Rubrik Agent Cloud
Overview
Product details compiled from public sources, each with a citation.
- Vendor
- Rubrik4
- Description
- Monitors enterprise AI agents, applies SAGE semantic guardrails in real time, and rewinds destructive agent actions.4
- Deployment
- SaaS1
- Status
- Active1
- Compliance
- SOC 2 Type 2, SOC 1 Type 2, ISO 27001, ISO 27017, ISO 27018, ISO 27701, ISO 42001, HIPAA, HITRUST, GDPR3agent (company-level, see Methodology)
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 | Recover | Coverage | Source |
|---|---|---|---|---|---|---|
| AI-Generated Code | Identify: Not covered | Protect: Not covered | Detect: Not covered | Secondary | 1 | |
| Runtime AI Data | Identify: Not covered | Recover: Not covered | Primary | 2 | ||
| AI Agent Identities | Protect: Not covered | Detect: Not covered | Primary | 4 |
Framework Relevance
These frameworks include controls relevant to the asset classes Rubrik Agent Cloud defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Rubrik implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | Runtime AI Data | Prompts (runtime); inference data |
| AI Agent Identities | Agents as autonomous principals; Keys; Integrations and permissions | |
| CSA AI Controls Matrix | AI-Generated Code | Application and Interface Security; Supply Chain Management |
| Runtime AI Data | Data Security and Privacy Lifecycle Management; Application and Interface Security | |
| AI Agent Identities | IAM; Governance, Risk and Compliance | |
| ISO 42001 | AI-Generated Code | A.6 AI system life cycle |
| Runtime AI Data | A.7 Data for AI systems; A.8 Information for interested parties | |
| AI Agent Identities | A.9 Use of AI systems; A.3 Internal organization; A.5 Assessing impacts of AI systems | |
| Google SAIF | AI-Generated Code | Secure the AI pipeline; code provenance and supply chain integrity |
| Runtime AI Data | Expand AI red-teaming; runtime input and output safety; prompt defense | |
| AI Agent Identities | Focus on Agents (explicit SAIF section); identity, authorization, and delegation controls | |
| SANS Critical AI Security Guidelines | AI-Generated Code | Model I/O Handling (AI deployment in IDEs: prefer local-only integrations to limit exposure of code, keys, and proprietary data); Governance, Risk, Compliance (regularly test and red-team AI applications before and after deployment) |
| 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) | |
| AI Agent Identities | Secure Agentic Systems and AI Autonomy Controls (defined function scope; API and function-call gating; escalation and fallback); Limit Model Behavior (least-privilege focused functionality; human oversight; override capabilities) | |
| MITRE ATLAS | AI-Generated Code | AML.T0010 AI Supply Chain Compromise (hallucinated dependencies and slopsquatting); AML.T0018 Manipulate AI Model (when models embed code-execution backdoors) |
| Runtime AI Data | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt | |
| AI Agent Identities | AML.T0053 AI Agent Tool Invocation; credential and delegation-chain abuse | |
| OWASP AI Exchange | AI-Generated Code | Development-time threats: insecure code generation, license risk, hallucinated dependencies |
| Runtime AI Data | Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering | |
| AI Agent Identities | Runtime threats: unauthorized agent actions, capability abuse, delegation chain exploitation | |
| OWASP LLM Top 10 | AI-Generated Code | LLM06 Excessive Agency (code execution); insecure or vulnerable code patterns inherited from training data |
| Runtime AI Data | LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling | |
| AI Agent Identities | LLM06 Excessive Agency; LLM05 Improper Output Handling; unauthorized actions by AI agents | |
| OWASP Agentic Security Top 10 | AI-Generated Code | ASI05 Unexpected Code Execution (RCE); ASI04 Agentic Supply Chain Vulnerabilities (hallucinated dependencies and vibe-coding artifacts) |
| Runtime AI Data | ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs) | |
| AI Agent Identities | ASI03 Identity and Privilege Abuse; ASI10 Rogue Agents; ASI09 Human-Agent Trust Exploitation; ASI02 Tool Misuse and Exploitation (when tied to agent permissions) |
Provenance
Last sourced 2026-07-16.
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Sources
- SiliconANGLE on Rubrik Agent Cloud for Claude
- “reverses unintended actions from any agent, including those built in Claude Code and Cowork”
- Rubrik press release on the SAGE governance engine
- Rubrik Security Compliance Program
- “At Rubrik, we are SOC 2 Type II certified against the security, confidentiality and availability criteria.”
- Rubrik Agent Cloud product page
Changelog
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Added verbatim source quotes and a quote waiver to coverage and compliance citations.
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Refined matrix coverage and primary function.
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Added to the catalog from the Rubrik documentation.
Found an error? Corrections are welcome. Suggest an edit.