AI Defense Matrix Catalog

Reco

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Overview

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

Vendor
Reco2 · Details at Cyber Company Profiles
Description
SaaS security platform that discovers shadow AI tools and AI agents, monitors agent permissions and behavior, and governs generative AI usage across enterprise apps.3
Deployment
SaaS4
Status
Active2
Compliance
SOC 2 Type 2, ISO 27001, ISO 42001, CSA STAR Level 1, GDPR1 (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 IdentifyDetect Coverage
AI Traffic Primary 3
Runtime AI Data Secondary 5
AI Agent Identities Primary 2

Framework Relevance

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

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Framework Asset class Relevant controls
NIST IR 8596 AI Traffic AI data flows; APIs; inference endpoints (traffic side); model registries and dataset sources
Runtime AI Data Prompts (runtime); inference data
AI Agent Identities Agents as autonomous principals; Keys; Integrations and permissions
CSA AI Controls Matrix AI Traffic Infrastructure Security; Interoperability and Portability
Runtime AI Data Data Security and Privacy Lifecycle Management; Application and Interface Security
AI Agent Identities IAM; Governance, Risk and Compliance
ISO 42001 AI Traffic A.8 Information for interested parties; A.9 Use of AI systems; A.10 Third-party and customer relationships
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 Traffic Harden and monitor infrastructure; network-level access and egress controls
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 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)
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 Traffic AML.T0057 LLM Data Leakage; AML.T0024 Exfiltration via AI Inference API (network-side observation)
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)
AI Agent Identities AML.T0053 AI Agent Tool Invocation; credential and delegation-chain abuse
OWASP AI Exchange AI Traffic Runtime threats: data leakage via AI egress; network-level access control gaps
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 Traffic LLM10 Unbounded Consumption (cost and rate control); shadow AI egress and output handling
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 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)
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 2026-10-03.

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Sources

  1. Reco Trust Center
    Vendor source accessed 2026-06-13
    • “ISO/IEC 42001:2023”
  2. Reco homepage
    Vendor source accessed 2026-10-05
    • “View one live map of your entire third-party ecosystem: every agent, every app, every connection between them.”
  3. Reco agentic AI security page
    Vendor source accessed 2026-10-03
    • “Reco Dynamic SaaS Security empowers security teams to identify, assess, and govern generative AI usage across the enterprise”
  4. Reco application discovery page
    Vendor source accessed 2026-06-10
  5. Reco shadow AI discovery use case
    Vendor source accessed 2026-06-10
    • “Reco provides complete visibility into how shadow AI tools access and potentially expose sensitive information.”

Changelog

  1. Refreshed source citations.

  2. Corrected matrix coverage (erratum): moved AI app discovery to the AI Traffic row.

  3. Refined matrix coverage.

  4. Refined matrix coverage.

  5. Added to the catalog from the Reco documentation.

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