What Is Generative AI?
A few years ago, “AI” mostly meant software that could sort, flag, or predict — spam filters, fraud scores, recommendation engines. Then a new category of tool showed up that didn’t just analyze information, it produced it: essays, images, code, even voices. That shift is generative AI.
Generative AI is a category of artificial intelligence that creates new content — text, images, audio, video, or code — by learning the statistical patterns in massive datasets and then producing original output that follows those patterns. Instead of classifying or predicting from existing data, a generative model is asked to make something new: write a paragraph, draft a function, summarize a document, or answer a question in natural language.
Most of today’s generative AI tools are built on large language models (LLMs) — the technology behind assistants like ChatGPT, Claude, and Copilot — or on image- and video-generation models trained on similar principles. What makes generative AI different from earlier AI systems is that it’s conversational and general-purpose: the same tool that drafts a marketing email can also summarize a contract, generate code, or answer a technical support question, all from a plain-language prompt.
For IT and security teams, that flexibility is exactly what makes generative AI worth understanding closely — the same openness that makes it useful also makes it a new kind of access control and data-handling problem.
What Are the Security Risks of Generative AI in the Enterprise?
Generative AI’s risk profile doesn’t look like traditional software risk. There’s no patch to apply for “a model might say the wrong thing” or “an employee might paste confidential data into a public tool.” The risks are behavioral and architectural as much as technical, and they tend to fall into a few recurring categories.
- Data leakage through everyday use. The most common generative AI risk isn’t a sophisticated attack — it’s an employee pasting a customer list, source code, or a draft contract into a public AI tool to save time. Once that data leaves the organization’s boundary, it may be logged, retained, or used to improve the vendor’s model, and it’s effectively outside enterprise governance. This is the core mechanism behind shadow AI — AI use that happens outside IT’s visibility entirely.
- Prompt injection. Attackers can embed malicious instructions inside content a model is likely to process — a document, an email, a web page, a support ticket — hoping the model treats that hidden instruction as a command rather than as data. Because generative AI systems are increasingly connected to other tools and data sources, a successful injection can result in leaked information or unintended actions, not just a bad response.
- Model and output manipulation. Generative models can be nudged toward biased, incorrect, or harmful outputs through carefully crafted prompts, and in fine-tuned or self-hosted deployments, poisoned training data can embed hidden behaviors that only trigger under specific conditions.
- Identity and access gaps. Many generative AI tools — copilots, coding assistants, autonomous agents — now act on a user’s behalf, calling APIs, reading files, or querying internal systems. Every one of those integrations is effectively a new identity with its own permissions, and organizations are only beginning to catch up on governing them. This is closely tied to the broader problem of non-human identity governance, and to AI agent access control specifically.
- Compliance exposure. Sending regulated data — health records, financial details, personal information — into a generative AI tool without proper controls can trigger the same violations as any other unauthorized data transfer, just with a friendlier interface. Frameworks like the OWASP Top 10 now include LLM-specific risk categories precisely because this exposure has become common enough to standardize around.
How Are Attackers Using Generative AI?
Defenders aren’t the only ones who’ve noticed what generative AI can do. Attackers have adopted it just as quickly — and in some ways, it suits offense better than defense, because generation is exactly what social engineering and malware development need.
Higher-quality phishing, at scale. Generic, typo-riddled phishing emails are becoming rarer. Generative AI lets attackers write fluent, personalized, context-aware lures in seconds — referencing a real project, a real colleague’s name, or a real recent event pulled from public sources. Combined with deepfake technology, some campaigns now include convincing synthetic audio or video of an executive to add urgency to a fraudulent request.
Faster malicious code generation. Generative AI can help less-skilled attackers produce functional malware, obfuscation techniques, or exploit code far faster than manual development would allow, lowering the skill floor for launching an attack.
Automated reconnaissance and pretexting. Attackers use generative models to quickly synthesize information about a target organization — org charts, technology stack, recent news — and turn it into a believable pretext for a device code phishing attempt or a social engineering call.
Credential and account-takeover attacks at volume. Because generative AI can automate the production of realistic-looking messages, login pages, and support scripts, it also scales attacks that used to require significant manual effort — password reset scams, help-desk impersonation, and similar identity-focused fraud.
The common thread is speed and believability. Generative AI doesn’t invent new attack categories so much as it removes the friction that used to limit how many of them an attacker could run at once.
How Can Organizations Secure Generative AI Adoption?
- Establish an AI usage policy that names which tools are sanctioned, what data can and can’t be entered into them, and who owns exceptions.
- Provide a sanctioned alternative. Employees adopt shadow tools largely because nothing approved meets the need — offering a vetted option significantly reduces unmanaged use.
- Extend access control principles to AI tools and agents, the same way you’d scope access for any new application — see what AI access control looks like in practice.
- Gain visibility into AI usage at the network level, since many shadow AI tools never touch an endpoint agent or SaaS security tool but do generate identifiable network traffic.
- Train employees on what not to paste into public AI tools, treating it as seriously as any other data-handling policy.