Is Cisco AI Defense a competitor or a complement to DeepInspect?

Both, depending on scope. They overlap where each inspects live AI traffic, and they diverge on the core decision: AI Defense classifies threats and unsafe content, DeepInspect authorizes identities and records decisions. Many mature programs run threat guardrails and identity-bound enforcement together, because catching an attack and proving who was permitted to do what are different obligations.

Does DeepInspect detect prompt injection?

DeepInspect's primary control is authorization and audit, not threat classification. It reduces prompt-injection blast radius by constraining what an authenticated caller is permitted to do and by recording every decision, so an injected instruction still cannot exceed the caller's authorized scope. For adversarial-content detection specifically, a guardrail layer such as AI Defense is the purpose-built tool, and the two compose.

Which one helps with regulatory audit?

DeepInspect is built around the per-decision, identity-bound record that regulations like EU AI Act Article 12 require. AI Defense produces security telemetry oriented toward threat detection. If the requirement is reconstructing what a specific user asked a specific model under which policy, verify whether a threat-detection platform retains that record at the needed granularity and independence, which is DeepInspect's design center.

Can DeepInspect govern MCP and agent traffic?

DeepInspect governs the HTTP calls agents make to LLM endpoints and records who authorized each. Attack vectors that run outside HTTP AI traffic, such as local process execution or STDIO transport, sit outside its enforcement boundary. Where agent behavior is the concern, pairing identity-bound HTTP enforcement with a behavior-focused guardrail covers more of the surface than

DeepInspect vs Cisco AI Defense: Threat Guardrails and Identity-Bound Enforcement

In February 2026 Cisco expanded AI Defense for what it called the agentic era, extending runtime protection to agent actions and MCP traffic and pairing it with AI-aware SASE. AI Defense blocks adversarial attacks and unsafe responses in real time, covers prompt injection, model denial of service, off-topic and code-detection risks, and detects agent-specific threats such as memory poisoning, tool misuse, privilege escalation, and intent hijacking. It integrates with NVIDIA NeMo Guardrails and, in VPC and AI POD deployments, keeps application and model data inside the customer environment with only management metadata sent to Cisco. That is a capable platform. It also answers a different question than DeepInspect, and a team evaluating both should see the line clearly.

TL;DR

Cisco AI Defense is a threat-and-content guardrail layer with model validation, strongest when you want adversarial-attack and unsafe-behavior detection integrated into Cisco's security fabric. DeepInspect is an identity-bound policy gateway that decides who may do what with which data and produces a signed per-decision audit record, strongest when authorization and independent audit evidence are the requirement.

Cisco AI Defense: where it sits

AI Defense works along two axes. Validation, built on the model-testing technology Cisco acquired in 2024, probes models through algorithmic red teaming to find weaknesses before deployment. Runtime protection then applies guardrails to live traffic, classifying and blocking adversarial prompts, harmful responses, and unsafe agent actions, including tool calls made over MCP.

The organizing idea is the threat model. AI Defense asks whether a given request or response is an attack or is unsafe, and blocks it when the classifier says so. That is a strong fit for the prompt-injection, jailbreak, and agent-misbehavior problem space, and integrating with NeMo Guardrails gives teams a modular way to run those checks in production.

DeepInspect: where it sits

DeepInspect asks a different question: not "is this request an attack" but "is this authenticated caller permitted to make this request, with this data, against this model, right now." It is a stateless proxy at the AI request boundary that evaluates identity, role, data classification, model authorization, and organizational policy, then returns pass, block, or redact before the model sees the traffic. The decision is deterministic and fails closed.

Its second job is the record. For every decision, DeepInspect commits a signed per-decision audit record bound to the identity that made the call, written independent of the application. That is the audit-ready evidence a regulator or internal auditor asks for, and it is a different artifact than a threat-detection alert.

Feature comparison

On the core question: AI Defense classifies threats and unsafe content; DeepInspect authorizes identities against policy. A guardrail decides whether content is dangerous; an authorization decision decides whether this caller is allowed.

On determinism: identity-and-policy authorization is deterministic, a role either may reach a model with a data class or it may not. Threat classification is probabilistic by nature, because it is judging whether input is adversarial. Both have a place; they are not the same kind of control.

On audit: DeepInspect's per-decision record is bound to identity and written independent of the application. Confirm what per-request evidence AI Defense retains and whether it is oriented toward security telemetry or toward regulatory record-keeping.

On agent coverage: AI Defense inspects agent actions and MCP tool calls for unsafe behavior. DeepInspect governs the agent's HTTP calls to LLMs and records who authorized each, which is the action-lineage side of agent security.

Pick Cisco AI Defense if

Your priority is adversarial-attack and unsafe-behavior detection: prompt injection, jailbreaks, model DoS, and agent misbehavior. You want model validation and red-teaming before deployment. You run Cisco's security fabric and value AI controls that integrate with your existing SASE and networking, and you want the NeMo Guardrails integration for in-production checks.

Pick DeepInspect if

Your requirement is identity-bound authorization on AI calls and a signed per-decision audit record you can hand a regulator. You need one enforcement policy across models from any provider, independent of your network vendor. You are closing the post-authentication gap, where an authenticated user can still send data through a prompt they should not, which a threat classifier is not designed to catch because the request is not an attack, it is a permission violation.

Pricing approach

Both are sold through enterprise sales rather than public tiers. Cisco prices AI Defense within its security portfolio, and buyers often acquire it alongside other Cisco security products. DeepInspect is scoped to the AI enforcement and audit layer and priced against that footprint. The framing question is whether you are extending a Cisco security estate or adding a dedicated, vendor-neutral enforcement and audit layer.