Overview

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

Vendor
Aim Security1 · Details at Cyber Company Profiles
Description
AI security platform that secures employee use of public AI applications, shields private AI apps and agents with an AI firewall, and covers the AI development lifecycle with AI-SPM.1
Deployment
SaaS1
Status
Acquired1
Acquisition
Acquired by Cato Networks, announced 2025-09-03. It has been folded into the acquirer as Cato AI Security.1

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 IdentifyProtectDetect Coverage Source
AI Traffic Protect: Not covered Detect: Not covered Primary 1
AI Model Protect: Not covered Detect: Not covered Secondary 1
Runtime AI Data Identify: Not covered Primary 1

Framework Relevance

These frameworks include controls relevant to the asset classes Aim Security defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Aim Security 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
AI Model Models; Algorithms (model configuration)
Runtime AI Data Prompts (runtime); inference data
CSA AI Controls Matrix AI Traffic Infrastructure Security; Interoperability and Portability
AI Model Model Security; Governance, Risk and Compliance
Runtime AI Data Data Security and Privacy Lifecycle Management; Application and Interface Security
ISO 42001 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
Runtime AI Data A.7 Data for AI systems; A.8 Information for interested parties
Google SAIF 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
Runtime AI Data Expand AI red-teaming; runtime input and output safety; prompt defense
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)
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)
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 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)
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 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
Runtime AI Data Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering
OWASP LLM Top 10 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
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 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)
Runtime AI Data ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs)

Provenance

Last sourced 2026-10-03.

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Sources

  1. Cato Networks acquires Aim Security
    Vendor source accessed 2026-10-03
    • “Aim discovers shadow AI usage, monitors and protects all end-user AI interactions”
    • “Aim continuously discovers, detects, and remediates AI security and compliance risks before they reach production, and scans internal AI models for misconfigurations and vulnerabilities to enable customers to maintain optimal AI model security posture at all times.”

Changelog

  1. Corrected matrix coverage (erratum): moved shadow AI discovery to the AI Traffic row.

  2. Refreshed source links and quotes against the current vendor pages.

  3. Replaced quote waivers with verbatim source quotes on the coverage citations.

  4. Recorded quote waivers on citations whose pages expose no server-rendered text.

  5. Added to the catalog from the Cato Networks documentation. Recorded the Cato Networks acquisition announced in September 2025, with Aim folded into the Cato SASE Cloud Platform as its AI security pillar.

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