Vendor research
Palo Alto Networks Prisma AIRS
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
Related research availableBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Established?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity signal, not a product-quality rating.
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EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $9.2B annual revenue (2025-07-31)
- 1000+ employees
- Founded 2005
Company context
Public company (Nasdaq: PANW)
Palo Alto Networks states it serves 70,000+ customers across network, cloud, security operations, AI, and identity
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 18 records
- Source-checked records
- 17
- Evaluation requirements
- 18 in this research model
- Unresolved requirements
- 1
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Founded2005
HeadquartersSanta Clara, California
OwnershipPublic company (Nasdaq: PANW)
Employees1000+
Capital and scalePlatform provider
Palo Alto Networks
- Latest annual company revenue
- $9.2B
- Operating scale
- Palo Alto Networks states it serves 70,000+ customers across network, cloud, security operations, AI, and identity
- Backing context
- Public company; current AI security platform includes organic Prisma AIRS work plus completed Protect AI and Portkey acquisitions
Palo Alto Networks Inc · period ended 2025-07-31 · filed 2025-08-29
Founders and leadershipBackground context
Current leadership and public filings provide more useful context for this company than historical founder information.
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- 2005
- Workforce scale
- 1000+
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
Company sources and research limits7 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourcePalo Alto Networks Prisma AIRS pagePalo Alto Networks positions Prisma AIRS across AI agents, apps, models, data, discovery, posture, runtime, red teaming, and agent security.Company sourcePalo Alto Networks AI Access Security pagePalo Alto Networks AI Access Security describes GenAI app discovery, sanction-state controls, data classification, and sensitive-data blocking.Company sourcePalo Alto Networks Protect AI acquisition announcementPalo Alto Networks completed the Protect AI acquisition in July 2025 and tied model scanning, posture management, red teaming, runtime protection, and AI agent security into Prisma AIRS.Company sourcePalo Alto Networks Portkey acquisition completionPalo Alto Networks completed its acquisition of Portkey on May 29, 2026 and positioned Portkey as the core AI Gateway for Prisma AIRS.Company information sourcecompany_intelSupports the company facts shown in this profile.Company information sourceweb_extractSupports the company facts shown in this profile.Regulatory filing10-K annual filingPalo Alto Networks Inc (PANW) · period ended 2025-07-31
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
AI application runtime protectionCore product focusAI gateway and tool-connection controlsCore product focusAI testing and adversarial assuranceCore product focusEmployee AI access and usage controlsCore product focusNetwork and cloud access controlsCore product focusAction-taking agent safeguardsRelated coverageExposed AI asset discoveryRelated coverageSensitive-data discovery and accessRelated coverageBrowser and extension controlsRelated coverageAI asset and configuration securityRelated coverage
Buyer context
- For current Palo Alto Networks, Prisma SASE, Prisma Cloud, Cortex, or Unit 42 customers, treat Prisma AIRS as a platform-extension candidate and verify packaging and entitlements across AIRS, AI Access Security, Protect AI capabilities, and Portkey.
- Particularly relevant when the buyer wants one control plane spanning workforce generative AI, custom AI apps, AI models, runtime controls, agent identity, and agentic traffic governance.
- Palo Alto Networks completed the Portkey acquisition on May 29, 2026; validate which Portkey capabilities are integrated, generally available, and included in the proposed entitlement.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCSA AI Controls Matrix
- Requirements with public support
- 17
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 16
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 16
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 4
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03AI-feature discovery in business applications
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 06Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 07Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 08Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
Strong public supportInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Strong public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
Strong public supportBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
Strong public supportDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
Strong public supportApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Strong public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Limited public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
Strong public supportTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
Strong public supportDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
Strong public supportMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
Strong public supportAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
Strong public supportInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
Strong public supportRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
Limited public supportDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
No supporting claim foundVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
Strong public supportPublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 18 source records. Open additional records only when needed.
Palo Alto Networks claims Prisma AIRS discovers shadow AI and provides visibility into AI agents, apps, and models.
Get full visibility into every AI agent, app and model, and identify how they connect across your environment.
