The capacity to run adversarial learning for real-time AI security provides a clear edge over static defense systems.
The rise of AI-powered attacks – leveraging reinforcement learning (RL) and Large Language Model (LLM) features – has spawned “vibe hacking” and adaptive threats that evolve quicker than human teams can counter. This poses a governance and operational challenge for enterprise leaders that policies alone cannot address.
Attackers now use multi-step reasoning and automated code creation to evade traditional defenses. As a result, the sector is shifting toward “autonomic defense” (i.e., systems that learn, predict, and respond smartly without human input.)
Shifting to these advanced defense models has long faced a key operational limit: latency.
Using adversarial learning, where threat and defense models train ongoing against each other, provides a way to combat malicious AI threats. However, deploying required transformer-based setups in live production hits a snag.
Abe Starosta, Principal Applied Research Manager at Microsoft NEXT.ai, said: “Adversarial learning succeeds in production only when latency, throughput, and accuracy align.
Computational costs tied to running