AI Defense Matrix Catalog

Cloaked AI

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Overview

Product details compiled from public sources, each with a citation.

Vendor
IronCore Labs2
Description
IronCore Labs SDK that applies searchable, data-in-use encryption to AI vector embeddings so RAG and vector-search apps run on protected sensitive data.2
Deployment
Self-hosted2
Status
Active1
Compliance
SOC 23 (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 Protect Coverage
Training Data Secondary 2
Runtime AI Data Primary 2

Framework Relevance

These frameworks include controls relevant to the asset classes Cloaked AI defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that IronCore Labs implements these controls or is certified against them.

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Framework Asset class Relevant controls
NIST IR 8596 Training Data Training data
Runtime AI Data Prompts (runtime); inference data
CSA AI Controls Matrix 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 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 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 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 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 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 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 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 2026-06-25.

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Sources

  1. ironcore-alloy GitHub repository
    Vendor source accessed 2026-06-25
  2. Cloaked AI product page
    Vendor source accessed 2026-06-25
    • “Unleash generative AI projects that use private data by protecting sensitive vector embeddings with searchable data-in-use encryption.”
    • “Protects your AI data while allowing GenAI workflows”
  3. IronCore Labs trust center
    Vendor source accessed 2026-06-25
    • “we are SOC 2 certified”

Changelog

  1. Added to the catalog from the IronCore Labs documentation.

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