AI agents now read email, call APIs, use MCP servers, and take real actions — not just generate text. This guide covers what runtime security actually needs to see, how it differs from AI guardrails, and how the major platforms compare.
An AI runtime security platform protects AI systems while they're actually operating — inspecting and controlling activity during execution, not just at pre-deployment test time. Depending on the platform, that can include prompts, model responses, retrieved content, tool calls, tool responses, agent behavior, sensitive data, identity, permissions, MCP interactions, multi-turn sessions, and autonomous actions.
The core distinction: runtime security protects the AI while it's making decisions and taking actions — not just when it's generating an answer. Some attacks don't look malicious at the infrastructure level at all. The request is authenticated, the API is legitimate, the network path is clean. The question runtime security asks is different: why did the agent decide to make that request in the first place?
NIST's 2026 work on AI-agent security recognizes that autonomous agents combine model outputs with software functionality and can take actions affecting real-world systems — and flags post-deployment monitoring as an unresolved challenge, since AI behavior can vary under real-world inputs in ways pre-deployment testing won't fully capture. OWASP created a dedicated Top 10 for Agentic Applications for 2026 for the same reason.
The security surface now includes prompt injection, indirect prompt injection, data leakage, tool abuse, memory manipulation, excessive permissions, credential misuse, agent hijacking, agent-to-agent propagation, and unauthorized actions. An AI agent isn't merely generating an answer — it may be generating an action, and actions have consequences a text-filtering guardrail was never built to evaluate.
Before comparing vendors, know what to actually test for:
There's no universal "best" — the right platform depends on your architecture, risk profile, existing security stack, and AI maturity. Here's how the major options position themselves.
Expanded into a broad AI security control plane covering applications, models, data, agents, red teaming, posture management, runtime security, and AI Gateway capabilities. Best fit for large enterprises already invested in the Palo Alto ecosystem who want AI security folded into an existing platform rather than a new point product.
Positioned around real-time protection of AI applications and agents — continuous runtime visibility, threat detection, investigation, inline protection, and agentic workflow monitoring. Best fit for teams wanting a dedicated AI runtime layer rather than a broader cybersecurity platform.
Builds on Lakera's guardrail heritage, extended into agent discovery, risk assessment, runtime protection, off-policy behavior detection, and tool allow/deny controls. Best fit for teams that want guardrails and emerging agent security in one place.
Extends Falcon into AI runtime security, including AWS-built AI applications. Best fit for organizations that want AI security folded into an existing XDR/SOC workflow rather than run as a separate system.
Focused on AI usage governance, runtime protection, prompt security, data protection, and MCP-related controls — particularly relevant for a heterogeneous AI environment with many applications and interaction paths rather than a single internally built agent.
Continuous AI discovery, risk assessment, red teaming, and runtime protection in one lifecycle — relevant for teams that want testing and runtime protection connected rather than run as separate functions.
Centers on understanding how an autonomous AI system can actually be attacked, manipulated, and driven toward unauthorized actions — attack, trace, measure, validate, block, retest. Covers agentic red teaming (testing autonomous agents, not just model responses), agent cascade testing (whether influence propagates across multiple agents), tool abuse testing, and a prompt-injection playground for hands-on exploration. Ran a standardized 5-category suite against 8 live LLM providers with every finding hand-verified — see the real results below.
Editorial note: this is a comparison framework, not an independent lab benchmark. Capabilities, packaging, and product names change fast in 2026 — validate current functionality in a proof of concept before procurement.
Most vendor pages assert detection capability without showing the work. HexTyx ran a standardized, 5-category adversarial suite — 69 payloads per model — against 8 live providers: OpenAI, Google Gemini, xAI Grok, Mistral, Alibaba Qwen, NVIDIA Nemotron, DeepSeek, and Anthropic Claude Sonnet 5. Every flagged result was manually checked against the model's full, untruncated response before being counted — never accepted from automated scoring alone.
That manual-verification discipline surfaced something arguably more useful than the scores themselves: 25+ real bugs in the testing infrastructure, caught specifically because results were checked rather than trusted — the large majority false positives (a refusal miscounted as compliance), several false negatives, and a handful of genuine crash or data-corruption bugs. Three of the eight rounds (OpenAI, Gemini, Grok) predate a ground-truth-signal fix later found to have silently hidden roughly 40% of one module's real findings in an earlier round — so those three are being re-run against the corrected infrastructure, and we're not presenting their current scores as final.
What is solid — cross-model patterns confirmed after every fix:
Lower isn't simply "safer." Two providers landed on numerically identical reward-hacking counts in this round, but their actual content differed enormously in severity — which is exactly why every finding here was verified against real response text instead of accepted as a number.
Run a free assessment across prompt injection, agent abuse, tool-call security, and MCP exposure — mapped to MITRE ATLAS and OWASP LLM Top 10.
Run Free Assessment →These terms get used interchangeably. They shouldn't be. A guardrail typically asks: is this output dangerous? Runtime security asks a broader question: is this action appropriate given everything that happened during this execution?
Guardrails control prompts, responses, content, topics, and policy violations. Runtime security additionally considers agent behavior, tool calls, identity, permissions, context, data flows, autonomous actions, multi-step sessions, MCP, and agent-to-agent interactions. If a platform only sees prompt → response, that's a guardrail. If it sees the full chain — prompt → context → agent → tool → data → action — that's runtime security.
The single most useful question to ask any vendor directly: "Show me exactly what happens when an agent receives a malicious document, retrieves sensitive information, calls a privileged tool, and attempts an external action. Can you see the entire chain?"