Redline AI or Prisma AIRS: which should a security team choose for tool-using agents?

Redline AI and Prisma AIRS, side by side

Redline AI and Prisma AIRS address overlapping but different security boundaries. Redline focuses on the agents a development team builds and connects. Prisma AIRS presents a wider enterprise scope covering AI applications, agents, models and data, including discovery, posture management, red teaming, runtime security and model security.

The comparison below uses Redline product information and Palo Alto Networks’ own product pages and documentation for Prisma AIRS. Where a Prisma AIRS cell does not describe how a control works, the relevant Palo Alto pages do not say how it works.

The most important distinction is the unit of enforcement. Redline places controls within the agent process, where the application forms inputs and tool calls. Prisma AIRS can apply controls around AI traffic and across a broader enterprise AI estate. The appropriate choice depends on whether the primary security boundary is an individual agent implementation or the wider AI environment.

How does pre-release red teaming differ?

Prisma AIRS AI Red Teaming starts with a target endpoint. Its profiler agent gathers information about the endpoint’s provisioned tools, banned words and system configuration. An attacker agent then stress-tests the target using 50+ techniques mapped to OWASP Top 10 and NIST.

The Prisma AIRS documentation also describes multi-turn attack support and tool-chaining attacks in agent scans. These capabilities address attack paths that involve more than a single request or isolated model response. The target remains the endpoint being assessed, whether that endpoint represents a model, application or agent.

Redline starts from the agent’s own process rather than an endpoint registration. Its SDK connects outbound, so there is no endpoint to register and nothing to expose. Each run receives its own container with the agent’s real tools, MCP servers and skills attached.

That distinction affects test fidelity. Redline tests the assembled agent with the attachments it uses to perform work. A task passes only when the agent completes the intended job and refuses the attack. A second agent decides whether the criterion passed by inspecting the machine and the run record for evidence.

The attack corpus contains 11,204 cases across 16 attack families, mapped to OWASP Agentic Security Initiative classes. This gives a security team a defined way to assess the behaviour of the connected agent rather than only the model or endpoint in isolation.

For a practical explanation of this workflow, see how to test an AI agent before you ship it.

Where are tool-call controls and runtime gates enforced?

Prisma AIRS documents two ways to protect live AI traffic. A managed next-generation firewall blocks prompt injections and data leaks. Alternatively, an application can use API intercept: it sends prompts and model responses to the Scan API and acts on the resulting threat assessment.

Prisma AIRS Agent Security adds real-time enforcement intended to stop unauthorised agent actions. Its documented coverage includes privilege misuse detection for AI agents, with policies managed in Strata Cloud Manager. These controls operate as part of the broader Prisma AIRS security model.

Redline’s gate is located in the agent process, where the tool call is formed. A rule at pre_tool sees the tool name and every argument before the call occurs. This allows a policy such as “only the signed-in customer’s order” to be checked against the session before the agent invokes the tool.

Redline can return different policy outcomes:

• deny blocks the call.
• require_approval holds an irreversible call for a named person.
• warn allows the configured warning behaviour.
• log records the event.

The lifecycle also includes pre_input, post_tool and pre_output. This gives the agent implementation policy points before input processing, before tool execution, after tool execution and before output handling.

Redline also scores each incoming message at pre_input with a prompt-injection classifier. Redline Guard v5 reports 0.9484 recall at 1.4% false positives on BIPIA, with its losses published. The classifier therefore sits alongside deterministic tool rules rather than replacing them.

Further detail is available in runtime guardrails for AI agents.

What does each platform cover beyond the agent?

Redline’s stated scope is the agents connected to it. It does not cover model files, datasets or network traffic. Its security model is therefore centred on the implementation that your team runs, the tools and MCP servers attached to it, and the decisions made within that process.

Prisma AIRS documents a broader set of controls. Its platform scope includes AI applications, agents, models and data across the enterprise. Its model security coverage includes scanning for tampering, malicious scripts and deserialisation attacks. It also documents posture over data, models and access.

The platform additionally describes agent discovery across SaaS, cloud and low-code tools. This matters when a security team needs visibility beyond the agents built directly by its own engineering group. It also places agent controls alongside enterprise AI traffic and model protections.

The distinction is not simply a feature checklist. It reflects two different operating models:

• Redline is centred on the agent runtime and the development team responsible for that agent.
• Prisma AIRS is centred on enterprise AI discovery, posture, traffic, models, data and agents.
• A team choosing between them should identify whether its immediate control gap is inside a particular agent or across the wider AI estate.

Which deployment model fits a security team?

Choose Prisma AIRS when

Prisma AIRS is the more suitable fit when security for AI is intended to live with the rest of a Palo Alto Networks estate. Its documented scope includes firewalls, Strata Cloud Manager and cloud accounts, alongside models, datasets and shadow agents.

This approach is also appropriate when the organisation needs controls that extend beyond the agents its own team builds. The platform’s documented capabilities cover discovery, posture management, model security, runtime security and red teaming.

