Secure AI Testing, Evaluation, and Optimization
As GenAI adoption expands, leaders need confidence that systems are being tested, evaluated, and improved in ways that keep security, reliability, and governance expectations intact. This workshop builds a shared understanding of secure AI testing best practices—how to define meaningful evaluation criteria, pressure-test real-world scenarios, and use findings to strengthen controls and decision-making over time.
Leave with a clear, actionable approach to evaluating secure AI performance and prioritizing improvements.
Many organizations want “secure GenAI,” but lack a repeatable way to prove it—and improve it—as real usage evolves.
- Unclear evaluation standards: Teams don’t share a common definition of “acceptable” performance and security: making approvals inconsistent and hard to defend.
- Testing doesn’t reflect real risk: Edge cases and adversarial behavior aren’t consistently exercised: so failures appear late, after rollout.
- Findings don’t translate into action: Results aren’t tied to clear remediation decisions and priorities: leading to rework, slowdowns, and recurring issues.
Without disciplined testing and evaluation, confidence erodes—and scaling becomes riskier and harder to govern.
We equip leaders with best practices and a practical path to make secure AI evaluation repeatable, decision-ready, and continuously improving.
- Baseline evaluation criteria: Define what “good” looks like for secure AI outcomes so teams can make consistent, defensible decisions.
- Scenario-based test planning: Build a structured set of test cases that reflects real business use, edge scenarios, and known risk patterns.
- Vulnerability-focused challenge testing: Apply proven approaches (including structured challenge exercises) to surface weaknesses before they scale.
- Metrics that evolve with risk: Establish how evaluation measures are reviewed and updated as usage, policies, and threat conditions change.
- Optimization with guardrails: Prioritize improvements that strengthen performance while maintaining required security and oversight thresholds.
- Establish baseline evaluation criteria for secure AI systems
- Design test cases for adversarial behavior and edge scenarios
- Use red teaming and synthetic data to test for vulnerabilities
- Continuously refine evaluation metrics for evolving risks
- Optimize system performance while maintaining security thresholds
Establish a clear set of baseline evaluation criteria leaders can use to align approvals and expectations
Define a prioritized set of real-world test scenarios to pressure-test high-value GenAI initiatives
Adopt a practical approach for interpreting findings and translating them into remediation priorities
Apply a repeatable method for updating evaluation metrics as risks and usage evolve
Produce a leadership-ready set of next steps to improve secure AI performance while maintaining required controls
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Shared collaboration space (virtual whiteboard or equivalent) and shared notes