Secure AI Prompt Injection Best Practices
As GenAI becomes part of everyday workflows, leaders need confidence that user inputs can’t easily steer systems into unsafe, non-compliant, or unintended behavior. This workshop explains prompt injection risk in plain business terms, highlights proven defensive practices, and helps you define practical guardrails and next steps so teams can use GenAI more safely at scale.
Leave with a clear understanding of prompt injection best practices, and prioritized actions to strengthen protections across GenAI initiatives.
Prompt injection is one of the most common ways GenAI experiences can be pushed outside intended boundaries—often through normal-looking interactions.
- Inputs can be weaponized: Seemingly harmless instructions can override intended behavior and produce unintended outcomes.
- Controls aren’t consistent: Teams implement protections unevenly across use cases, vendors, and channels—creating gaps.
- Detection is reactive: Without clear monitoring expectations, issues are discovered after incidents, escalations, or reputational risk.
If prompt injection isn’t addressed early, GenAI experiences can become unpredictable—driving risk, rework, and slowed rollout.
We equip leaders with practical best practices and actionable next steps to reduce prompt injection risk while keeping GenAI usable and scalable.
- Risk pattern awareness: Build a shared understanding of how prompt injection typically shows up in real business scenarios.
- Defensive interaction standards: Establish consistent guidance for how prompts and user interactions should be structured to reduce exposure.
- Behavior isolation expectations: Clarify how to separate intended system behavior from user influence to improve reliability and safety.
- Monitoring and escalation approach: Define what to watch for, how to respond, and when to escalate issues with confidence.
- Control roadmap: Prioritize the most impactful near-term protections and align on what to implement next across initiatives.
- Identifying Prompt Injection Patterns
- Creating Defensive Prompt Templates
- Using System Prompts to Isolate Model Behavior
- Monitoring Prompt Responses in Real Time
- Blocking Malicious Input Using Pattern Matching
Develop a shared understanding of prompt injection risk and how it presents in business contexts
Define a set of practical next steps to strengthen protections across priority GenAI use cases
Establish clear expectations for defensive prompting and interaction standards teams can apply consistently
Create a lightweight monitoring and escalation outline to improve detection and response
Prioritize a list of control improvements to reduce exposure while maintaining usability
Who Should Attend:
Solution Essentials
Facilitated workshop (in-person or virtual)
4 hours
Intermediate
Shared collaboration space (virtual whiteboard or equivalent) and shared notes