Generative AI for Threat Modeling and Incident Response
A few years ago, most of us associated “generative AI” with artistic endeavors—painting surreal portraits, composing music, or even writing short stories. Fast-forward to today, and we’re seeing these same generative techniques turn into powerful tools for cybersecurity. It’s a bit ironic that the technology once used to create whimsical cat images is now helping us spot sophisticated threat vectors and respond to real-world incidents.
But is this convergence of generative AI and cybersecurity merely hype? Or are we on the cusp of a new era in threat modeling and incident response—one that could drastically reduce the average time to detect and mitigate attacks? I’m going to make the case that generative AI is poised to become a game-changer in both identifying new threats and orchestrating efficient, data-driven responses. Yet, like any emerging tech, it’s not without its pitfalls. Let’s dig in.
Thesis: Generative AI’s unique ability to synthesize patterns, predict novel attack vectors, and automate response strategies will significantly enhance our threat modeling and incident response capabilities—but only if we tackle challenges around reliability, ethics, and data governance head-on.
Cyber threats evolve at breakneck speed, and traditional rule-based or signature-based systems often lag behind. Generative models (like advanced large language models) can detect anomalies and hypothesize potential future attack patterns far beyond the scope of conventional heuristics. However, they also introduce new vulnerabilities, such as the possibility of “hallucinating” false positives or inadvertently generating malicious code. We must approach these capabilities with equal parts excitement and caution.
Threat modeling beyond known signatures
Why generative AI for threat modeling?
Traditionally, threat modeling has depended on known attack signatures, historical patterns, and human expertise. But the surge in polymorphic malware, supply chain vulnerabilities, and zero-day exploits makes purely reactive methods inadequate.
Enter generative AI. While “generative” often implies large language models (LLMs) in today’s parlance, it can also include other algorithms capable of producing new data patterns. These models spot subtle correlations in massive telemetry datasets—things like suspicious command sequences, lateral movement attempts, or exfiltration patterns. Importantly, they aren’t limited to explicit labels of what’s “malicious.” Instead, they learn the underlying distribution of “normal” behaviors and can flag anomalies that haven’t been explicitly listed as threats.
However, detecting anomalies is only the first step. If no one has labeled certain behaviors as malicious or benign, an LLM (or any generative approach) may need to be chained with additional classifiers or heuristic checks to confirm whether something is truly nefarious or just unusual. For example, it could hypothesize that a new lateral movement pattern is suspicious, but some organizations legitimately use a jump host that’s rarely accessed—making the anomaly harmless in context. Ultimately, generative AI excels at surfacing possibilities beyond conventional signatures, but it must be paired with robust decision logic—either automated or human-led—to determine which anomalies represent real threats.
A real-world trial: F5 sandbox environment
At F5, we ran a controlled simulation in late 2024 to see how a generative AI model might perform in an evolving threat environment. We fed the internal F5 testing environment anonymized log data from a multi-tenant environment, purposely injecting new, previously unseen attack patterns. Initially, the model generated a few “false alarms” (these models can be over-eager), but with iterative training, it started detecting anomalies with better precision than our baseline signature-based system. The truly impressive part? It also flagged potential exploits that even our blue team analysts hadn’t considered—like certain lateral move attempts disguised under normal file-sharing protocols.
Shortening the ‘mean time to remediate’
Adaptive response playbooks
Generative models aren’t just for detection—they can quickly spin up suggested playbooks when incidents occur. Think of it as an AI collaborator that monitors real-time logs, merges intelligence from multiple data sources, and proposes a coordinated response plan.
For instance, if the system detects a high-risk anomaly, it can recommend dynamic firewall policies or quarantining suspicious virtual machines (VMs). Because it learns from past incidents, these suggestions refine over time. That’s a big step beyond static runbooks that rarely get updated after initial setup.
Orchestrating tools at scale
We’ve seen a recent wave of generative AI integrations at major security conferences (like Black Hat 2024 and the newly launched AI-SecOps Summit this year). They’re focusing on “autonomous” or “agentic” responses, where an AI layer orchestrates multiple security tools—SIEMs, endpoint protection, WAFs—in real time. If multicloud is the norm these days, a single generative model that coordinates threat responses across AWS, Azure, and on-prem environments starts looking very appealing.
But here’s the catch: If we’re just automating outdated or inefficient processes, we risk “failing faster” instead of improving our overall security posture. By adopting AI without rethinking fundamental workflows, we might accelerate the execution of flawed procedures. That’s why AI should be viewed as a catalyst to reimagine how we approach security and delivery, rather than merely speeding up what we’re already doing.
It’s also worth noting that just because a generative model can automate a response doesn’t mean it should. We need guardrails and escalation paths to ensure humans remain in the loop for critical decisions (like isolating entire segments of production). In short, generative AI presents an exciting opportunity to challenge old assumptions and design more efficient, adaptive incident response frameworks—if we’re willing to update the very foundations of our processes, not just the speed.
Current buzz in 2025
- LLM wars continue: The top cloud providers are vying to release the most “secure” large language model (LLM), each claiming advanced fine-tuning to reduce hallucinations and improve compliance.
- Open-source projects: Community-driven initiatives (like AI-SIGS or ThreatGen) are emerging, giving security researchers open-source frameworks to build custom generative threat models.
- Regulatory spotlight: The EU’s new AI Regulation proposals place generative cybersecurity applications under “high-risk” technology, demanding transparency and explicit data provenance.
It’s a heady time: we see more adoption by large enterprises and mid-tier players eager to leapfrog legacy solutions. But the rush to deploy generative AI can lead to issues if organizations skip fundamental steps like robust data governance, model explainability, and accountability frameworks.
Hallucinations, privacy, and ethical dilemmas
While generative models can dazzle with creative inferences, they can also “hallucinate” plausible-sounding threats that don’t exist. A wave of false positives could bury your security ops team in an avalanche of meaningless alerts. Over time, this can erode trust in AI-driven systems.
Training robust models requires data—lots of it. Depending on the region, privacy laws might restrict which logs or telemetry can be used. Scrubbing or anonymizing sensitive user data is essential to avoid compliance nightmares and to uphold ethical data use.
We can’t ignore the darker flip side: Threat actors can (and have) used generative AI to draft convincing phishing emails, develop new forms of malware, or discover zero-days more quickly. As we build these defensive capabilities, we must assume attackers are doing the same on offense.
Embracing the future
Looking ahead, generative AI could evolve into a standard layer in every SecOps toolkit, integrated as tightly as vulnerability scanners or intrusion detection systems are today. With improvements in explainability—think more transparency about how the AI reaches its conclusions—security teams will feel more confident letting AI handle greater parts of the threat modeling lifecycle.
We might also see global threat intelligence coalitions, where generative models trained at different organizations share partial insights while maintaining privacy. That could lead to faster, more coordinated responses to zero-day attacks across industries.
Still, it’s worth reiterating that we’re in the early days. Organizations must invest in data best practices, robust training pipelines, and well-defined governance before relying on generative AI in mission-critical scenarios.
Ready to rethink security?
Generative AI has the potential to upend traditional approaches to threat modeling and incident response. By creatively analyzing patterns and automating response strategies, it can deliver unparalleled speed and accuracy—assuming we address inherent challenges like hallucinations, compliance, and ethical safeguards.