Employee AI access and usage controlsDirectly addresses · Discover employee AI use and control access to approved and unapproved AI services.Network and cloud access controlsAdditional if: Employee AI traffic crosses managed web or cloud controls · Employee AI traffic passes through security controls for web, cloud, or business applications.Action-taking agent safeguardsAdditional if: AI takes multi-step actions · The employee-facing AI performs multiple steps or takes actions rather than only generating content.AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Calls to models, tools, or connections are routed through a security gateway, including Model Context Protocol (MCP) connections.
Vendor research
Cisco AI Defense
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
Established?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity signal, not a product-quality rating.
?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity 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.
- $56.7B annual revenue (2025-07-26)
- 1000+ employees
- Founded 1984
Company context
Public company (Nasdaq: CSCO)
Cisco positions its platform around networking, security, observability, collaboration, and AI-era infrastructure
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 18 records
- Source-checked records
- 14
- Evaluation requirements
- 18 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.
Founded1984
HeadquartersSan Jose, California
OwnershipPublic company (Nasdaq: CSCO)
Employees1000+
Capital and scalePlatform provider
Cisco
- Latest annual company revenue
- $56.7B
- Operating scale
- Cisco positions its platform around networking, security, observability, collaboration, and AI-era infrastructure
- Backing context
- Public company; AI Defense is a Cisco security product family
CISCO SYSTEMS, INC. · period ended 2025-07-26 · filed 2025-09-03
Founders and leadershipBackground context
Current leadership and public filings provide more useful context for this company than historical founder information.
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
- 1984
- Workforce scale
- 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 limits4 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 sourceCisco AI Defense pageCisco AI Defense describes AI Access, AI Runtime Protection, AI Cloud Visibility, AI Model and Application Validation, and AI Supply Chain Risk Management.Company sourceCisco about pageCisco's about page describes its role across networking, security, observability, collaboration, and AI-era infrastructure, and lists San Jose headquarters.Company information sourceStored company websiteSupports the company facts shown in this profile.Regulatory filing10-K annual filingCISCO SYSTEMS, INC. (CSCO) · period ended 2025-07-26
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Employee AI access and usage controlsCore product focusAI application runtime protectionCore product focusAI testing and adversarial assuranceCore product focusNetwork and cloud access controlsRelated coverageAction-taking agent safeguardsRelated coverageAI gateway and tool-connection controlsRelated coverage
Buyer context
- For current Cisco security, networking, Secure Access, firewall, or Splunk customers, evaluate AI Defense as a platform-adjacent option that can use network-layer visibility and existing telemetry.
- Cisco's official AI Defense materials are strong for AI app access, model/application validation, runtime guardrails, cloud visibility, and data exposure reduction.
- Do not over-credit Cisco for Model Context Protocol (MCP), agent-to-agent (A2A), or non-human identity (NHI) lifecycle coverage unless additional official product evidence is confirmed.
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 referenceCSA AI Controls Matrix
- Requirements with public support
- 14
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 13
- 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.
- 03AI-feature discovery in business applications
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.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 06Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 07Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 08Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
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
Strong public supportInventory 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
Limited 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
Strong public supportTest 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
Strong 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
Limited 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
No supporting claim foundAuthorize, 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
No supporting claim foundInventory, 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
No supporting claim foundDiscover 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
Strong public supportPublicly 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 18 source records. Open additional records only when needed.
Cisco claims AI Defense can surface third-party AI applications in use and discover AI workloads, applications, models, data, and users across distributed environments.
Automatically surface third-party AI applications in use across your organization.
Cisco claims AI Access monitors and manages access to third-party AI applications.
Monitor and manage access to third-party AI applications.
Cisco claims AI Defense discovers AI workloads, applications, models, data, and users across distributed cloud environments.
Discover the AI workloads, applications, models, data, and users across your distributed cloud environments.
Cisco claims AI Access can enforce policies that limit sensitive data exposure and protect against external threats for third-party AI applications.
Enforce policies that limit sensitive data exposure and protect against external threats.
Cisco claims AI Defense prevents sensitive data loss and protects AI applications against data leakage.
Protect AI applications against rapidly evolving threats, including prompt injections, denial of service, and data leakage.
Cisco claims AI Defense can manage employee access to third-party AI applications and prevent sensitive data loss.
Define policies that manage employee access, protect against threats, and prevent sensitive data loss.
Show 12 additional evidence records
Cisco claims AI Defense validates AI models and applications and protects production AI applications with network-embedded guardrails.
Protect production AI applications with guardrails embedded in the network.
Cisco AI Defense mentions red teaming AI models and agents, but reviewed materials do not show broad autonomous-agent telemetry or graph visibility.
Try AI red teaming for your models and agents today
Cisco AI Defense official materials reviewed did not provide a clear Model Context Protocol (MCP), agent-to-agent (A2A), or agent-to-agent security control claim.
No quoted source text is recorded for this claim.
Cisco AI Defense official materials reviewed did not provide a clear non-human identity or service-account lifecycle security claim.
No quoted source text is recorded for this claim.
Cisco claims standards-aligned findings, risk scoring, policy-driven gating, and compliance support across model, agent, and Model Context Protocol (MCP) workflows.
Findings and protections map to leading frameworks, including MITRE ATLAS, OWASP Top 10 for LLMs, and NIST AI-RMF
Cisco claims algorithmic red teaming for models and applications across more than 200 threat subcategories with model-specific guardrail generation.
Automatically test models and applications at AI scale with Cisco algorithmic red teaming.
Cisco claims scanning of model files, repositories, datasets, Model Context Protocol (MCP) servers, and tools for malicious code, poisoning, backdoors, and compromised components before production.
Scan AI model files, repositories, and MCP servers for hidden risks before they are introduced into development or production.
Cisco claims real-time Model Context Protocol (MCP) enforcement, tool allowlists and blocklists, and runtime policies across prompts, responses, agent actions, and tool calls.
MCP runtime enforcement: Monitor and enforce safe behavior across MCP requests and responses connecting LLMs, agents, and tools.
Cisco claims runtime policies that detect and block unsafe agent actions, unauthorized tool usage, and privilege escalation.
Prevent harmful or unintended agent actions by enforcing policies on tool invocation, privilege levels, and action chains.
Cisco AI Defense materials reviewed did not provide a public claim for governing coding-agent commands, developer-workstation files or networks, integrated development environment (IDE) extensions, skills, hooks, secrets, or package actions.
No quoted source text is recorded for this claim.
Cisco AI Defense 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.
Cisco documents AI Defense as a software subscription offered in packages, including an AI Validation package, with ordering quantities and software subscription terms defined in official offer and ordering materials.
If You purchase an AI Defense subscription package with AI Validation