From Red Teaming to Real Protection: Building Enterprise AI Security for the Agentic Era

"The Enterprise Challenge: When AI Breaks, Who Gets the 2AM Call? Consider a financial services company deploying an AI agent to handle customer inquiries. The agent needs to: Now imagine these scenarios: Prompt injections bypass traditional input validation Jailbreaks exploit model behavior, not code vulnerabilities Multi-step agent attacks emerge from seemingly innocent individual actions Data poisoning happens during training, not runtime Model extraction threatens your competitive IP"

"As AI systems evolve from simple chatbots to autonomous agents, the security challenges have fundamentally changed. Traditional computing security approaches weren't built for this new reality, and the gap is widening fast. The difference between a research demo and a production AI system often comes down to systematic safety testing and real-time protection. At Virtue AI, we've learned this through working with enterprises like Uber and Glean: effective AI security requires running scenarios hundreds of times, testing across 320+ attack vectors, and implementing guardrails that work in sub-40 milliseconds without breaking user experience."

Autonomous AI agents now access sensitive account information, execute multi-step transactions, remember long conversation context, and use external tools. These expanded capabilities create novel attack surfaces including prompt injection, jailbreaks, multi-step agent attacks, data poisoning during training, and model extraction risking intellectual property. Traditional security controls like web application firewalls and DLP operate at the wrong layer for agent behavior and model-level threats. Effective protection requires systematic safety testing at scale, scanning hundreds of scenarios across hundreds of attack vectors, and real-time guardrails that block attacks in sub-40 milliseconds without degrading user experience.

Read at Medium