Protect AI
Overview
Product details compiled from public sources, each with a citation.
- Vendor
- Protect AI1
- Description
- A unified platform that secures the AI lifecycle: model scanning (Guardian), automated red teaming (Recon), and runtime protection (Layer).1
- Deployment
- SaaS, Self-hosted2
- Status
- Acquired5
- Acquisition
- Acquired by Palo Alto Networks, announced 2025-07-22. It has been folded into the acquirer as Prisma AIRS.5
Matrix Coverage
Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.
| Asset class | Identify | Protect | Detect | Coverage | Source |
|---|---|---|---|---|---|
| AI Orchestration Tools | Protect: Not covered | Primary | 4 | ||
| AI Model | Detect: Not covered | Primary | 2 | ||
| Training Data | Identify: Not covered | Protect: Not covered | Secondary | 2 | |
| Runtime AI Data | Identify: Not covered | Protect: Not covered | Secondary | 3 |
Framework Relevance
These frameworks include controls relevant to the asset classes Protect AI defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Protect AI implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI Orchestration Tools | Agents as deployed artifacts (orchestration view; see AI Agent Identities row for the principal view); system prompts and templates |
| AI Model | Models; Algorithms (model configuration) | |
| Training Data | Training data | |
| Runtime AI Data | Prompts (runtime); inference data | |
| CSA AI Controls Matrix | AI Orchestration Tools | Application and Interface Security; Supply Chain Management |
| AI Model | Model Security; Governance, Risk and Compliance | |
| Training Data | Data Security and Privacy Lifecycle Management; Model Security | |
| Runtime AI Data | Data Security and Privacy Lifecycle Management; Application and Interface Security | |
| ISO 42001 | AI Orchestration Tools | A.6 AI system life cycle; A.5 Assessing impacts of AI systems |
| AI Model | A.6 AI system life cycle; A.10 Third-party and customer relationships; A.5 Assessing impacts of AI systems | |
| Training Data | A.7 Data for AI systems | |
| Runtime AI Data | A.7 Data for AI systems; A.8 Information for interested parties | |
| Google SAIF | AI Orchestration Tools | Secure the AI supply chain; application and pipeline security; agent orchestration controls |
| AI Model | Protect the AI model; ensure model integrity, provenance, and weight security | |
| Training Data | Secure training data; data-security foundations; dataset provenance and integrity | |
| Runtime AI Data | Expand AI red-teaming; runtime input and output safety; prompt defense | |
| SANS Critical AI Security Guidelines | AI Orchestration Tools | Secure Agentic Systems and AI Autonomy Controls (defined function scope; execution isolation; API and function-call gating); Limit Model Behavior (focused functionality; access controls outside the model) |
| AI Model | Conventional Security Controls (protect model parameters with least privilege, encryption at rest, runtime obfuscation, and trusted execution environments); Data/Model Engineering Controls (adversarial training; alignment and fine-tuning); AI Supply Chain Management (public-model caution; transfer-attack exposure) | |
| Training Data | Conventional Security Controls (defend training data; avoid data commingling); Data/Model Engineering Controls (data-quality controls; poison-robust training); Data Minimization and Obfuscation (differential privacy; synthetic data; federated learning) | |
| Runtime AI Data | Model I/O Handling (sanitize, validate, and filter inputs and outputs; segregate user and system prompts; multilayered prompt-injection defense); Conventional Security Controls (protect augmentation and RAG data with vector-store access controls and validation); Data Minimization and Obfuscation (limit sensitive prompt content; context-window management); Limit Model Behavior (AI guardrails) | |
| MITRE ATLAS | AI Orchestration Tools | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0016 Obtain Capabilities (malicious plugins) |
| AI Model | AML.T0043 Craft Adversarial Data; AML.T0024 Exfiltration via AI Inference API (subtechniques: AML.T0024.001 Invert AI Model and AML.T0024.002 Extract AI Model); AML.T0018 Manipulate AI Model (integrity and backdoor) | |
| Training Data | AML.T0020 Poison Training Data; AML.T0019 Publish Poisoned Datasets; AML.T0024.000 Infer Training Data Membership | |
| Runtime AI Data | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt | |
| OWASP AI Exchange | AI Orchestration Tools | Development-time threats: agent framework supply chain; runtime threats: plugin abuse, prompt injection via tools |
| AI Model | Development-time and runtime model threats: model inversion, extraction, evasion, poisoning | |
| Training Data | Development-time threats: data poisoning, backdoor injection, dataset integrity violations | |
| Runtime AI Data | Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering | |
| OWASP LLM Top 10 | AI Orchestration Tools | LLM01 Prompt Injection; LLM05 Improper Output Handling; LLM07 System Prompt Leakage; LLM10 Unbounded Consumption |
| AI Model | LLM03 Supply Chain; LLM04 Data and Model Poisoning; LLM09 Misinformation | |
| Training Data | LLM04 Data and Model Poisoning; LLM03 Supply Chain (dataset provenance) | |
| Runtime AI Data | LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling | |
| OWASP Agentic Security Top 10 | AI Orchestration Tools | ASI01 Agent Goal Hijack; ASI02 Tool Misuse and Exploitation; ASI05 Unexpected Code Execution (RCE); ASI07 Insecure Inter-Agent Communication; ASI08 Cascading Failures; ASI10 Rogue Agents |
| AI Model | ASI04 Agentic Supply Chain Vulnerabilities (model provenance, weights, and dynamic loading) | |
| Training Data | ASI04 Agentic Supply Chain Vulnerabilities (dataset provenance and integrity) | |
| Runtime AI Data | ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs) |
Provenance
Last sourced 2026-06-09.
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Sources
- Protect AI platform
- Guardian CI/CD scanning
- “Protect your most sensitive intellectual property with distributed, on-premises, and local scanning.”
- “identifying deserialization, architectural backdoors, and runtime threats across all major model formats.”
- protectai.com/layer
- “Rather than only monitoring prompts and outputs, Layer tracks the entire conversation flow, including tools, function calls, downstream workflows, multi-turn attacks, and metadata.”
- protectai.com/recon
- “Using trained LLMs as detectors, Recon delivers accuracy, not just coverage, to ensure your systems remain protected against a wide range of evolving vulnerabilities with limited false positives.”
- Palo Alto completes Protect AI acquisition
Changelog
-
Recorded the post-acquisition product name (Prisma AIRS).
-
Verified details and recorded the Palo Alto Networks acquisition and fold into Prisma AIRS.
Found an error? Corrections are welcome. Suggest an edit.