Secure Your AI Workloads - Protect Models, Data, and ROI

AI/ML Inference Security Use Case

The Challenge

AI is moving into production faster than ever: with inference services, LLMs, and Nvidia NIMs powering real-world applications. But with exposure comes risk.

Model endpoints are directly accessible, making them high-value targets for:

- Model theft or extraction
- Data exfiltration
- Prompt injection and poisoning
- Service disruption and downtime

Traditional defenses can’t keep up. Static environments are predictable. Once attackers map your inference infrastructure, they can return again and again.

The Phoenix Advantage

Phoenix brings Automated Moving Target Defense (AMTD) to AI/ML inference. Instead of serving models from static containers, Phoenix constantly rotates workloads, configurations, and endpoints.

That means:

- Attack surfaces shift before adversaries can stabilize an exploit
- Reconnaissance data is outdated in minutes
- Model endpoints are harder to fingerprint, extract, or poison
- Security scales automatically with your AI workloads

The Results

- 94% fewer successful attacks (proven in testing)
- Protection for AI investments worth $10M-$100M+ in training costs
- $2M-$5M in avoided breach costs annually
- Operational savings by reducing the need for additional SecOps staffing

The Bottom Line

Phoenix makes your AI deployments resilient by design.
Your models and data stay protected. Your customers stay confident.
And your AI investments deliver maximum ROI. Even without being derailed by security incidents.