Home OffSec
  • Pricing
Get started Sign in Search Contact us
Red Teaming in the Age of AI | OffSec
Resources Whitepapers

Red Teaming in the Age of AI

Red Teaming in the Age of AI

AI creates a new type of attack surface shaped by prompts, data, integrations, and user interactions. OffSec’s OSAI course gives hands-on experience to help teams move from theory to real-world AI testing.

Build a clear, actionable approach to testing AI systems in production

Download Red Teaming in the Age of AI to get:

  • A clear breakdown of how AI expands the enterprise attack surface
  • Insight into AI system vulnerabilities via prompts, data & integrations
  • A practical way to prioritize which AI systems to test first
  • A definition of what AI red team capability looks like in practice
  • Build AI red teaming skills through OffSec’s hands-on OSAI course
Download Now

Executive Summary

AI introduces a fundamentally different attack surface. Its behavior is influenced by prompts, data, integrations, and user interaction, which makes it harder to test using traditional approaches.

As teams work to build this capability, training becomes a critical enabler. OffSec's OSAI course is designed to support this shift by providing hands-on, adversarial experience with AI systems, allowing practitioners to move from theory to applied testing.

About This Whitepaper

AI is already in production across your organization. It's shaping decisions, automating workflows, and interacting with users at scale. But most security teams still aren't equipped to test how these systems actually fail.

This whitepaper explores how AI red teaming is evolving, and how OffSec's OSAI training helps teams build the skills needed to validate AI systems, uncover real-world failure modes, and develop a repeatable approach to securing this new attack surface.

Download the whitepaper now

See where AI systems actually break

See how systems fail in real conditions, from prompt attacks to risky outputs that standard tests miss.

Test systems with a structured approach

Move from ad hoc experimentation to a consistent methodology that improves coverage and reduces blind spots.

Build capability to scale with adoption

Develop a repeatable program that keeps pace with evolving models, new deployments, and increasing reliance on AI systems.