Its red-team workflow is endpoint-oriented. Private endpoints can be reached without opening inbound firewall ports, while runtime protection can be delivered through the managed AI runtime firewall or through the Scan API called from application code.

Choose Redline when

Redline is the more suitable fit when the primary problem is the agents a team ships. Its SDK connects outbound from wherever the agent runs and pulls work, so the agent is not exposed as an endpoint. Each test can run against the assembled agent with its real tools, MCP servers and skills attached.

Redline also keeps the runtime gate in the agent process. The tool name and every argument are available to the pre_tool rule before execution, and irreversible actions can be held for approval. This is suited to teams that want pre-release testing and production controls around the same agent implementation.

The deployment model avoids placing a component in front of the agent’s traffic. The agent connects outbound, while policy evaluation takes place at the process lifecycle points.

Use both when the boundaries are different

Using both is a valid configuration when the organisation needs a network-level AI firewall and an in-process tool gate. These controls inspect different things at different points.

A network-level control can assess AI traffic and provide broader enterprise coverage. An in-process control can inspect the exact tool call and every argument before the agent invokes the tool. The two approaches can therefore address separate control requirements rather than being treated as interchangeable implementations.

How should a buyer compare the two?

A security review should begin with the systems that must be covered. If the requirement includes model files, datasets, network traffic, SaaS agents and cloud or low-code tools, Prisma AIRS documents those areas. If the requirement is limited to agents the team builds and connects, Redline’s scope is more directly aligned.

The next question is how the agent should be tested. Endpoint-based assessment and in-process assessment provide different views:

• An endpoint assessment begins with the target’s exposed interface and its provisioned configuration.
• An in-process assessment runs the assembled agent with its real tools, MCP servers and skills.
• A team should decide whether its acceptance criteria concern endpoint responses, completed tasks, refused attacks, or all of these.

Runtime placement should then be made explicit. A managed firewall and Scan API operate around AI traffic. A pre_tool rule operates where the call is formed. If authorisation depends on session context, tool arguments or the identity of the person approving an irreversible action, the enforcement location is material.

Finally, teams should define what constitutes evidence. Redline’s result decision is based on a second agent inspecting the machine and run record. Prisma AIRS documents granular risk insights for attack simulations and a report with a risk score. These are different reporting models and should be assessed against the team’s release and remediation process.

Key takeaways

• Prisma AIRS is designed for broader enterprise AI coverage across applications, agents, models, data, traffic and posture.
• Redline is focused on the tool-using agents a development team builds, tests and runs.
• Prisma AIRS red teaming starts from a target endpoint and uses a profiler agent and an attacker agent.
• Redline tests the assembled agent with its real tools, MCP servers and skills attached in an isolated container per run.
• Redline enforces tool-call policies inside the agent process, with the tool and every argument visible before execution.
• Both can be used together when network-level AI traffic controls and in-process agent controls are separate requirements.

Common questions

Is Redline AI an alternative to Prisma AIRS?

For the tool-using agents a team builds, yes. Both products red-team agents before release and enforce controls at runtime. For model-file scanning, AI posture over datasets and network-level AI traffic across the enterprise, no. Redline covers the agents connected to it, while Prisma AIRS documents all three broader areas.

Where does each enforce a tool-call control?

Redline evaluates the rule inside the agent process before the call, with every argument in view. Prisma AIRS documents enforcement through its managed AI runtime firewall, through the Scan API that an application calls with prompts and responses, and through Agent Security’s real-time enforcement against unauthorised actions.

Does either need the agent exposed as an endpoint?

Prisma AIRS AI Red Teaming assesses a target endpoint and can reach private endpoints without opening inbound firewall ports. Redline does not use an endpoint for this purpose. Its SDK connects outbound from wherever the agent runs and pulls work, including from a laptop behind NAT.

How are attacks counted?

Redline runs 11,204 cases across 16 attack families. Palo Alto Networks describes 50+ attack techniques and thousands of attack scenarios. A technique, scenario and test case are counted differently, so the figures are not directly comparable. The more useful comparison is the assessment model and what each run tests.

Sources

• Palo Alto Networks, Prisma AIRS platform — discover, assess and protect; managed AI runtime security; AI agent, red teaming, runtime and model security.
• Palo Alto Networks, Prisma AIRS Agent Security — agent identity, real-time security to stop unauthorised actions, agent discovery across SaaS, cloud and low-code tools.
• Palo Alto Networks, Prisma AIRS AI Red Teaming — profiler and attacker agents, 50+ techniques mapped to OWASP Top 10 and NIST.
• Palo Alto Networks, Prisma AIRS AI Runtime Security — managed next-generation firewall for prompt injection and data leaks.
• Palo Alto Networks, Prisma AIRS documentation — features and release notes, including multi-turn attacks, tool-chaining attacks in agent scans and privilege misuse detection for AI agents.
• Palo Alto Networks, AI Red Teaming targets and AI Runtime API.
• Redline, Guard benchmark, Redline AI vs Akto and monitoring docs.