AccuKnox AI Security
Overview
Product details compiled from public sources, each with a citation.
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 | Respond | Coverage | Source |
|---|---|---|---|---|---|---|
| AI-Workload Platforms | Primary | 1 | ||||
| AI Coding and Orchestration Tools | Respond: Not covered | Primary | 2 | |||
| AI-Generated Code | Identify: Not covered | Detect: Not covered | Respond: Not covered | Secondary | 2 | |
| AI Traffic | Identify: Not covered | Detect: Not covered | Respond: Not covered | Secondary | 1 | |
| AI Model | Protect: Not covered | Respond: Not covered | Primary | 1 | ||
| Training Data | Protect: Not covered | Respond: Not covered | Secondary | 1 | ||
| Runtime AI Data | Identify: Not covered | Respond: Not covered | Primary | 4 |
Framework Relevance
These frameworks include controls relevant to the asset classes AccuKnox AI Security defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that AccuKnox implements these controls or is certified against them.
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| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI-Workload Platforms | Containers, microservices, and libraries (AI-specific subset); inference endpoints (platform side) |
| AI Coding and Orchestration Tools | Agents as deployed artifacts (orchestration view; see AI Agent Identities row for the principal view); system prompts and templates | |
| AI Traffic | AI data flows; APIs; inference endpoints (traffic side); model registries and dataset sources | |
| AI Model | Models; Algorithms (model configuration) | |
| Training Data | Training data | |
| Runtime AI Data | Prompts (runtime); inference data | |
| CSA AI Controls Matrix | AI-Workload Platforms | Infrastructure Security; Threat & Vulnerability Management |
| AI Coding and Orchestration Tools | Application and Interface Security; Supply Chain Management | |
| AI-Generated Code | Application and Interface Security; Supply Chain Management | |
| AI Traffic | Infrastructure Security; Interoperability and Portability | |
| 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-Workload Platforms | A.6 AI system life cycle; A.4 Resources for AI systems |
| AI Coding and Orchestration Tools | A.6 AI system life cycle; A.5 Assessing impacts of AI systems | |
| AI-Generated Code | A.6 AI system life cycle | |
| AI Traffic | A.8 Information for interested parties; A.9 Use of AI systems; A.10 Third-party and customer relationships | |
| 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-Workload Platforms | Expand strong security foundations; secure and harden the AI deployment environment |
| AI Coding and Orchestration Tools | Secure the AI supply chain; application and pipeline security; agent orchestration controls | |
| AI-Generated Code | Secure the AI pipeline; code provenance and supply chain integrity | |
| AI Traffic | Harden and monitor infrastructure; network-level access and egress 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-Workload Platforms | Conventional Security Controls (host AI within the existing ISMS; authentication and access controls; encryption at rest); AI Supply Chain Management (local vs. SaaS hosting trade-offs; internal model garden) |
| AI Coding and 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-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) | |
| AI Traffic | Conventional Security Controls (authenticate and control access to inference APIs; API key management); Model I/O Handling (rate limiting; egress output filtering); Monitoring (interaction and API-usage logging) | |
| 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-Workload Platforms | AML.T0010 AI Supply Chain Compromise; AML.T0012 Valid Accounts (platform credential abuse); container and inference-server exploits |
| AI Coding and Orchestration Tools | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0016 Obtain Capabilities (malicious plugins) | |
| AI-Generated Code | AML.T0010 AI Supply Chain Compromise (hallucinated dependencies and slopsquatting); AML.T0018 Manipulate AI Model (when models embed code-execution backdoors) | |
| AI Traffic | AML.T0057 LLM Data Leakage; AML.T0024 Exfiltration via AI Inference API (network-side observation) | |
| 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 (system prompt leaks as inference data; the AI Coding and Orchestration Tools row owns it as configuration) | |
| OWASP AI Exchange | AI-Workload Platforms | Development-time threats: supply chain attacks, model-platform CVEs, container escape |
| AI Coding and Orchestration Tools | Development-time threats: agent framework supply chain; runtime threats: plugin abuse, prompt injection via tools | |
| AI-Generated Code | Development-time threats: insecure code generation, license risk, hallucinated dependencies | |
| AI Traffic | Runtime threats: data leakage via AI egress; network-level access control gaps | |
| 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-Workload Platforms | LLM03 Supply Chain (compromised AI platform components); LLM04 Data and Model Poisoning (via platform) |
| AI Coding and Orchestration Tools | LLM01 Prompt Injection; LLM05 Improper Output Handling; LLM07 System Prompt Leakage; LLM10 Unbounded Consumption | |
| AI-Generated Code | LLM06 Excessive Agency (code execution); insecure or vulnerable code patterns inherited from training data | |
| AI Traffic | LLM10 Unbounded Consumption (cost and rate control); shadow AI egress and output handling | |
| 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-Workload Platforms | ASI04 Agentic Supply Chain Vulnerabilities (model and tool-platform components); ASI08 Cascading Failures (platform fault propagation) |
| AI Coding and 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-Generated Code | ASI05 Unexpected Code Execution (RCE); ASI04 Agentic Supply Chain Vulnerabilities (hallucinated dependencies and vibe-coding artifacts) | |
| AI Traffic | ASI07 Insecure Inter-Agent Communication; ASI02 Tool Misuse and Exploitation (egress and tool-invocation scope); ASI04 Agentic Supply Chain Vulnerabilities (MCP and tool-registry trust) | |
| 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 2026-09-23.
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Sources
- AccuKnox AI Security platform page
- “Builds a live, agentless inventory of every model, agent, dataset, and pipeline, spanning cloud and on-prem, in one dashboard.”
- “Scans all 5 major model formats for supply-chain tampering and deserialization attacks.”
- “Scans training datasets for PII/PHI before it becomes a compliance incident.”
- “Prompt Firewall, AI Gateway Enforcement, Response Filtering, Policy Analytics, and usage monitoring.”
- AccuKnox Agentic AI Security
- “Enforce least-privilege at every agent step. Kernel-level sandboxing via eBPF.”
- “Isolates LLM-generated scripts and code execution to prevent malicious runtime behavior.”
- AI-SPM SaaS vs On-Prem Deployment
- AccuKnox Prompt Firewall documentation
- “AccuKnox Prompt Firewall is a real-time inline security layer that sits between your users and your LLM.”
Changelog
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Added at the request of a contributor affiliated with AccuKnox, from the AccuKnox product pages and help documentation.
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