AI is changing the skills developers need to build secure applications, especially as many are now shipping features such as retrieval systems and autonomous agents. Traditional secure coding knowledge is still important, but it doesn’t cover everything developers come across when working with AI.
The need for AI security training is evident. Research has found that more than 20% of surveyed organizations reported a breach targeting AI models or applications, up from 13% the previous year.
This guide explores what AI security training should cover, how hands-on practice can prepare developers for these risks, and where training fits into AI governance.
Key takeaways
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AI security training gives developers practical skills for securing AI-driven features rather than only general awareness.
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Traditional secure coding training doesn’t address many of the risks found in AI applications, including prompt injection, insecure output handling, and RAG and agent security.
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Academic studies show AI-assisted developers often write less secure code while feeling more confident about it.
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The OWASP Top 10 for LLM Applications, alongside the OWASP Top 10 for Agentic Applications, gives program leaders a reference point for what to teach.
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Hands-on, lab-based training complements AI governance by catching issues before deployment.
What is AI security training?
AI security training helps developers and security teams understand how vulnerabilities can affect AI-powered applications and how to address them. It covers risks across the models, data, tools, and systems that make up an AI application.
These risks, amongst others, include prompt injection, insecure model output, poisoned retrieval data, and agents that can take actions on behalf of a user. Practical training gives teams the opportunity to recognize and fix these risks in realistic scenarios.
Why teams need AI security skills
AI introduces attack vectors that many developers haven’t come across before. There’s also growing pressure on organizations to demonstrate that they can manage AI-related risks. The EU AI Act, for example, includes penalties of up to 7% of global annual turnover for certain **forms of non-compliance.
The need for these skills is seen in the SANS 2025 AI Survey, which found that 65% of organizations said their teams need more specialized AI and cybersecurity training, and 64% said continuous learning is important.
Training gives developers a way to address AI risks earlier in the development process. Building AI security skills alongside existing secure coding knowledge helps teams catch issues during design and development.
Limitations of traditional secure coding training for AI
Older secure coding training was based on a simpler development process where developers manually wrote the code, but AI changes that. Developers now need to think about security across the AI application, including how models handle inputs and outputs, where retrieved data comes from, and what tools an AI agent can access.
AI-generated code risks
There’s a growing body of research showing that AI-generated code has both traditional and AI-specific vulnerabilities. For example, in an early study, GitHub Copilot found that around 40% of generated programs contained vulnerabilities across 89 scenarios.
Developer overconfidence
The problem isn’t limited to the code itself, as developers can also become overconfident in AI-generated output. A study found that participants using an AI assistant wrote less secure code and were more likely to believe that their code was secure, particularly when working with weak encryption and SQL injection.
More code to review
AI-assisted development also means more code can be produced and shipped in less time, which puts pressure on reviews and makes it harder to rely on security teams to catch every issue.
As AI changes what developers need to secure, training needs to change with it, which means giving developers opportunities to practice these skills in real development environments rather than only reading about them.
What AI security training should cover
AI security training should give developers practical skills they can use day-to-day, while also covering recognized standards and the AI components they’re building with.
Secure prompting and reviewing AI-generated code
Two practices make a big difference when developers use AI to write code. First, they should include security requirements in their prompts. Second, they should see the output as a starting point that still needs a security review.
That review needs to be practical, as developers should know how to find problems in AI-generated code, such as:
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Checking that untrusted input cannot manipulate an AI system’s instructions or behavior.
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Making sure model output is validated before it is passed to another component or used in a sensitive operation.
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Preventing AI features from exposing personal data, credentials, system prompts, or other sensitive information.
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Ensuring that AI agents have only the permissions and tool access they need.
Security prompts can guide the AI, but proper code review is still needed. Developers should state their security requirements up front and then check that the generated code meets them.
Use OWASP to guide AI security training
AI security training should be based on recognized standards. The OWASP Top 10 for LLM Applications is a good starting point, giving developers a view of the risks they’re likely to find when building with AI.
Developers should understand the main risks in practical terms, including prompt injection, sensitive information disclosure, supply chain vulnerabilities, and excessive agency. They should also understand how these risks can appear in the applications they build, rather than simply memorizing the OWASP categories.
For teams working with AI agents, the OWASP Top 10 for Agentic Applications focuses on risks that come with autonomous agents that can plan and take actions, including memory and context poisoning, tool misuse, and unsafe autonomy.
Other frameworks can add useful context. MITRE ATLAS and NIST’s adversarial machine learning taxonomy help developers understand how attackers can target AI systems and are useful references for threat modeling.
