Runtime Protection Agent
OverviewAPI PostureAPI Advanced ProtectionAI PostureAI Firewall

AI Firewall

Runtime protection for agents and AI

You can't protect what you can't see. With Runtime Protection Agent, AI Firewall helps you know your attack surface, then defend, govern, and control it in real time.

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Why Teams Switch

AI changed the threat model

Defend

Legacy filters miss AI attacks

WAFs and API gateways don't understand prompts, so attacks slip through.

Stop AI attacks

Block prompt injection, evasion, and malicious payloads in real time.

Govern

No AI governance

Models and data flow through AI without oversight or policy enforcement.

Enforce AI policy

Govern models, protect PII, and enforce policies across every environment.

Control

Uncontrolled AI costs and abuse

Uncontrolled requests drain tokens, spike costs, and destabilize AI systems.

Prevent AI resource abuse

Rate-limit traffic, detect input explosion, and prevent resource exhaustion.

Defend

AI Firewall: stop AI attacks in real time

Protect AI applications in production from prompt injection, evasion, and malicious payloads.

Prompt injection protection. Detect attempts to override system instructions, bypass safeguards, extract sensitive information, or trigger unintended behavior.

Prompt evasion & misdirection. Identify attempts to evade safeguards through obfuscation, homoglyphs, invisible characters, and other misleading prompts.

Malicious code detection. Detect executable code and exploit patterns in prompts, including SQL injection, XSS, and shell commands.

Govern

AI Firewall: enforce AI policy everywhere

Keep AI usage and sensitive data within policy across every model, vendor, and environment.

Model governance. Detect unauthorized model usage, configuration changes, and version drift.

PII protection. Detect personally identifiable and sensitive information in prompts, including organization-specific data types.

Custom AI Firewall policies. Create organization-specific policies and scope them by environment, model, vendor, and other criteria.

Control

Prevent AI resource abuse

Stop resource exhaustion and automation abuse with granular, policy-driven controls.

AI rate limiting. Control excessive AI traffic and automation abuse with configurable request thresholds.

AI input explosion protection. Detect prompts engineered to exhaust tokens or spike latency and cost, independent of whether the content itself is malicious.

Granular policy management. Manage protections by environment, threat type, and rule so different AI applications can follow different policies.

WHO OWNS AI SECURITY?

New risks. Everybody owns it.

Know your AI attack surface

Automatically discover and continuously inventory every AI asset and agent in your environment.

Prioritize remediation with risk scoring based on authentication, exposure, and vulnerability data.

Test every AI asset against OWASP Top 10 LLM and MCP risks before it reaches production.

FAQs

Frequently asked questions

AI-native application security is the practice of securing applications built with AI components such as LLMs, MCP servers, and third-party GenAI services. It requires runtime visibility into AI APIs, data flows, and model behavior to detect threats specific to AI systems — unlike traditional approaches focused solely on code vulnerabilities.

The AI blind spot refers to undiscovered AI components deployed without security team awareness. 62% of security practitioners say they have no way to tell where LLMs are in use across their organization, creating exploitable gaps that attackers can target.

Discovery requires continuous monitoring of runtime traffic to identify every LLM, MCP server, and GenAI service. Runtime API traffic analysis can detect AI assets as they appear — including shadow AI that was never formally inventoried.

MCP security protects connections between MCP servers, clients, tools, and resources. MCP servers represent a significant and often overlooked attack surface that requires continuous monitoring and vulnerability assessment.

AI-SPM continuously assesses the security posture of AI assets including authentication, encryption, exposure, and data flows, providing a risk-based view of your AI attack surface.

Prompt injection inserts malicious instructions into LLM inputs. Protection requires real-time inspection of prompts and model responses at the API layer to detect and block malicious inputs before they reach the model.

All AI components communicate via APIs. Without deep visibility into runtime API traffic, security teams cannot discover all AI assets, monitor their behavior, or detect threats in real time.

AI security adds runtime protection layers that traditional tools lack. Static code analysis alone cannot detect threats like prompt injection, LLM jailbreaking, or sensitive data leakage through AI APIs.

Compliance requires automated discovery, risk scoring, and policy enforcement. Out-of-the-box compliance policies aligned with frameworks like the OWASP LLM Top 10 provide a starting point for governance.

LLM security protects models from prompt injection, jailbreaking, data leakage, and overconsumption. It requires visibility into every API connection to and from the model and continuous monitoring of model inputs and outputs.

Get started with Harness AI Firewall

Try Harness AI Security free. No credit card. Full access to AI asset discovery, agent security, OWASP LLM testing, and runtime protection.

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