AI data protectionAdditional if: Sensitive content enters AI flows · Sensitive content must be inspected or blocked in prompts, responses, files, retrieved information, or tool calls.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.Action-taking agent safeguardsDirectly addresses · Observe and govern agent plans, memory, tool use, delegated tasks, and actions while the agent runs.
Vendor research
AIM Security / Cato Networks
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
Related research availableBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Scaled?
ScaledA private provider with at least $100M in known funding or at least 250 employees.This is a company-scale signal, not a product-quality rating.
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ScaledA private provider with at least $100M in known funding or at least 250 employees.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $1B known funding
- 250-1000 employees
- Founded 2015
Company context
Acquired by privately held Cato Networks
Cato reported more than $300M ARR when announcing the Aim acquisition
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 14
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 4
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Founded2022
HeadquartersTel Aviv, Israel
OwnershipAcquired by privately held Cato Networks
Employees250-1000
Capital and scaleOwned business
Cato Networks
- Known funding
- $1B
- Operating scale
- Cato reported more than $300M ARR when announcing the Aim acquisition
- Backing context
- Aim was backed by YL Ventures and Canaan Partners; pre-acquisition round amount was not confirmed in public sources reviewed; parent Cato expanded its Series G to $409M
Series G · $409M · 2025-09-03
Named investors
Vitruvian Partners · ION Crossover Partners · Lightspeed Venture Partners · Acrew Capital · Adams Street Partners
Founders and leadership1 person listed
- Shlomo KramerCurrent role listed
Co-Founder and CEO
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- 2022
- Workforce scale
- 250-1000
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
Company sources and research limits5 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceCato Networks announcementCato announced acquiring Aim Security, surpassing $300M ARR, and expanding its Series G to $409M.Company sourceCB Insights company profilePublic company profile lists Aim as founded in 2022 and based in Tel Aviv.Company information sourcecompany_intelSupports the company facts shown in this profile.Company information sourcecompany_intelSupports the company facts shown in this profile.Company information sourceCato Series G funding announcementSupports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat Aim as a Cato platform extension for procurement, commercial leverage, roadmap, and support.
- Public claims may be most relevant where the buyer is also evaluating SASE/SSE consolidation, not only AI security point tooling.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCIS Critical Security Controls
- Requirements with public support
- 14
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 14
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 3
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 04Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 05Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 06Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 07Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 08Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
No supporting claim foundInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Strong public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
Strong public supportBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
Strong public supportDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
Limited public supportApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Strong public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Limited public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
No supporting claim foundTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
Limited public supportDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
Strong public supportMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
Limited public supportAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
Limited public supportInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
Limited public supportRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
Strong public supportDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
No supporting claim foundVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
No supporting claim foundPublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 19 source records. Open additional records only when needed.
Cato AI Security claims discovery and inventory of managed, local, and shadow agents, centralized runtime visibility, security posture, compliance-risk remediation, and corporate policy across users, agents, applications, models, and tools.
Gain visibility into which agents exist, what data sources and tools they can access, and how they behave at runtime.
Cato AI Security and AIM materials reviewed did not provide a public customer-facing product claim for automated adversarial testing, repeatable attack suites, model or agent red teaming, or release-gate evaluation.
No quoted source text is recorded for this claim.
Cato and AIM claim continuous AI-SPM scanning of internal models, agent configurations, Model Context Protocol (MCP) connections, training environments, misconfigurations, and vulnerabilities before production.
Continuously discovers, detects, and remediates AI security and compliance risks before they reach production, and scans internal AI models for misconfigurations and vulnerabilities.
Cato AI Security claims runtime policy across user prompts, model outputs, tool calls, and tool messages, blocking or redacting sensitive data and indirect prompt injection before content reaches an agent or model.
Enforces runtime policies across all four inspection points: user prompts, model outputs, tool calls, and tool messages.
Cato AI Security claims visibility into agent configurations, licenses, data and tool access, plus corporate policy enforcement and blocking of unauthorized access or unsafe actions across managed and local agents.
Reduce the risk of unauthorized access, sensitive data exposure, and unsafe agent actions.
Cato claims endpoint discovery and runtime control for Cursor, Claude Code, and other coding agents, covering configurations, licenses, Model Context Protocol (MCP) servers, prompts, model outputs, tool calls, tool messages, credentials, PII, and malicious tool responses.
Discover and secure AI agents that run on user endpoints, such as coding agents.
Show 13 additional evidence records
Cato AI Security materials reviewed did not provide a public product claim for AI usage-cost attribution, token or spend metrics, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
AIM Security (now Cato AI Security) claims it surfaces AI adoption trends, assesses risks, and detects unauthorized data exchange with public AI services.
With Cato, IT teams can understand AI adoption trends with the business, assess risks and enforce granular access controls, and detect unauthorized data exchange with public AI services in real-time.
No public claim found for this capability.
No quoted source text is recorded for this claim.
AIM Security (Cato) claims it gives full visibility and control into which generative AI apps are used and what data is shared with the AI models.
full visibility and control into what GenAI apps are used, and what data is shared with the AI models
AIM Security (Cato) claims it enforces granular access controls, prevents data leakage, and limits AI misuse via policy enforcement.
enforce granular access controls, and detect unauthorized data exchange with public AI services in real-time.
AIM Security (Cato) claims monitoring and governing prompts and responses prevents data leakage and supports governance and compliance.
By monitoring and governing prompt and responses, inline, using APIs or with a browser extension, organizations can prevent data leakage, limit misuse, and ensure governance and compliance for AI use.
AIM Security (Cato) claims it monitors every interaction between agents, models, and Model Context Protocol (MCP) servers to keep agents secure and compliant.
monitor every interaction between agents, models, and MCP servers, to ensure agents operate securely, remain complaint, and align with business needs.
Cato AI Security claims monitoring every interaction between agents, models, and Model Context Protocol (MCP) servers helps ensure agents operate securely and remain aligned with business needs.
monitor every interaction between agents, models, and MCP servers, to ensure agents operate securely, remain complaint, and align with business needs.
Cato AI Security claims it discovers and analyzes enterprise AI agent activity and monitors interactions between agents, models, and Model Context Protocol (MCP) servers.
monitor every interaction between agents, models, and MCP servers
AIM (Cato) claims it lets organizations use generative AI securely with full visibility and control into which apps are used and what data is shared.
full visibility and control into what GenAI apps are used, and what data is shared with the AI models
AIM (Cato) claims it secures private AI applications in runtime and detects runtime AI attacks and compliance violations.
Homegrown AI applications and AI agents are attractive targets for internal and external attacks—an emerging threat vector that requires dedicated defenses. Using proprietary models specifically trained to detect all types of runtime AI attacks and compliance violations, IT teams can support enterprise-scale secure delivery of AI apps and agents.
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.