Why AI Red Teaming Belongs at the Core of Your AI Security Program

Enterprises are putting AI applications and agents to work on sensitive data and business workflows, while attackers look for new ways to manipulate the models and exploit the tools connected to them. Security leaders need to know whether those systems will hold up against a real attack, and AI red teaming gives them that answer by testing AI the way an attacker would.

What AI red teaming is

AI red teaming is the process of proactively testing models, agents, applications, and other AI systems to find the weaknesses an attacker could exploit. It simulates techniques such as prompt injection, jailbreaks, data leakage, and model manipulation to see how a system responds under pressure.

Because AI systems and the attacks against them change all the time, red teaming works best as a continuous loop: test real-world behavior, find what fails, use that evidence to strengthen the protections that guard AI in production, and test again.

Why AI systems need continuous testing

Attack methods keep evolving. Threat actors constantly develop new ways to manipulate models and abuse connected tools and APIs. Because these techniques evolve as fast as AI itself, a system that passed last quarter’s tests may not hold up against this month’s attacks. Security teams need to test continuously for new attack techniques, not just the ones they already know.

Model choices change the risk profile. As token costs shape decisions, teams may switch models to lower-costs, or alternatively, switch to more capable, or different models to better fit their AI application. Each switch can change how a system responds to attacks and which safeguards it needs.

What red teaming achieves

Done well, AI red teaming delivers two outcomes: reduced business risk and proof that your AI controls work.

Red teaming shows which weaknesses matter

Red teaming exposes weaknesses before attackers find them and shows which of those weaknesses matter most.

Not every unexpected model response is a real risk, and testing for weaknesses no attacker could realistically exploit wastes effort. Good red teaming focuses on the attack paths that matter for your applications and data and ranks findings by realistic impact, so teams can choose models and design guardrails that align with real risk.

Red teaming produces the evidence compliance teams need

Governance, risk, and compliance teams increasingly need evidence that safeguards withstand realistic attacks, that AI systems don’t expose sensitive or regulated information, and that each application aligns with internal policy.

Gartner’s 2026–2027 ThreatScape names AI application compromise and prompt injection among four critical threats where attackers hold a significant advantage. To counter them, Gartner recommends testing AI applications early and using the results to improve runtime controls.

Frameworks including the OWASP Top 10 for LLM Applications, the OWASP Top 10 for Agentic Applications (2026), the NIST AI Risk Management Framework, MITRE ATLAS, and the EU AI Act also call for adversarial testing and ongoing evaluation. Red teaming produces the record these frameworks expect: what was tested, what failed, how risks were prioritized, and how the organization responded.

Red teaming shows where your AI can be exploited

Treating red teaming as a prelaunch checkbox leaves a gap between what was tested and what runs in production, because models and connected tools keep changing after launch.

A mature AI security program should include three layers of defense: discovery, posture control, and runtime guardrails. Discovery answers “What AI do we have?” Red teaming sits within posture control and answers the next question: “Where can it be exploited?”

Those results help teams choose models that fit their risk profile and design guardrails around proven attack paths instead of generic assumptions.

What to look for in AI red teaming

A strong AI red teaming approach should include:

• Continuous, proactive testing that keeps pace as models and attack techniques change or new models are adopted.
• Actionable risk visibility that prioritizes tests by exploitability and impact, so teams focus on the risks that matter when choosing models and designing guardrails.
• Audit-ready reporting that maps results to the risk and compliance frameworks governance teams already use.
• Integrated remediation that connects findings to the policies and protections that address them, without stitching together disconnected tools.

Research quality matters too. Generic prompt libraries catch generic problems, while testing built on new attack research catches the techniques advanced attackers use.

Cato connects red teaming to runtime protection

At Cato, we believe red teaming delivers the most value when it combines deep attack research with the platform that protects AI in production. That research comes from Cato AI Labs, the team behind EchoLeak, the first reported zero-click AI vulnerability in Microsoft 365 Copilot, and CurXecute, a remote code execution flaw in the Cursor IDE. The team turns discoveries like these into realistic attack scenarios and adds new techniques as threats evolve, so testing reflects how attackers actually operate.

Cato AI Red Teaming puts that research to work. It continuously tests AI models with context-aware adversarial scenarios, prioritizes findings by realistic risk, maps them to leading frameworks, and turns them into policy and model recommendations inside the same AI Security solution that protects AI interactions at runtime.

With testing and protection on one platform, security teams can find AI risk and close the gaps before attackers exploit them.