AI agents are no longer experimental. They are executing workflows, accessing sensitive data, and making decisions that impact your business. With that shift comes a new security imperative: red teaming your agentic systems to identify vulnerabilities before adversaries do.
The challenge? Most organizations do not have a dedicated AI security red team. The responsibility increasingly falls on AI/ML engineers, GRC teams, and platform engineering leads who have never conducted adversarial testing on autonomous systems. If that describes your situation, this guide will help you launch your first agentic red teaming campaign and turn findings into meaningful security improvements.
Who Is Being Asked to Red Team AI
Traditional red teaming was the domain of specialized offensive security professionals. Agentic red teaming is different. The urgency around AI security means that organizations cannot wait to build dedicated teams from scratch.
Instead, the mandate is landing on the desks of people who already understand the systems: AI/ML engineering leads who built the agents, security managers who own risk, and GRC professionals who must demonstrate compliance. These teams have deep organizational knowledge but often lack formal training in adversarial testing techniques.
This gap is not a failure. It is a reality of how quickly agentic AI has moved from pilot to production. The question is how to bridge it.
The Initiation Challenge
The number one blocker to running a red team campaign is not willingness. It is not knowing where to start.
Teams face a series of interrelated questions: How do I define what is in scope? Which attack patterns should I test? How do I interpret results without a baseline for comparison? And once I find vulnerabilities, how do I translate them into remediations my engineering team can implement?
Each of these steps requires expertise that most organizations do not have in house. Without a clear path forward, red teaming initiatives stall before they begin.
The solution is a structured framework that breaks the process into manageable phases and leverages tooling designed for teams without dedicated AI security expertise.
A Practical Framework for Your First Campaign
Follow these five steps to move from intent to execution.
Step 1: Define the Agent and Its Scope
Start by documenting exactly what the agent is designed to do. What tasks can it perform? What tools and systems does it have access to? What actions are explicitly outside its intended behavior?
This scoping exercise establishes the boundaries of your test. An agent with database query access presents different risks than one limited to summarizing documents. Be specific about permissions, integrations, and the data the agent can read or modify.
Step 2: Map the Attack Surface
With scope defined, identify every point where the agent could be exploited. This includes:
- The tools and APIs the agent can invoke
- External integrations and data sources
- The system prompt and any user-facing instructions
- Authentication and authorization mechanisms
- Data flows between the agent and connected systems
Mapping the attack surface helps you understand where vulnerabilities are most likely to exist and where exploitation would cause the greatest harm.
Step 3: Run Dataset-Based Tests Against OWASP Categories
Before attempting complex goal-based attacks, start with structured dataset testing. Use test cases aligned to established frameworks like the OWASP Top 10 for LLM Applications to systematically probe for common vulnerabilities.
This approach provides broad coverage without requiring you to design custom attack scenarios from scratch. Categories like prompt injection, insecure output handling, and excessive agency are well documented, with test cases available in commercial AI security platforms.
Dataset-based testing gives you a baseline understanding of where your agent is vulnerable before you invest time in more sophisticated adversarial campaigns.
Step 4: Document Findings with Severity and Remediation Recommendations
Every vulnerability you discover should be logged with consistent metadata: what the vulnerability is, how it was triggered, the potential impact, and a severity rating. More importantly, each finding should include specific remediation recommendations.
Generic findings like “agent is vulnerable to prompt injection” are not actionable. Effective documentation specifies which input patterns triggered the behavior, which guardrails failed, and what configuration changes would mitigate the risk.
If you are using a platform with pre-built attack libraries, remediation recommendations can often be generated automatically and mapped directly to guardrail configurations.
Step 5: Implement Remediations and Retest
Red teaming is only valuable if findings lead to fixes. Work with your engineering team to implement the recommended remediations, then rerun the relevant tests to confirm the vulnerabilities have been addressed.
This remediation loop is what transforms a red team campaign from an exercise into an improvement. It also creates the documentation you need to demonstrate security maturity to stakeholders.
What Commercial Tooling Can Do
The right tooling dramatically lowers the barrier to entry for agentic red teaming. Modern platforms provide pre-built attack libraries mapped to OWASP and MITRE frameworks, so you do not need to develop test cases from scratch. Campaigns can be launched with defined objectives, and findings come back with severity ratings and specific remediation recommendations.
For teams without specialist AI security expertise, this means you can run meaningful campaigns without hiring consultants or building custom infrastructure. The Airia platform is designed with this workflow in mind, connecting red teaming findings directly to guardrail and constraint configuration so remediations can be implemented without leaving the platform.
The Ongoing Commitment
Red teaming is not a one-time activity. As your agents evolve, so do their attack surfaces. New tools get connected, prompts get updated, and permissions change. A campaign that cleared your agent six months ago may no longer reflect its current risk profile.
Build red teaming into your development lifecycle. Retest after significant changes. Maintain a baseline of known vulnerabilities and confirm they remain addressed.
The Organizational Signal
Beyond the immediate security benefits, running a red team campaign sends a powerful signal about governance maturity. Documenting your process, showing the remediation loop, and presenting audit-ready evidence to a board or regulator demonstrates that you are taking AI risk seriously.
In an environment where regulators are still defining expectations, proactive red teaming positions your organization as a leader rather than a laggard. It shows you understand that AI governance requires continuous validation, not just policy documentation.
Get Started Today
Your first agentic red teaming campaign does not require a dedicated security team or months of preparation. With a clear framework and the right tooling, you can begin identifying and remediating vulnerabilities in your AI agents today.
Secure your AI agents before vulnerabilities become incidents. Connect with the Airia team to see how pre-built attack libraries and automated remediation recommendations can accelerate your first campaign.
Put these ideas to work.
Schedule a 30-minute walkthrough with our team.