Prompt Injection: The Cybersecurity Problem Inside Your AI
🛡️ CyberSecurity Month 2026 · WAIG × DefenAI
Prompt Injection: The Cybersecurity Problem Inside Your AI
OWASP LLM
Top 10
Injection & leakage
RAG risk
Poison
Untrusted retrieval
Control
Guardrails
Input/output policy
Your model trusts too much
Add input/output guard on one LLM app
4-part action model
Treat prompts and retrieved content as untrusted input.
Governance
AI usage policies and inventory for LLM Security.
Technology
Secure models, APIs, agents, and data paths.
Resilience
Degrade safely when AI services are abused or unavailable.
Incident response
Include AI abuse cases in severity matrix.
Today's flow
Understand
Why LLM Security matters for your organization today.
Assess
Map assets, owners, and gaps against today's theme.
Act
Red-team one customer-facing LLM flow.
Share
Publish learning internally and on social — #CyberSecurityMonth2026.
Did you know?
Frameworks & standards
Organizational checklist (infographic summary)
Audience awareness infographic
Tap a card for tailored awareness and today's action — built for sharing.
Treat prompts and retrieved content as untrusted input. As an employee, prompt injection: the cybersecurity problem inside your ai affects every inbox, device, and customer record you touch.
Share: Employees: Day 12 — LLM Security. #CyberSecurityMonth2026 #ResponsibleAI
One-click publish
LinkedIn, Medium, Instagram, Facebook, and X — captions tailored for this theme (employees).
Select an audience, then use one-click publish to tailor the share caption for that community.
Treat prompts and retrieved content as untrusted input.
WAIG: AI literacy content for builders.
DefenAI Labs: FFAI-style firewall patterns and testing.
Suggested format: Explainer