
Threat intelligence and reporting
Keeping up with the latest threat intelligence can feel like drinking from a firehose. GenAI can digest large volumes of unstructured information – from threat feeds, forums, blogs, and more – and turn it into structured summaries. It can also help automate the creation of tailored threat reports for specific stakeholders, helping to create more actionable intel across the business.
Social engineering and phishing
Unfortunately, attackers can also use GenAI to craft more believable phishing emails, deepfake voice messages, or fake profiles. What used to take time and skill to create can now be generated quickly and at scale. This raises the bar for detection and makes awareness training even more critical.
Security automation
GenAI is helping streamline routine security tasks. For instance, it can generate scripts to automate configuration checks, policy enforcement, or vulnerability scans – based on natural language prompts or observed workflows. Combined with traditional automation tools, this enables teams to rapidly prototype and deploy automation without starting from scratch every time.
Training and simulation
Cybersecurity training often relies on realistic scenarios, and GenAI can create them on demand. From crafting phishing simulations to generating dynamic adversary behavior in red team exercises, AI can help teams prepare for a wide range of threats.
How attackers use GenAI
Just as GenAI is proving useful for defenders, it’s also becoming a powerful tool for attackers. These models lower the barrier to entry for less experienced threat actors and help skilled ones scale their operations faster.
Phishing and social engineering
One of the most immediate uses of GenAI by attackers is in creating highly convincing phishing content. Instead of relying on clunky, error-filled messages, threat actors can now generate emails that are grammatically correct, tailored to a specific industry, and even mimic a target’s writing style. Some attackers use AI to create fake LinkedIn profiles, write scripts for vishing calls, or generate documents that appear to come from legitimate sources – all of which make social engineering more effective and harder to spot.
Malware generation
While GenAI models can’t (yet) reliably build fully functional malware from scratch, they can assist attackers in piecing together components. For example, an attacker might prompt an AI model to generate code snippets, payload templates, or variations of known malware to evade signature-based detection. Some LLMs can also help with scripting behaviors in multiple programming languages, making it easier to build polymorphic or modular malware at scale.
Exploit development assistance
GenAI doesn’t replace deep technical knowledge, but it can act as a coding copilot for attackers. Threat actors may use it to help identify misconfigurations, write proof-of-concept code for known vulnerabilities, or translate exploit instructions across languages and platforms. This can significantly speed up the development process for zero-day or one-day exploits, especially for less experienced adversaries who previously lacked the skills to weaponize vulnerabilities.
Benefits and risks of GenAI in cybersecurity
GenAI holds tremendous promise for improving cybersecurity workflows. But like any powerful tool, it comes with trade-offs. Understanding both the advantages and the potential pitfalls is essential for using AI responsibly and effectively in security contexts.
Key benefits
- Speed: GenAI can process and summarize vast amounts of data in seconds, helping analysts spot issues and make decisions faster.
- Scale: AI enables security teams to automate responses, generate reports, and analyze threats across large environments without a proportional increase in headcount.
- Multilingual context handling: Many generative models can understand and generate text in multiple languages, which is useful for analyzing global threat intel and adversary communications.
- Reduce analyst toll: By handling repetitive or low-level tasks – like drafting tickets, summarizing alerts, or writing basic scripts – AI helps free up human analysts to focus on higher-order work.
Risks and limitations
- Hallucinations and inaccuracy: Generative models sometimes produce outputs that sound plausible but are factually incorrect, which can mislead users if not caught.
- Over-reliance on unvetted output: Using AI-generated content without review - especially in sensitive areas like incident response or compliance - can introduce errors or unintended consequences.
- Model poisoning and jailbreaks: Adversaries can attempt to manipulate model behavior by feeding in malicious prompts (jailbreaks) or poisoning training data to produce biased or unsafe outputs.
GenAI vs. traditional automation in cybersecurity
Traditional automation in cybersecurity – like the kind used in security orchestration, automation, and response (SOAR) platforms or SIEM systems – relies on clearly defined logic. These systems follow deterministic playbooks: if X happens, do Y. They’re reliable, repeatable, and great at handling well-understood tasks at speed and scale.
GenAI, by contrast, brings a layer of adaptability and reasoning that deterministic systems can’t match. Instead of executing predefined actions, generative models can summarize alerts, suggest response actions, write scripts, and even explain why a particular step might be appropriate in context.
How GenAI augments SOAR/SIEM
LLMs can be layered on top of existing tooling to provide human-friendly interfaces and context-aware analysis. For example:
- In a SIEM, an LLM might summarize alert clusters or explain an unusual spike in activity in plain language.
- In a SOAR platform, it might auto-generate playbook suggestions based on the nature of the incident, or draft the remediation steps for human review.
Rather than replacing these systems, GenAI extends their value, especially in environments where analysts are short on time and overwhelmed with data.
Where LLMs fit in the incident lifecycle
GenAI can assist across multiple stages of the incident response lifecycle:
- Detection and triage: Summarizing logs, clustering alerts, identifying potential indicators of compromise (IOCs).
- Investigation: Explaining tactics and techniques, proposing hypotheses, drafting analyst notes.
- Containment and remediation: Generating scripts or commands for approval, walking responders through procedures.
- Post-incident review: Drafting reports, translating technical outcomes for different audiences, or suggesting process improvements.
Used wisely, LLMs act like smart assistants, speeding up response while providing relevant context.
When to trust — and when to verify
Despite their usefulness, generative models are not infallible. They can make mistakes, fabricate details, or misunderstand intent. That’s why it’s critical to apply human judgment, especially for decisions that carry legal, financial, or operational consequences.
As a general rule:
- Use LLMs for summarization, ideation, and low-risk automation.
- Always verify outputs when accuracy, safety, or compliance are on the line.
By treating AI as a collaborator – not an oracle – teams can get the best of both worlds: faster operations without compromising trust.
Future outlook on GenAI in cybersecurity
As generative AI continues to mature, its role in cybersecurity is poised to grow far beyond today’s early use cases. What we’re seeing now – automated summaries, intelligent chat interfaces, and contextual code suggestions – is just the beginning. The next frontier lies in systems that go beyond generation into decision-making: Agentic AI that can initiate tasks, adapt based on feedback, and coordinate across tools without constant human prompting.
In the security operations center (SOC), this shift could transform the way analysts work. We’re already seeing the rise of SOC copilots – AI agents trained to act as sidekicks for human defenders. These tools can guide analysts through investigations, recommend next steps, and even automate containment measures with a human-in-the-loop (HITL).
But with greater capability comes greater responsibility. As AI systems gain influence over critical security decisions, organizations will need to grapple with questions of trust, accountability, and transparency. Regulatory frameworks are beginning to emerge, and ethical considerations – like avoiding bias in models or ensuring explainability – are coming to the forefront. Security leaders will need to balance innovation with governance, ensuring their use of GenAI aligns with both organizational goals and societal expectations.