AI Runtime Protection

AI runtime protection is the practice of securing machine learning models and generative AI systems during their active use. Unlike traditional defenses that focus on model development or training, runtime protection operates continuously—monitoring live inputs, outputs, and behavior in production environments.

As AI adoption expands, runtime risks have become increasingly urgent. Deployed models are often exposed to:

• Adversarial manipulation, such as prompt injection or evasion attacks.
• Abuse or misuse, like generating toxic content or automating harmful decisions.
• Model drift, where accuracy and relevance degrade due to changing data.
• Resource overload, where APIs or compute budgets are exhausted maliciously or accidentally.
• Privacy violations, where sensitive information is exposed via responses.

Traditional security tools don’t address these threats because AI operates differently from static software. Behavior is dynamic, probabilistic, and often unpredictable—making runtime visibility essential for maintaining control and compliance.

AI runtime protection typically includes:

• Input and output inspection.
• Policy-based filtering or blocking.
• Real-time anomaly and drift detection.
• Identity-aware access controls.
• Threat intelligence integration.

By defending models in real time, organizations can respond to attacks or policy violations immediately, rather than waiting for post-incident investigations. Runtime protection is especially critical for public-facing LLMs, high-impact models in finance or healthcare, and any AI integrated with business-critical systems.

How PointGuard AI Addresses This:PointGuard AI is purpose-built to secure AI systems at runtime. It continuously inspects model behavior, monitors for anomalies, and enforces security and compliance policies in real time. Whether blocking unsafe outputs, detecting adversarial prompts, or alerting, PointGuard keeps deployed AI models protected, observable, and trustworthy in production.

Resources:

Gartner: TRiSM in AI Models