Secure AI Misinformation Best Practices
As GenAI expands into employee and customer workflows, misinformation and “hallucinated” outputs can quickly become business risk—driving bad decisions, reputational harm, and avoidable escalation. This workshop helps leaders understand where misinformation shows up, what best practices reduce exposure, and how to establish practical next steps for stronger validation, oversight, and response.
Leave with a clear understanding of misinformation best practices—and prioritized actions to strengthen content quality, validation, and correction protocols.
Misinformation risk often emerges in normal-looking interactions—and scales faster than most oversight approaches.
- Trust breaks quickly: Confident-sounding inaccuracies can mislead employees or customers and undermine credibility.
- Quality varies by use case: Standards for content accuracy and sourceability are inconsistent across teams and domains.
- Correction is ad hoc: Without clear response protocols, errors linger, repeat, and escalate into larger incidents.
When misinformation isn’t actively managed, GenAI becomes harder to trust.
We align leaders on practical best practices and an actionable path to reduce misinformation risk while keeping GenAI useful and scalable.
- Misinformation pattern awareness: Identify common ways inaccurate outputs show up and where they create the most business risk.
- Domain-specific quality expectations: Define what “acceptable accuracy” means by context, audience, and decision criticality.
- Validation layers and source discipline: Establish expectations for fact checks, source support, and citation-ready outputs when needed.
- Structured knowledge reinforcement: Use approved knowledge and standards to reduce variance and improve consistency of responses.
- Correction and escalation protocols: Create a clear approach to detect, correct, document, and prevent repeat misinformation incidents.
- Recognize patterns of hallucination and misinformation in GenAI models
- Assess content quality risks in specific domain applications
- Design validation layers for facts, sources, and citations
- Apply structured knowledge to reduce hallucination probabilities
- Establish response protocols to correct AI-generated misinformation
Develop a shared understanding of how misinformation risk shows up and where it matters most to the business
Establish clear, practical best practices for setting content quality expectations across priority use cases
Define a prioritized set of next steps to strengthen validation, source discipline, and review workflows
Adopt a lightweight approach for using approved knowledge to improve consistency and reduce inaccurate outputs
Create a response-ready outline for correcting misinformation incidents and preventing recurrence
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Shared collaboration space (virtual whiteboard or equivalent) and shared notes