As generative AI and AI agents become part of enterprise workflows, security testing is evolving beyond traditional penetration testing.
Modern AI systems can be vulnerable to prompt injection attacks, jailbreak attempts, data leakage, model manipulation, and unsafe outputs. These threats often originate from weaknesses that conventional application security assessments were never designed to identify.
AI Red Teaming is a security testing methodology that evaluates AI systems from an attacker's perspective.
Instead of focusing solely on infrastructure vulnerabilities, AI Red Teaming tests how models behave when exposed to adversarial inputs and malicious prompts. Security teams attempt to manipulate outputs, bypass safeguards, extract sensitive information, and identify weaknesses in model behavior.
Common AI Red Teaming objectives include:
• Testing resistance to prompt injection attacks
• Identifying data leakage risks
• Evaluating model alignment and safety controls
• Assessing AI agent behavior
• Validating access controls and governance mechanisms
• Measuring resilience against adversarial inputs
As AI technologies continue to mature, organizations need security testing approaches specifically designed for AI environments. Traditional security testing remains important, but it is no longer sufficient on its own.
AI Red Teaming helps organizations understand how attackers might target AI systems and provides actionable insights for strengthening defenses before deployment.
If your organization is adopting AI, AI Red Teaming should be part of your overall AI security strategy.
Read the complete guide:
AI Red Teaming: How Organizations Can Test AI Systems for Security Risks
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