What is Gen AI Security…And Where is It Taking Us?

Generative artificial intelligence (Gen AI) is brand new. So, what is Gen AI security? Its novelty might be why it’s often used to mean 2 different things: 

• Protecting generative AI models from attack
• Using generative AI models to protect all kinds of assets from attack

We’ve covered some components of AI security in general, especially how teams can get started protecting AI workloads. Here, we’ll go in depth about how teams are using generative AI in particular — the kind of intelligence that extrapolates novel outputs from patterns (not just analyzes or rates existing data) — to guard against cyber attacks, but also to protect their own resources in the cloud.

What is Generative AI (Gen AI) in Cybersecurity?

Generative AI produces data. And that’s novel, since previous security models used traditional AI to analyze data, scouring logs for anomalies, for instance.

Today, Gen AI makes its own data, so it can create incident reports, suggest remediation, and even write the code that fixes security issues. Examples include:

• Auto-generating incident summaries based on raw log files, like summarizing a brute-force login attack from a week of access logs.
• Proposing remediation steps for a misconfigured S3 bucket, such as rewriting IAM policies or suggesting rule changes to block public access.
• Writing and validating code to patch a vulnerable API endpoint after a known Common Vulnerability and Exposure (CVE) is detected, sometimes even with test cases included to verify that patches work.

In security applications, Gen AI can also use machine learning models based on Generative Adversarial Networks (GANs), the machine learning models comprised of 2 competing neural networks that create and detect data with one another, improving until generated data can’t be discerned from real inputs.

Those models are useful in the following scenarios: 

• Creating fake-but-realistic data from logs to payloads and user behavior, all crafted to help train other security models. For example, those models may be working to detect credential stuffing attacks.
• Simulating realistic attack scenarios from step to step, so teams can test how their current defenses might react to scenarios like a ransomware or malware attack starting from a phishing email.
• Mimicking behavior in real time in an autonomous adversarial simulation, with Gen AI taking on the role of an attacker and helping teams hone a live response.

Here are some key terms and distinctions in this brave new world: 

Gen AI vs GAI

Let’s clear up a common confusion.

Gen AI includes any generative model that produces content, from text to images or code. 

GAI is used informally as shorthand for Generative AI, but technically, it means General AI (or AGI) and refers to hypothetical human-level reasoning systems.

In other contexts, multimodal GAI means models that can process and output across formats — text, images, audio, and video.

Single-Modal vs Multi-Modal AI 

So far, most Gen AI in security has focused on large language models (LLMs) that are single-modal systems trained on text. Before Gen AI, the job of AI in security was mainly predictive: flagging anomalous behavior in logs or events.

Now we’re seeing tools that both predict and generate. Instead of saying, “Something weird happened,” they can also explain what, when, and why, and even suggest a fix. Most current tooling is still single-modal and text-based, but that’s also changing. As models mature, we’ll see more tools that can:

• Combine log analysis with visual network diagrams
• Generate fake phishing webpages as part of simulations
• Interpret audio-based social engineering attempts 

Predictive vs Generative AI

Even within a single modality, the fusion of predictive and generative AI is transforming how security teams detect and respond to threats. Today, security tools using AI can use predictive capabilities to find anomalies in data, then use generative capabilities to write summaries of incidents and suggest remediation efforts. These tools can create “threat stories” that detail what happened in plain language and direct teams on how to take action, speeding remediation.

The Four Frontiers of Generative AI Security

As AI transforms the security stack, new capabilities are emerging that go beyond posture improvement. These aren’t just upgrades to existing tools, but fundamental restructurings of how teams will approach testing, response, remediation, and overall security posture in the future.

At its core, Gen AI is helping equip humans with clearer insight: understanding what’s happening, what to do about it, how to validate it, and when to challenge the model’s assumptions. What GenAI offers is the ability to interpret complexity and surface what was previously invisible.

New research promises breakthroughs in Gen AI training models to recognize even Zero-Day threats — threats that are novel and for which there are no models with which to train LLMs.

Even without examples, Gen AI can extrapolate meaningful patterns and simulate high-risk scenarios, so defenders can prepare for the unknown. That’s where the first major shift is already happening: triage.

Triage, Understanding, and Action

Security teams are inundated with logs, alerts, and telemetry. But data doesn’t always lead to insight. Gen AI offers a path forward: it transforms raw signals into plain-language insights that help analysts focus on action rather than deciphering tomes of raw data.

For example, Gen AI can expand on predictive AI’s parsing of thousands of log entries by outputting a narrative of what happened and when. That allows human analysts to move from reacting to alerts to incident ownership. 

Automated Remediation Guidance and Secure Code Suggestions

While traditional AI flags issues, Gen AI begins to close the loop by recommending and even generating fixes using automation. The approach closes the gap between detection and response. For example, Gen AI could rewrite a vulnerable Terraform script or Identity and Access Management (IAM) policy based on best practices, including an explanation and optional test case.

Adaptive Red Teaming and Threat Simulation

When Gen AI is trained to act like an attacker, it lets red teams and security testers adapt to malicious behaviors. Gen AI can generate realistic payloads, mutate phishing attempts, or simulate multi-stage attack chains based on the target environment. For instance, it can craft a phishing email tailored to a company’s internal style, or model how ransomware could spread laterally after an initial compromise with a deep understanding of the environment.

