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Establish Recurring AI Red Teaming Validation Cadence

Implementation Effort: Medium – Requires defining a recurring schedule, assigning ownership, integrating red teaming results into remediation workflows, and tracking posture changes over time.
User Impact: Low – Testing runs in controlled environments; end users are not affected.

Overview​

A single red teaming scan at deployment time provides a point-in-time assessment of an agent's security posture, but agent behavior changes over time. Model updates, changes to grounding data, new tool integrations, and evolving system prompts can all introduce new vulnerabilities that did not exist when the agent was first tested. Establishing a recurring red teaming validation cadence ensures that agents are retested on a regular schedule, catching regressions and new attack vectors before threat actors discover them.

The recurring cadence should define how frequently each agent is retested, with the interval tied to the agent's risk tier and change velocity. High-risk agents that receive frequent updates — new tools, updated grounding data, or model version changes — should be retested more frequently than stable, low-risk agents. A reasonable starting cadence is quarterly for high-risk agents and semi-annually for lower-risk agents, with additional ad-hoc scans triggered by significant changes such as a model upgrade or the addition of a new MCP tool connection.

Each recurring scan should include both the baseline attack scenarios used during initial deployment testing and any new attack scenarios that have been developed since the last scan. The AI red teaming landscape evolves as researchers discover new attack techniques and as language models change their behavior in response to updates. The organization's security team should maintain the attack scenario library and update it as new techniques emerge, ensuring that recurring scans test for the latest known attack vectors rather than rerunning the same static set indefinitely.

Scan results from recurring validations should be compared against previous results to identify posture trends. An agent that passed all scenarios at deployment but fails new scenarios after a model update has experienced a security regression that needs immediate attention. Tracking these trends across the agent fleet gives security leadership visibility into whether the organization's overall AI security posture is improving, stable, or degrading. Microsoft Foundry Control Plane operationalizes this cadence by providing the ability to schedule automated red teaming scans and drift monitoring directly from the Assets pane. Rather than relying on individual teams to remember and execute recurring scans, Control Plane manages the schedule at the fleet level, surfaces results across projects, and correlates findings with other observability signals such as evaluation results, runtime traces, and compliance posture. This turns recurring red teaming from a manual process into an automated fleet management capability.

This task supports Assume Breach by maintaining continuous adversarial pressure on the agent fleet, ensuring that security assumptions are verified over time rather than assumed to hold indefinitely. It supports Verify Explicitly by replacing one-time certification with ongoing evidence-based validation. Organizations that perform red teaming only at deployment time but never again are operating on stale security assessments that do not account for the changes that accumulate over the agent's operational lifetime.

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