DeepInspect vs Robust Intelligence (Cisco AI Defense): Runtime Protection and Identity-Bound Enforcement

The AI Firewall that defined the category is now part of Cisco AI Defense. Cisco acquired the company behind it in October 2024 for a reported $400 million, and its technology became the foundation of a platform with four parts: AI Access for visibility into who uses which AI, AI Cloud Visibility for discovery, AI Model and Application Validation for pre-deployment algorithmic red-teaming, and AI Runtime Protection, the firewall that inspects live traffic for prompt injection and sensitive-data exposure.

The validation engine is the heritage worth naming. Algorithmic red-teaming, automated model assessment, and risk scoring aligned to OWASP and MITRE ATLAS were the original contribution, and Cisco now offers a self-service version of that same engine trusted by Global 2000 customers.

Teams comparing this platform to DeepInspect are usually weighing a validation-and-firewall stack against an identity-bound policy layer. Both reach into live AI traffic, which makes the comparison worth doing with care. I want to walk what each one inspects, then show where the overlap ends.

TL;DR

Cisco AI Defense, built on the AI Firewall and validation engine acquired from the pioneer of the category, pairs pre-deployment model validation with runtime protection against prompt injection and data leakage, integrated into Cisco's security fabric. DeepInspect sits inline between authenticated callers and model providers, decides whether each request proceeds based on identity and data, and commits a per-decision audit record. One inspects content for threats. The other authorizes the caller and records the ruling.

Cisco AI Defense: validation and the runtime firewall

The platform splits into two motions. Validation runs before deployment: the engine generates adversarial inputs, scores a model against attack categories, and produces a risk posture measured against OWASP and MITRE ATLAS, so a team knows how a model behaves under pressure before it ships. Runtime protection runs after: the AI Firewall inspects live prompts and responses for prompt injection, jailbreaks, and sensitive-data exposure, blocking traffic that trips a policy.

Integration into Cisco's stack is the differentiator worth naming. For an organization already running Cisco for network and security, folding AI protection into the same fabric means AI traffic inspection sits alongside the controls the security team already operates, with one console and one set of telemetry.

The firewall inspects content. It reads a prompt or a response and decides whether either carries an attack or a data-exposure pattern. That is a content-threat question, and it is a different question from whether the specific caller was authorized to make the call.

Content threats versus caller authority

DeepInspect is a stateless proxy sitting between authenticated users or agents and any LLM endpoint. Every call traverses it, and the decision happens before the provider sees the request.

The primary key is the difference. The AI Firewall asks whether the content of an exchange is dangerous. DeepInspect asks whether this authenticated identity, holding this role, may send this class of data to this model. A prompt with clean content and no injection can still violate policy, because the caller had no entitlement to the data it carries, and that case sits outside content inspection. Identity-first decisions are described in identity-aware AI gateway architecture.

The audit record is the second difference. The firewall produces security telemetry for an operations team to monitor and investigate. DeepInspect writes a structured decision record for every request, keyed to identity and committed to storage the application cannot alter, built for an auditor rather than an analyst. The distinction between telemetry and an audit system of record is in signed audit logs for AI requests.

Feature comparison

| Capability | Cisco AI Defense | DeepInspect | |---|---|---| | Runtime content-threat inspection | Yes, a core strength | Content checks available | | Pre-deployment model validation | Yes, core | No | | Algorithmic red-teaming | Yes, core | No | | Integration with a network security fabric | Yes, Cisco | No | | Identity-bound per-request policy | Limited | Core | | Decision keyed to role and data class | Limited | Core | | Application-independent audit record | Security telemetry | Core, tamper-evident | | Inline blocking of a request | Yes, on content | Yes, on identity and policy | | Model-agnostic across HTTP LLM endpoints | Yes | Yes | | Primary buyer | Security operations | Security and compliance |

The honest reading is that both reach live traffic and split on what they evaluate. Cisco AI Defense inspects content for threats and validates models before launch. DeepInspect authorizes by identity and produces the independent record.

Where Cisco AI Defense is the better answer

If the requirement is validating models before deployment and inspecting live traffic for injection and data exposure at network scale, inside a stack you already run, Cisco AI Defense has depth and reach I would not try to match with a policy proxy. Algorithmic validation plus a runtime firewall integrated into an existing security fabric is a strong fit for a Cisco shop.

I will state the part a committee might cut: if you are standardized on Cisco and your exposure is dominated by content threats, look at Cisco AI Defense before you look at us, because that is the surface it was built for and the fabric it already lives in.

Where the gap opens

Two requirements move a team past what content inspection covers.

The first is authorization on clean traffic. An authenticated employee sending regulated data into an approved model, with no injection and no attack pattern, passes a content firewall. An identity-bound layer refuses it, because the employee's role carries no entitlement to move that data. This is the post-authentication gap, and content inspection does not close it.

The second is evidence independence, and it interacts with vendor concentration. Security telemetry records what the firewall observed. A regulator under the EU AI Act asks for a per-decision record of who was authorized, under which policy, on storage the deploying application never controlled, and some teams prefer that record to sit outside the same vendor that supplies their network. The retention and independence requirements are in EU AI Act Article 19 logging.

Pick Cisco AI Defense if

• You are standardized on Cisco for network and security.
• Your exposure is dominated by prompt injection and data-exposure threats.
• Pre-deployment model validation and algorithmic red-teaming are stated needs.
• You want AI traffic inspection inside your existing security fabric.
• Per-decision authorization evidence is already handled elsewhere.

Pick DeepInspect if

• Your exposure includes who may call the model with which data.
• An auditor or enterprise customer wants per-decision records tied to an identity.
• You want your audit evidence independent of your network vendor.
• Policy must differ by role and be enforced independently of application releases.
• You operate under the EU AI Act, DORA, HIPAA, or a comparable mandate.

DeepInspect

This is exactly what DeepInspect does. DeepInspect sits inline between your users or agents and the LLM APIs they call. For every request and response, it evaluates identity, data classification, model authorization, and organizational policy, then makes a pass or block decision before the traffic reaches the model.

The record is the second half of the product. Every decision writes a structured entry containing the authenticated identity, the resolved role, the data classes found in the prompt, the policy version in force, and the outcome. That entry commits before the response returns, on storage the application cannot write to, which is what lets it serve as evidence rather than as security telemetry. Enforcement adds under 50 ms in internal testing against inference times of 500 ms to several seconds. A validation-and-firewall stack and an identity-bound enforcement layer address different exposures and can run on the same traffic.

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