As artificial intelligence becomes part of modern applications, security teams are facing a new class of risks. Traditional application security testing remains essential for finding vulnerabilities in software, APIs, infrastructure, and application logic. However, AI-powered applications introduce additional challenges that conventional security testing may not fully address.
This is where AI red teaming comes into play.
AI red teaming focuses specifically on testing the security, reliability, and behavior of AI systems under adversarial conditions. It can involve deliberately crafting malicious prompts, attempting to bypass safeguards, testing model behavior, and examining how an AI system responds to unexpected or harmful inputs.
Understanding the difference between AI red teaming versus traditional application security testing can help organizations build a more comprehensive security strategy.
What Is Traditional Application Security Testing?
Traditional application security testing is designed to identify weaknesses in conventional software systems before attackers can exploit them.
Common approaches include:
- Static Application Security Testing (SAST): Examines source code or compiled code for potential vulnerabilities.
- Dynamic Application Security Testing (DAST): Tests running applications from an external perspective.
- Software Composition Analysis (SCA): Identifies vulnerabilities and licensing issues in third-party dependencies.
- Penetration testing: Simulates attacks against applications, APIs, networks, and other systems.
- API security testing: Looks for authentication, authorization, input validation, and business-logic weaknesses.
Typical vulnerabilities include SQL injection, cross-site scripting (XSS), broken access controls, insecure authentication, and vulnerable dependencies.
These techniques are still highly relevant—even when an application includes AI.
What Is AI Red Teaming?
AI red teaming is an adversarial testing approach designed for AI-enabled systems.
Instead of focusing only on traditional software vulnerabilities, security professionals investigate how an AI system behaves when deliberately exposed to difficult, misleading, or malicious scenarios.
For example, an AI red team may test whether a model can be manipulated through:
- Prompt injection
- Jailbreak attempts
- Sensitive-information extraction
- Insecure tool usage
- Manipulation of model instructions
- Unsafe or unintended outputs
- Data leakage
- Excessive permissions
- Model-specific abuse cases
AI red teaming can evaluate both the AI model and the surrounding application architecture.
This distinction is important because an AI system can be technically secure from traditional application vulnerabilities while still behaving in an unsafe or unexpected way.
AI Red Teaming vs. Traditional Application Security Testing
The biggest difference is the object being tested.
Traditional application security testing primarily evaluates software components, application logic, infrastructure, and known security weaknesses. AI red teaming additionally evaluates the behavior of AI systems when they encounter adversarial inputs and unusual interactions.
| Area | Traditional Application Security Testing | AI Red Teaming |
|---|---|---|
| Primary focus | Software and application security | AI behavior and AI-enabled systems |
| Typical inputs | Requests, parameters, files, API calls | Prompts, conversations, files, tool calls |
| Common vulnerabilities | Injection, authentication flaws, XSS, access control | Prompt injection, jailbreaks, data leakage, unsafe behavior |
| Testing approach | Often structured and repeatable | Often exploratory and adversarial |
| Main target | Code, APIs, infrastructure, business logic | Models, agents, AI workflows, and integrations |
| Human behavior | Simulated attacker behavior | Adversarial interaction with AI |
| Output evaluation | Usually based on security rules | May require contextual and behavioral evaluation |
Neither approach completely replaces the other. For many AI-powered applications, organizations need both.
Why Traditional Security Testing Is Not Enough for AI Applications
AI applications have characteristics that make them different from conventional software.
A traditional application generally follows predefined instructions and logic. A large language model, by contrast, generates responses based on its training, instructions, context, and inputs.
This introduces a different security dimension.
For example, a conventional security test might determine whether an API properly restricts access to customer records. An AI red team exercise could go one step further and examine whether a user can manipulate an AI assistant into revealing information that the user should not have access to.
The underlying API may be secure, but the AI application’s implementation could still create an unintended path to sensitive information.
Prompt Injection and AI-Specific Threats
One of the most important areas of AI security testing is prompt injection.
A prompt injection occurs when an attacker attempts to influence an AI system’s instructions or behavior through specially crafted input.
