Cisco AI Defense vs Trussed AI for Regulated AI

Three capabilities that are often compared as one

Buyers evaluating AI security products usually meet three different capabilities under similar labels. The first is pre-deployment validation: testing applications and models for security and safety weaknesses before they reach production. The second is network-level or traffic-level security, which inspects and controls AI traffic as it crosses a boundary. The third is runtime policy enforcement inside the AI execution path, where each model interaction and tool call is evaluated against policy and acted on.

These answer different questions. Validation tells you how a system behaves under attack conditions at a point in time. Traffic-level controls tell you what crossed a boundary. In-path enforcement determines what a specific interaction or tool call was allowed to do, and records it. A regulated enterprise often needs more than one of these, so the useful comparison is which question each product answers.

Cisco AI Defense is publicly described as spanning validation and runtime protection. Earlier listings used the names Cisco AI Validation, described as assessing AI applications and models for security and safety vulnerabilities, and Cisco AI Runtime, described as securing GenAI applications against prompt injection, sensitive data loss and compliance concerns. The current product name is Cisco AI Defense, so older material using the previous names should be read with that in mind.

Comparison against defined criteria

The table summarizes how each product's publicly described scope lines up with the criteria a regulated buyer should apply. Where a Cisco cell describes a function rather than a mechanism, the mechanism is a question for the vendor.

• Lifecycle position: Cisco AI Defense: validation of applications and models, plus runtime protection. Trussed: runtime control in the execution path. It is not a pre-deployment testing or red-teaming product.
• Threats addressed: Cisco AI Defense: prompt injection, sensitive data loss and compliance concerns. Trussed: policy enforcement across models, agents, tools, MCP servers, AI sites and coding assistants.
• Actions on an interaction: Cisco AI Defense: protection functions are described, but the specific action set is a vendor question. Trussed: allow, redact, block, rate limit, escalate for human approval.
• Scope of control: Cisco AI Defense: confirm whether control reaches individual model interactions and tool calls. Trussed: individual interactions and tool calls.
• Evidence output: Cisco AI Defense: ask for sample artifacts and export formats. Trussed: continuous audit-ready evidence generated by enforcement, mapped to HIPAA, NAIC, NYDFS, EU AI Act and NIST.
• Deployment and residency: Cisco AI Defense: ask for deployment models, residency and retention terms. Trussed: self-managed deployment in the customer cloud, with configurable retention and data residency.

Enforcement point decides what can be controlled

The enforcement point determines which controls are even possible. A control applied before deployment cannot stop a prompt injection that arrives in production. A control at the traffic layer sees requests and responses but may have limited visibility into what an agent then does with a tool. A control in the execution path can act on the individual interaction or tool call at the moment it occurs.

This matters most for agentic workloads, where the consequential step is often a tool call, such as a query against a record system or a write to an external service. Asking whether a product detects and alerts, or enforces inline, separates monitoring from control. Detection after the fact lets the action complete. Enforcement at runtime, not review after the fact, changes the outcome.

Trussed is built for this point in the path. It enforces policy on individual interactions and tool calls across models, agents, tools, MCP servers, AI sites and coding assistants, with actions to allow, redact, block, rate limit or escalate for human approval. It also supplies code provenance: the origin, licence and security context of code. It has run in continuous production in a regulated enterprise for more than 18 months, with 99.95% uptime on live production traffic and more than 100 models under one policy plane.

Trussed does not test or red-team models before deployment. A team that needs that function would pair a validation capability with runtime enforcement rather than expect one product to cover both.

Evidence is what a regulator actually reviews

Preventing a bad outcome and proving what happened are separate requirements. A regulator or auditor will ask what the AI system was permitted to do, what it did, and who approved exceptions. Answering requires records tied to individual decisions, retained for a defined period, stored in a known location and exportable for review. Operational logs that were designed for debugging rarely meet that bar.

Framework language points the same way. NIST AI RMF MANAGE 1.2 states that treatment of documented AI risks is prioritized based on impact, likelihood, and available resources or methods. For a security team, that implies the ability to show which controls apply to which risks and what they did in practice. Regulation (EU) 2024/1689, the EU AI Act, adopted 13 June 2024, is another reference point. As of October 2026, counsel should confirm current applicability and any amendments from the official text.

Trussed generates audit-ready evidence as a direct product of enforcement, so each allow, redact, block, rate limit or escalation decision produces a record. That evidence is mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST, and the platform generates more than 30 GB of audit evidence per day in production. For any product, including Cisco AI Defense, the test is the same: obtain a sample artifact and have compliance or audit stakeholders review it before shortlisting.

Deployment model, data residency and complementary use

Deployment model affects whether sensitive prompts, responses and evidence leave the environment. SaaS delivery is operationally simple but moves data handling into the vendor's terms. Customer-managed deployment keeps traffic and evidence inside the customer's own cloud, which can simplify residency and retention decisions for regulated workloads. Hybrid models sit between the two. Each option needs written terms for residency, retention and data handling.

Trussed supports self-managed deployment in the customer cloud, with configurable retention and data residency. It holds SOC 2 Type II, HIPAA, GDPR, FERPA, ISO 27001 and NIST AI RMF credentials, and serves customers in four regulated verticals: insurance, pharma, healthcare and education.

The two products are not necessarily alternatives. If Cisco AI Defense is used for validation of applications and models, it addresses a different lifecycle stage from in-path runtime enforcement. Teams comparing Cisco AI Defense alternatives should first decide which function they are replacing. Many will find the real gap is not validation, but control and evidence at the moment of each interaction.