Using recognized standards also makes it easier to show what the training covers and connect it to broader security and compliance requirements.
Securing RAG pipelines, agents, MCP integrations, and skills
Retrieval-Augmented Generation (RAG) applications bring external data into the conversation, which creates another place for attackers to interfere. A retrieval system can be compromised through the content it indexes, leading to risks such as knowledge base poisoning, PII leakage, or malicious content being passed through retrieved documents.
The problem may be in the data rather than the application code. Developers need to know how to validate retrieved content and handle external data as untrusted input.
Agents and skills expand the attack surface further. Once an agent can use tools or access external services, it may be able to take actions beyond what was intended. MCP integrations can also expand the data and services an agent can reach, making carefully scoped permissions and validated tool inputs important before an agent reaches production.
When it comes to risks such as these, developers need to have practical training. Reading about knowledge base poisoning or excessive permissions builds awareness, but hands-on practice lets developers identify these risks in realistic applications and learn how to fix them.
AI threat modeling and secure design
Keep in mind that AI security should start before code is written or generated. Threat modeling gives teams a way to identify risks in AI features during design and decide which controls are needed before development begins.
Frameworks such as STRIDE can be applied to AI systems, while MITRE ATLAS provides a reference for adversarial techniques targeting machine learning.
The result is a more complete approach to AI security, where developers understand the risks they need to address and security teams can identify them earlier in the development process.
Hands-on practice vs. lectures and certificates
Knowing that a vulnerability exists is different from finding it in code. This is particularly true for AI security, where developers may come across unfamiliar attack paths and interactions between different parts of a system.
Hands-on labs let developers work through these scenarios in realistic development environments, using the same tools they use at work. Instead of answering questions about prompt injection, they can identify it in an application, understand why it works, and fix the underlying issue.
The same applies to AI-generated code. Developers can practice reviewing suggestions for security issues and learn to verify the output before it becomes part of the codebase.
Practical training also gives security leaders something more useful than completion rates. Results can show which skills developers have demonstrated and where additional training is needed.
How AI security training fits alongside AI governance
AI security training and governance serve different purposes, but they work better together.
Governance establishes policies, processes, and controls for how an organization uses AI. Training gives the people building AI systems the skills to put those controls into practice.
That makes training an earlier layer of defense. When developers understand AI-specific risks during development, there is less for security and governance processes to catch later.
NIST’s AI Risk Management Framework provides one useful way to structure this work through its govern, map, measure, and manage functions. Training and threat modeling can support the map and manage functions by helping teams identify risks and build the skills needed to address them.
In practice, the layers can work together like this:
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Threat-model AI features during design.
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Train developers on the risks relevant to what they are building.
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Review and govern AI systems before and after release.
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Use results to identify where further training is needed.
How SecureFlag helps teams build AI securely
SecureFlag turns AI security concepts into hands-on practice that mirrors real development work.
Its Agentic Coding Labs help developers practice secure prompting, reviewing AI-generated code, fixing vulnerabilities, and building secure agents, skills, and MCP integrations. Developers work with vulnerable code rather than simply reading about common mistakes.
SecureFlag also offers RAG Security Labs where teams can practice finding and fixing risks such as knowledge base poisoning, data leakage, and injection through indexed documents.
The labs run in virtualized environments across 70+ languages and technology stacks, including dedicated AI, LLM, and agentic AI categories. Training plans can adapt content based on individual results, while dashboards and reporting give leaders insight into skill development.
For teams addressing AI risks during design, ThreatCanvas adds automated threat modeling with risk templates for LLMs and agentic AI. Threats identified during design can connect directly to hands-on training, helping teams move from identifying a risk to practicing how to address it.
Get in touch to see how we can help.
FAQs about AI training security
Which AI security certification is best for developers?
ISC2’s emerging AI security certification and Practical DevSecOps’ CAISP are gaining recognition. However, hands-on lab experience often matters more than credentials alone for building practical skills.
How much does AI security training typically cost?
Costs range from free resources such as Microsoft Learn and AttackIQ Academy to enterprise platforms with comprehensive support. Pricing depends on depth, hands-on lab access, and customer success services.
How long does it take to complete AI security training?
Fundamentals can be covered in a few hours, while comprehensive coverage requires ongoing continuous learning. The timeline depends on role requirements and the depth of AI integration in your applications.
Can developers learn AI security without a cybersecurity background?
Yes, many programs are designed for developers new to security, with learning paths starting from foundational concepts and progressing toward more advanced topics.
