NEW! Data443 Acquires Vaikora – Real-Time AI Runtime Control & Enforcement for AI Agent
AI red teaming is the practice of probing AI systems with adversarial inputs to find security and safety gaps before attackers do. Red-team engagements may target the model itself, the agent’s tool surface, the RAG corpus, or the orchestration layer that connects them. The output of a red-team engagement is a set of findings: which inputs caused which unsafe behaviors, with what reproduction conditions.
The findings from a red-team engagement only matter if they translate into deployed defenses. In 2024 and early 2025, most red-team reports landed as PDFs that informed the next training run but did not produce runtime changes. By 2026, the more mature programs have begun feeding red-team findings directly into runtime policy: a finding that the agent leaks PHI under prompt class X becomes a runtime rule that blocks actions matching pattern X.
This is the loop that makes red teaming compounding rather than episodic. Each engagement adds to the policy library; the policy library protects future workloads; the next engagement focuses on novel risk rather than re-finding known issues.
Red teaming overlaps with traditional penetration testing but the techniques differ. AI red teaming uses prompt injection, jailbreak, RAG poisoning, and adversarial example crafting in addition to the network and application techniques of traditional pentest. Findings feed into AI runtime control policy.
A financial services firm runs a quarterly red-team engagement against its customer-service AI. The team finds that a specific indirect-injection vector hidden in PDF attachments causes the agent to leak account numbers. The finding becomes a policy rule that blocks any agent response containing account numbers when the conversation context includes a PDF attachment. A second example: an internal red-team finds that a developer-tools agent can be coerced into running arbitrary shell commands via a poisoned README. The finding produces a policy that constrains the agent’s shell access to a known allow-list.
Internal security teams, specialist vendors, or in-house ML safety teams. The OWASP and NIST frameworks both provide playbooks. Frontier labs run their own red-team programs internally before model release.
Continuously for high-stakes systems, quarterly for medium-stakes, on every major model or prompt change. The cadence is similar to traditional pentest cadence and depends on the rate of change in the system.
A workflow that converts findings into runtime policy. Vaikora is designed to be the destination for these findings. The output of the engagement maps to policy rules; the rules deploy via the same control plane that handles compliance presets.
There is overlap. Safety testing focuses on harm prevention and alignment; red teaming focuses on adversarial discovery. The two share techniques and often share staff inside frontier labs.
Last updated: 2026-05-20.
