The exponential growth of GenAI over the past two years has been nothing short of transformative. AI is now everywhere. It’s embedded in our productivity tools, powering new applications, and automating decisions across industries. But as adoption surges, so do the risks. Attack surfaces have expanded, threat actors have adapted, and the need to secure AI systems has never been more urgent.
In all the buzz surrounding AI, it’s easy to get lost in the noise. “AI security” is a trending term, but what does it really mean? And more importantly, what does it not mean?
This blog explores the difference between traditional security powered by AI and what’s actually needed to secure AI systems themselves. We’ll look at how security for AI can be broken down into two key categories: securing employee use of third-party GenAI and securing the actual AI models, applications, and agents. The goal is to provide a practical framework to understand where the risks are and what needs to be done to manage those risks.
Security for AI vs. AI security
Let’s start by drawing an important distinction.
AI security tools often refer to traditional security tools that have been enhanced with AI. From firewalls to endpoint detection to cloud security, AI is now built into virtually every security solution. These AI-powered tools help detect threats faster, automate responses, and improve decision-making. They’re an important part of modern security infrastructure, but they aren’t built to protect AI itself.
Security for AI is entirely different. It refers to the purpose-built tools designed to secure AI models, applications, and agents. These systems have unique attack surfaces and failure modes that traditional tooling doesn’t cover. Think prompt injection, model extraction, data leakage, and other adversarial attacks that don't exist in your typical web app.
In short: AI in security does not equal security for AI.
The security for AI landscape
Securing AI involves protecting both the way employees use third-party AI tools and the way your organization builds and deploys AI-powered systems.
We can break this down into two major ecosystems:
- Tools that secure employee use of third-party GenAI
- Tools that secure homegrown AI apps and agents
In the following sections, we look at both of these segments in more detail.
Security ecosystem for employee use of third-party GenAI
Whether it's ChatGPT, GitHub Copilot, Adobe Firefly for images, or other AI-enabled SaaS tools, your employees are already using GenAI. This usage is often under the radar, and it can introduce serious risks if left unchecked.
Securing employee use of third-party GenAI starts with visibility and control. Shadow AI discovery tools help identify which AI-enabled tools are being accessed, by whom, and how often. This creates a baseline understanding of your organization’s exposure, especially in environments where users may copy sensitive code, customer data, or intellectual property into AI interfaces.
Once discovered, the next step is policy enforcement and data protection. Organizations must implement Zero Trust Network Access (ZTNA) and Secure Service Edge (SSE) solutions that can restrict access to high-risk AI tools or apply fine-grained controls to monitor data flows. In parallel, Data Loss Prevention (DLP) tools should be configured to prevent sensitive information from being input into untrusted AI systems, including anything that could create compliance or IP leakage risks.
Audit and compliance logging is also essential. As AI becomes more embedded in business processes, organizations need to demonstrate that employee use aligns with internal policies and external regulations. Logging interactions with third-party AI systems allows for accountability and enables investigations if a data leak or policy violation occurs.
Finally, security teams should educate employees on safe usage practices and continuously adapt controls based on emerging AI capabilities. As AI tools evolve, so too must the guardrails that keep them in check.
In short, securing employee GenAI use is about managing risk without stifling innovation. It’s a balancing act that starts with visibility and ends with smart enforcement.
Security ecosystem for homegrown AI apps and agents
Whether you are using public, private, or homegrown AI models, building your own AI-powered apps and agents comes with its own set of challenges. It also comes with new attack surfaces. From model configuration to deployment and runtime protection, securing AI systems themselves requires a multi-layered approach.
This category can be divided into four core functions:
- AI discovery
- AI scanning
- AI testing
- AI protection
Each of these covers a specific area of security for AI. Let’s look at them in more detail.
AI discovery
You can't protect what you can't see. That’s the foundational principle behind AI discovery. Before you can secure your AI systems, you need full visibility into what exists across your organization. This includes identifying all AI models, datasets, underlying infrastructure, and who has access to them.
AI discovery tools help organizations map their entire AI footprint. These tools automatically detect where models are deployed, what data they rely on, how they’re configured, and which systems they’re connected to. They also uncover access patterns such as who can query the model, who can retrain it, and who can modify its outputs.
AI discovery is the first step toward securing the modern AI stack. Without it, models can be misconfigured, sensitive training data might be exposed, and unauthorized access could go unnoticed. These risks increase when models are deployed in production environments without centralized oversight.
Whether you are using open-source models, building your own large language models (LLMs), or fine-tuning commercial ones, AI discovery provides the baseline visibility needed to detect misconfigurations, prevent data leakage, and enforce policy controls.
AI scanning
Once you have visibility into your AI assets, the next step is assessing them for vulnerabilities. AI scanning is the process of identifying risks in AI models, their supporting files, and the environments they run in.
Many AI models are packaged and shared using serialization formats like Pickle, PyTorch, or ONNX files. These formats may include embedded code that executes when the model is loaded. Without scanning these files, it’s possible for attackers to embed malicious payloads that trigger remote code execution the moment the model is used.