Context-Aware Decision Support and Incident Response

The most visible (and hyped) frontier is the rise of security copilots: AI interfaces embedded in tools, dashboards, and chat interfaces. These copilots are there to interpret context, surface risk, and guide decision-making with near-instant answers to questions that can guide better human decision-making. Analysts might ask Gen AI, “What’s the blast radius of this exposed API key?” or “What are the next most likely steps an attacker might take after gaining access with this foothold?” 

From Using Gen AI to Secure Systems to Securing Gen AI Itself

So far, we’ve focused on how Gen AI is transforming security workflows. But there’s a second, equally urgent frontier emerging: the security of Generative AI applications themselves.

As organizations embed LLMs into apps, infrastructure, and user-facing tools, they’re introducing entirely new attack surfaces that often fall outside the purview of traditional application security. And many of those surfaces are invisible to traditional security tooling.

Most cloud-native security platforms and broader cloud security strategies were never designed to monitor or protect AI workloads, especially as newer AI technologies introduce dynamic behaviors and novel security risks. These systems behave differently, expose new interfaces, and rely on models that interact unpredictably with prompts, data, APIs, and embedded functionality, and pathways that traditional firewalls and perimeter defenses can’t easily monitor.. That’s created a growing gap in visibility, governance, control, and exposure to security risks. Let’s map prominent risks:

LLMs Can Leak Sensitive Data 

LLMs can regurgitate confidential or proprietary information, raising serious data security concerns. Sometimes they divulge this information unintentionally, but sometimes it happens by design. Models that retain prompt history or offer auto-generated “examples” can surface customer data, internal documents, or even source code. Worse, they can be intentionally coerced into revealing sensitive data via prompt injection or adversarial chaining.

Misconfigured AI APIs Allow Shadow Integrations

AI services frequently interface with third-party or open-source APIs, often without SOC visibility. It’s a reality that introduces hidden supply chain risks that can evade traditional monitoring. A developer prototyping with public LLM APIs might connect internal datasets to external models with no formal review. That customer-facing chatbot? It may be exfiltrating Personally Identifiable Information (PII) to a model hosted in another jurisdiction without ever triggering a security event.

Model Endpoints are Often Over-Permissioned

AI workloads often run under service accounts with broad permissions and insufficient security controls, particularly weak access controls that exceed what inference or fine-tuning should require. And CSPMs tend to ignore these misconfigurations because the services look legitimate. But when that model gets exploited via prompt injection or plugin misuse, it becomes an entry point with high-value access.

AI Systems are Dynamic

AI workloads evolve: they’re non-static, non-linear, and non-deterministic. They learn from user input, change with usage patterns, and expand their own capabilities through plugin architectures. Unfortunately, that behavior drift after deployment is rarely tracked across the model lifecycle. That highlights the need for continuous monitoring to detect changes and prevent model misuse. And once models start making operational decisions, or even shaping customer-facing experiences, those drifts can have serious consequences.

The Future of Gen AI Research

AI workloads aren’t going anywhere. Neither are AI cybersecurity defenses. Sadly, attackers aren’t holding back either, so the future of Gen AI for cybersecurity is wrapped up in the confluence of these three rapidly accelerating trajectories all heading in one direction: the exponential expansion of Gen AI use.

Beyond that? Here’s what researchers are working on in 2025. 

Integration with IoT Complexity

Researchers call the use of Internet of Things (IoT) devices a “virulently expansive landscape” that’s growing at a rate that traditional methods won’t be able to match. Gen AI will be key to managing that added complexity, helping scale the defense of these devices and enabling their continued growth.

Focus on AI-Enabled Threats and AI-Based Defenses

Gen AI won’t just deploy machine learning against threats; it’ll also have to counter AI threats from AI-powered hackers, ransomware-as-a-service platforms, malware variants designed using Gen AI, deepfakes, and other adversarial AI use cases. One 2024 report details how Dark Web marketplaces have quickly transformed to accommodate Gen AI tools meant to be used maliciously; organizations on the defensive will have to keep up to protect against Gen AI threats.

Advancing Techniques like GANs,  Variational Autoencoders (VAEs), and Reinforcement Learning

One way that defensive AI analysts are helping thwart AI threats is through advanced models. They hope to model threats more realistically, simulate and detect novel attack vectors, and generate synthetic data for model testing and training that acts more naturally. Better datasets are key to filling gaps where models once couldn’t tread, making these methods an up-and-coming approach to threat detection.

Dual Analysis: Opportunity vs. Threat

Gen AI platforms are both an opportunity and a threat. Teams must contend with that reality, asking new questions about the ethical use of Gen AI, how to prevent its use in adversarial, automated attacks, and what regulatory and governance frameworks to embrace. Here, too, synthetic data sets are often a solution to some of the questions being raised by Gen AI’s ability to access, correlate, and use data. But so are the benefits of using Gen AI to ethical ends, like customer data protection.

Cross-Sector Collaboration

The future of Gen AI security is one in which industry, academia, and government collaborate toward more systems-level, ethics-based, and cooperative strategies. That only makes sense, with risks threatening international cooperation and government goodwill filtered through the lens of organizational trust.

Gen AI is A Turning Point for Security Solutions

Generative AI will change more than the tools currently used in cybersecurity. It stands to shift the speed and tactics, too. Organizations using AI apps will invariably need to outpace Gen AI adversaries and find attacks faster, but they’ll also need to foreground transparency, making sure their Gen AI can explain itself and that it can’t be misled. That goes for the Gen AI used in cybersecurity as well as Gen AI workloads that organizations need to secure.

Ultimately, the next generation of cybersecurity won’t just rely on Gen AI. It will be defined by how well it understands and can secure it.