Consider an AI assistant connected to internal business documents. A conventional security test might verify that authentication and authorization controls work correctly.
An AI red team could additionally test whether a malicious user can manipulate the assistant into:
- Ignoring its intended instructions.
- Accessing information outside the user’s intended scope.
- Revealing sensitive system information.
- Performing unauthorized actions through connected tools.
This illustrates why AI security requires testing the interaction between the model, application, data, and tools, rather than examining the model in isolation.
AI Red Teaming for AI Agents
AI agents introduce another layer of complexity.
Unlike a basic chatbot, an AI agent may be able to interact with external systems, retrieve information, execute workflows, or call tools.
For example, an enterprise AI agent might have access to:
- Customer relationship management systems
- Internal databases
- Email platforms
- Cloud services
- Business applications
- File repositories
AI red teaming can examine whether an attacker can manipulate the agent into performing actions beyond its intended permissions.
This makes authorization, least-privilege access, tool validation, and human approval controls particularly important.
When Should Organizations Use Traditional Application Security Testing?
Traditional application security testing should remain part of the security lifecycle for virtually every modern application.
Organizations should consider it when developing or deploying:
- Web applications
- Mobile applications
- APIs
- SaaS platforms
- Cloud applications
- Enterprise software
- Databases and backend services
AI functionality does not eliminate conventional vulnerabilities. An AI-powered application can still contain SQL injection, broken access control, insecure APIs, vulnerable dependencies, and other traditional weaknesses.
When Should Organizations Use AI Red Teaming?
AI red teaming becomes especially useful when an application relies heavily on AI or allows AI systems to interact with sensitive data or external tools.
It can be particularly valuable for:
- Customer-facing AI assistants
- Enterprise chatbots
- Generative AI applications
- AI-powered search systems
- AI coding assistants
- Autonomous or semi-autonomous agents
- AI systems handling confidential information
- AI applications connected to business-critical tools
The greater the AI system’s access and autonomy, the more important behavioral and adversarial testing becomes.
How AI Red Teaming and Application Security Testing Work Together
The most effective approach is not to choose one methodology over the other.
Instead, organizations can combine them into a broader security testing program.
A typical process might look like this:
1. Perform Traditional Application Security Testing
Test the application, APIs, infrastructure, authentication mechanisms, authorization controls, and dependencies.
2. Identify AI-Specific Attack Surfaces
Map out models, prompts, system instructions, retrieval systems, data sources, plugins, APIs, and connected tools.
3. Conduct AI Red Team Exercises
Challenge the AI system with adversarial prompts, manipulation attempts, information-extraction scenarios, and unsafe tool-use situations.
4. Test the Complete AI Application
Evaluate how the model interacts with application controls, databases, external services, users, and business workflows.
5. Remediate and Retest
Fix identified weaknesses and perform another round of testing to determine whether the controls actually work.
The Future of Application Security Is Becoming More AI-Aware
As organizations integrate generative AI and AI agents into their applications, security testing is becoming more multidisciplinary.
Traditional application security remains the foundation. However, security teams increasingly need to understand model behavior, prompt security, AI-specific attack techniques, data governance, and agent permissions.
The key lesson is simple: AI red teaming and traditional application security testing solve overlapping but different problems.
Traditional testing helps organizations identify vulnerabilities in the application and its underlying technology. AI red teaming focuses more heavily on how AI systems can be manipulated, misused, or pushed outside their intended behavior.
For organizations deploying AI in production, combining both approaches provides a broader view of the application’s security posture.
Final Thoughts
The debate around AI Red Teaming versus Traditional Application Security Testing should not be framed as a choice between two competing methods.
Traditional application security testing remains essential for protecting software, APIs, infrastructure, and business logic. AI red teaming adds another layer by examining the unique risks created by intelligent, probabilistic, and increasingly autonomous systems.
As AI becomes more deeply integrated into business applications, organizations should consider security testing across the entire stack—from conventional code and infrastructure to models, prompts, data, tools, and AI-driven workflows.
A strong security program is therefore not simply about testing whether an application can be attacked. It is also about understanding how an AI system behaves when someone deliberately tries to make it behave in ways it was never intended to.