AI runtime security solutions

AI runtime security protects models while they are actively processing production data and user requests. Runtime guardrails, API protection, model behavior monitoring, and policy enforcement help reduce risks including prompt injection, data leakage, model evasion, and API abuse. These controls can be applied across cloud-hosted and self-hosted AI environments to support secure inference and align runtime operations with frameworks such as the NIST AI Risk Management Framework (AI RMF).

AI agent runtime security controls what agents can access, which tools and APIs they can use, and the actions they can take during execution. Effective controls include restricting privileges, protecting credentials and API keys, monitoring anomalous agent behavior, and applying guardrails to tool use. Where agents are permitted to generate or execute code, sandboxing can provide an additional layer of isolation for higher-risk workloads.

Prompt injection attacks use malicious instructions to manipulate the behavior of a large language model (LLM). Indirect prompt injection can enter through external content such as documents, websites, retrieved data, or tool responses and influence an agent as it processes that content. The OWASP Top 10 for LLM Applications 2026 identifies prompt injection as a critical risk for LLM applications. AI runtime security can inspect prompts, context, tool calls, and outputs in production and enforce policy before unsafe interactions reach connected systems or sensitive data.

Manual AI red teaming uses expert-led adversarial testing to explore novel, context-specific attack paths. Automated red teaming scales testing across larger libraries of adversarial techniques and can be repeated as models, applications, and agents change. Runtime AI security applies controls to live production interactions and provides ongoing visibility into behavior and emerging threats. Traditional runtime application self-protection (RASP), web application firewalls (WAFs), and API security remain important layers, while AI-specific runtime controls address risks such as prompt injection, harmful outputs, data leakage, and agent tool misuse.

Organizations need visibility into AI-bound traffic, AI API endpoints, models, agents, and sensitive data flows to identify unsanctioned AI usage. Runtime monitoring and policy-based controls can help security teams detect shadow AI, monitor anomalous model behavior, reduce data leakage, and steer users toward approved services. For regulated organizations, these controls can also support broader AI governance and compliance programs aligned with frameworks such as the NIST AI RMF and requirements including GDPR.