Cisco AI Defense is Cisco's product for securing enterprise AI. Cisco announced it on 15 January 2025 as one product for two problems: building and deploying secure AI applications, and controlling how employees use AI applications. Teams searching for Cisco AI Defense alternatives usually do not run Cisco's security stack, want a narrower tool, or need controls on what an agent does, not only on what it says. This guide covers what AI Defense includes, where it is strong, and how to choose between the categories. Other vendor guides are on the comparison hub.

What Cisco AI Defense covers (as of 17 October 2026)

Cisco's launch announcement names these capabilities:

  • Discovering AI. Detecting shadow and sanctioned AI applications across public and private clouds.
  • Model validation. Automated testing of models for safety and security issues, which Cisco calls an algorithmic red team. It recommends guardrails based on the results.
  • Runtime security. Continuous protection against prompt injection, denial of service and sensitive data leakage.
  • Securing access to AI applications. Visibility of the AI apps employees use, policies that restrict access to unsanctioned AI tools, and data and threat protection.

The announcement also says AI Defense uses threat intelligence from Cisco Talos, sends enriched alerts to Splunk customers, and is built into Cisco Security Cloud.

The AI Defense user guide, last updated 14 October 2026 when we checked, adds detail. Its sections cover AI cloud discovery and visibility, AI model and application validation, AI runtime protection, AI supply chain security, AI access protection, network fabric-level AI guardrails and hybrid deployment. The guide describes three ways to set up runtime protection: gateway interception, protection through Cisco Multicloud Defense, and an inspection API. It also covers standard and adaptive red teaming, discovery through an AWS integration, a ServiceNow AI Control Tower integration, and log export to a SIEM through OpenTelemetry.

Cisco also publishes an open-source MCP Scanner under the Apache-2.0 licence. Its README says it scans MCP servers, tools, prompts and resources with three main engines (YARA rules, LLM-based analysis and the AI Defense inspect API) plus further analyzers. It runs as a command-line tool or as a REST API server.

One more piece of context: Astrix Security, an AI agent and non-human identity security vendor, states on its site that it is now part of Cisco and that Cisco is bringing Astrix capabilities into its products over time.

Where Cisco AI Defense is strong

  • Network position. Cisco can enforce AI policy at network and cloud enforcement points it already runs. For a Cisco-heavy enterprise, that coverage needs no change to each application.
  • Threat intelligence. Talos research feeds the detection models.
  • Operations fit. Splunk alerting and OpenTelemetry log export put findings where a security operations team already works.
  • Testing before production. Validation and red teaming find problems before an application ships, and the results feed guardrail settings.
  • Open tooling for MCP. The MCP Scanner gives any team a free way to check MCP servers before they connect them. Scanning is one gate; the MCP security guide covers the runtime controls that follow.

Why teams evaluate alternatives

  1. No Cisco security estate. The network advantage assumes AI traffic already crosses Cisco enforcement points.
  2. A smaller scope. A team that only needs prompt and response guardrails in one application may prefer a guardrail API.
  3. Decisions on agent actions. Runtime protection that inspects prompts and responses answers "is this content safe". Some teams also need "may this agent call this tool with these arguments now", with a person approving some calls.
  4. Evidence a third party checks. Some auditors want exported evidence they can verify offline.

Alternatives by category

What you need Category Examples covered on this site
Network-level and platform-wide AI security AI security platform Prisma AIRS, Cisco AI Defense
Guardrails added to your own application through an API Guardrail API Pangea, Lakera
Agent discovery, red teaming and runtime protection from an agent-native vendor Agent-native security Lasso Security, Zenity
Agent identity, tool-call policy, approvals and signed evidence Agent control plane Praesidia
Non-human identity inventory and posture NHI security Astrix Security

Our post on AI guardrails versus LLM firewalls explains the difference between inspecting content in band and enforcing a decision on an action.

Where Praesidia fits

Praesidia is our product. It is an AI agent control plane. It is not a network security product, and it does not do Cisco's network or cloud-level discovery: its inventory comes from five sources only (OpenTelemetry GenAI traces sent to it, agents and MCP servers registered through the API or SDK, unregistered callers seen reaching a registered agent, the tool inventory of registered MCP servers, and scans of connected GitHub and GitLab repositories where that is enabled). What it does:

  • Identity per agent. Each registered app, agent and MCP server gets its own credentials for authentication and attribution.
  • Tool access rules. Rules are set per agent and scoped to one MCP server or all of them. Without a matching allow rule, a governed tool call is denied.
  • Approvals. A step-up rule holds an MCP tool call as a durable approval request that expires if nobody answers.
  • Guardrails in the model gateway. Rule matches can block, warn, redact or replace; ML and LLM guardrails block or warn.
  • Signed evidence. Praesidia records the decisions it makes in an append-only, hash-chained audit trail, signed under the default configuration; an operator can turn signing off. A signed record shows it was not changed afterwards. It does not show that every action was captured. Calls that do not pass through Praesidia are not recorded.

On red teaming, Praesidia is narrower than Cisco. Its red-team probes check organization-wide input guardrails and intent rules; they do not call the production target or test its agent-specific guardrails. Praesidia also does not currently publish a SOC 2 report or an ISO/IEC 27001 certificate.

A common pattern is to keep network-level AI security where the network vendor is strong, and add a control plane for agent identity and tool-call decisions. See MCP server governance for how tool-call rules work.

Questions to ask in an evaluation

  1. Which runtime option will you deploy? Gateway interception, Multicloud Defense and the inspection API see different traffic. Map each to your agent paths.
  2. What does a policy see? Ask whether runtime policy can read a tool call's name and arguments, or only prompt and response text.
  3. Who approves a risky action? Ask whether a call can wait for a person, and what is recorded when it does.
  4. What leaves your environment? Hybrid deployment and the inspection API move different data. Ask for the data flow of the mode you will run.
  5. What does the licence include? Confirm which of the documented capabilities are in your quote.

Common questions

What is Cisco AI Defense? Cisco AI Defense is Cisco's AI security product, announced on 15 January 2025. It covers AI discovery, model validation and red teaming, runtime protection and control of employee access to AI applications.

Is the Cisco MCP Scanner part of AI Defense? It is a separate open-source project under Apache-2.0 from the cisco-ai-defense GitHub organization. It can use the AI Defense inspect API as one of its three scanning engines, together with YARA rules and LLM-based analysis.

Does Praesidia replace Cisco AI Defense? No. Praesidia does not inspect network traffic or discover AI across cloud accounts. It governs the agents and MCP tool calls routed through it and produces signed evidence. Teams often run the two kinds of control together.

What is the difference between runtime protection and tool-call authorization? Runtime protection inspects prompts and responses for threats such as prompt injection and data leakage. Tool-call authorization decides whether a specific agent may run a specific tool with specific arguments, and can hold the call for a person.

Sources (as of 17 October 2026)