AI scanning tools inspect these files for known CVEs, malicious scripts, and unusual artifacts. They operate much like traditional static application security testing (SAST) or software composition analysis (SCA), but are adapted specifically for AI file types. In addition to model files, these tools can also analyze dependencies, data preprocessing pipelines, and configuration files that govern how a model is used.
Another critical function of AI scanning is the generation of an AI Software Bill of Materials (AI SBOM). This creates an inventory of all components within a model, including libraries, datasets, and any third-party modules. Just as SBOMs are now the standard for supply chain security, AI SBOMs are becoming essential for traceability, compliance, and risk mitigation in AI systems.
The bottom line: AI models are not just mathematical artifacts. They are executable code bundles that can be tampered with. Scanning them is a necessary step to ensure that what you deploy into production isn’t hiding malicious surprises.
AI testing
AI testing during application and agent development helps you understand how AI models behave in real-world adversarial conditions. This helps you uncover security weaknesses before they can be exploited in production systems.
Unlike traditional software, AI models don’t follow hard-coded logic. This makes their behavior harder to predict and test using conventional methods. AI testing fills this gap by simulating attacks and edge cases to probe the model's responses and uncover vulnerabilities in its logic, output, or structure.
This category includes AI auto red teaming. Auto red teaming simulates realistic attacker techniques, such as prompt injection, model inversion, or data extraction, to see how the model responds. The goal here is to throw a variety of unpredictable or malformed inputs at a model to study how it fails and whether it fails safely.
Testing is also a key step in understanding how models treat sensitive content, personal data, or harmful language. It helps security teams and developers determine if a model could be coerced into violating usage policies, leaking confidential information, or generating toxic content.
Ultimately, AI testing is not just about identifying known vulnerabilities. It’s about learning how your model behaves under adversarial stress. This insight is essential for refining model design, enforcing safety boundaries, and preparing for production deployment. Just as QA testing is standard for traditional applications, adversarial testing must become a standard part of the AI development lifecycle.
AI protection
Once an AI model is deployed, real-time protection becomes critical. This is where AI protection comes into play. While discovery, scanning, and testing help prepare models for production, protection is what keeps them safe once they’re live and exposed to real-world inputs.
AI protection is sometimes referred to as GenAI runtime defense, AI application firewalls, or AI guardrails. It focuses on monitoring and defending AI systems in production environments, acting as a defense layer between the model and potential threats, analyzing inputs and outputs in real time, and responding to malicious activity as it happens.
The threats faced in production systems are very different from traditional application security risks. Think prompt injection, where users craft clever inputs to bypass restrictions. Or jailbreaks, where attackers trick a model into saying or doing something it shouldn’t. Denial of service attacks may try to overwhelm a model’s inference capacity, while data leakage threats aim to extract sensitive or proprietary information from its training set. In some cases, attackers might try to manipulate model outputs to generate misinformation, toxic content, or regulatory violations.
To address these risks, AI protection tools inspect every interaction with the model. They detect anomalies, block malicious prompts, and flag unusual behavior. In more advanced setups, they can enforce custom safety policies, redact sensitive information from outputs, or automatically quarantine suspicious activity.
This layer of defense is especially important for customer-facing AI applications and autonomous agents that make decisions or take actions without human review. Once models are exposed to the open internet or integrated into production workflows, there’s no margin for error. Real-time protection ensures that even if something slips through discovery or testing, the system is still able to defend itself.
AI protection is not just a best practice. It is a necessity for any organization running AI in production environments. It is the safety net that catches what earlier stages might miss.
Final thoughts
Securing AI systems requires more than retrofitting traditional security tools to a new domain. AI introduces fundamentally different attack surfaces, behaviors, and risks that demand purpose-built solutions at every stage of the development and deployment lifecycle.
From managing employee use of third-party GenAI tools to safeguarding the models, data, and infrastructure behind your own AI applications and agents, security challenges are increasing.
Each of these layers plays a critical role. Discovery tells you what’s out there. Scanning helps you understand the risks. Testing uncovers how your models respond to adversaries. And protection ensures your systems are defended the moment something goes live. Skip any one of these, and you’re leaving your organization exposed.
As AI becomes more deeply embedded into business operations and decision-making, securing it must become your first priority. The organizations that move early to build a layered, AI-specific security posture will be the ones best positioned to innovate safely and stay ahead of emerging threats.
Now is the time to stop thinking of AI security as an add-on. It’s part of your core security stack. Treat it that way.
How TrojAI can help
TrojAI is a security for AI platform. Our mission is to enable the secure rollout of AI in the enterprise. Our comprehensive security platform for AI protects AI models, applications, and agents. Our best-in-class platform empowers enterprises to safeguard AI systems both at build time and run time. TrojAI Detect automatically red teams AI models, safeguarding model behavior and delivering remediation guidance at build time. TrojAI Defend is an AI application firewall that protects enterprises from real-time threats at run time.
By assessing the risk of AI model behavior during the model development lifecycle and protecting it at run time, we deliver comprehensive security for your AI models and applications.
Want to learn more about how TrojAI secures the largest enterprises globally with a highly scalable, performant, and extensible solution?
Check us out at troj.ai now.