Palo Alto Networks claims AI Access Security discovers and categorizes generative AI applications, agents, and marketplace plugins.
Access the most up-to-date GenAI app dictionary to accurately discover and categorize GenAI applications, agents and marketplace plugins.
Palo Alto Networks claims Prisma AIRS provides visibility and control over AI data, agent and app integrity, and deployed model access.
Gain visibility and control over critical components of your AI data used for training or inference, the integrity of AI agents and apps, and access to deployed models.
Palo Alto Networks claims AI Access Security can classify generative AI apps by sanction status and enforce access, upload, and download controls.
Classify apps as sanctioned, tolerated or unsanctioned, and implement robust access controls.
Palo Alto Networks claims AI Access Security blocks sensitive text and file data transfer to generative AI apps.
Inline data detection ensures regulatory compliance and blocks sensitive text- and file-based data transfer to GenAI apps.
Palo Alto Networks claims AI Access Security provides workforce generative AI visibility, risk classification, access controls, and user coaching.
Proactively reduce employee risk with notifications and user coaching directly from AI Access Security.
Show 12 additional evidence records
Palo Alto Networks claims Prisma AIRS red teams AI agents and applications and provides runtime safeguards against manipulation, data exposure, and unsafe actions.
Monitor AI behavior. Enforce real-time safeguards to prevent manipulation, data exposure, and unsafe actions during live AI interactions.
Palo Alto Networks says Prisma AIRS with Portkey is intended to monitor, govern, and protect AI applications, models, and autonomous agents.
With the integration of Portkey’s AI Gateway into Prisma AIRS, we will deliver a centralized control plane to monitor, govern, and protect every AI application, model, and autonomous agent across your organization
Palo Alto Networks says Portkey provides an AI Gateway for Prisma AIRS that can monitor, route, and secure AI transactions and supports agent-to-agent communication.
monitor, route, and secure every AI transaction across the enterprise.
Palo Alto Networks claims Prisma AIRS verifies AI agent identity and applies least-privilege controls to agent interactions.
By enforcing AI Identity Security, it will apply strict least-privilege controls to every agent interaction, ensuring all AI workloads remain secure and compliant.
Palo Alto Networks claims centralized visibility and governance over agents, models, and interactions, including ownership, permissions, and least-privileged access.
Move from pilots to production with full visibility and governance over every agent, model and interaction.
Palo Alto Networks claims automated AI red teaming for applications and agents using simulated real-world threats and security-vulnerability scanning.
AI Red Teaming: Prisma AIRS Provides automated AI Red Teaming that you can use for scanning your AI applications or agents for security vulnerabilities.
Palo Alto Networks claims predeployment model and agent-artifact scanning across models, source code, Model Context Protocol (MCP) servers, and skills.
Scan supply chain vulnerabilities in agent artifacts, including agent code, MCP servers, and skills.
Palo Alto Networks claims centralized control of tool calls, large language model (LLM) interactions, and Model Context Protocol (MCP) connections with granular runtime policy enforcement.
Gain centralized control of tool calls, LLM interactions, and Model Context Protocol (MCP) connections — enforcing granular policies on how agents interact with systems.
Palo Alto Networks claims agent identity inventory and validation, accountable ownership, permissions, least-privileged access, and privilege revocation.
Inventory and validate the identities of agents operating in your enterprise. Define AI agent ownership, permissions and enforce least-privileged access for AI agents.
Palo Alto Networks claims agent artifact and endpoint security covering agent code, Model Context Protocol (MCP) servers, skills, local systems, files, and workflows.
Agentic Endpoint Security extends protection to endpoints, governing how agents interact with local systems, files and workflows.
Prisma AIRS materials reviewed did not establish customer AI workload cost attribution, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
Palo Alto Networks documents Prisma AIRS as bring-your-own-license funded through Software NGFW credits, with runtime firewall capacity based on instances and vCPUs and runtime application programming interface (API) usage measured in monthly token allocations.
Prisma AIRS uses a bring-your-own license (BYOL) model.