Is DeepInspect a replacement for Cisco AI Defense or these alternatives?

These tools are complementary rather than substitutes. Cisco AI Defense, CalypsoAI, Lakera, and Protect AI test models, scan artifacts, and inspect content for threats, while DeepInspect authorizes the caller on each request and produces an independent per-decision audit record. Many regulated teams run a validation or content-security tool for those jobs and put DeepInspect underneath it for identity-bound authorization and audit evidence.

What is the difference between model validation and identity-bound enforcement?

Model validation stress-tests a model before deployment and scores how it behaves under adversarial pressure. Identity-bound enforcement decides, at runtime, whether this authenticated caller in this role is permitted to send this class of data to this model, and records that ruling. One measures the model in advance while the other checks the caller's entitlement on the live call and writes the evidence.

Which alternative is strongest for prompt injection?

Lakera leans hardest into prompt-injection defense, with a detection corpus fed by more than 80 million adversarial prompts from its Gandalf game. CalypsoAI and Cisco AI Defense also inspect live traffic for injection. If prompt injection is your single largest concern, Lakera under Check Point is the closest fit among these three.

Do any of these satisfy EU AI Act Article 12 logging?

Red-team scores, model scans, and content-firewall hits are not the same as the automatic event logging Article 12 requires over a high-risk system's lifetime. That obligation calls for timestamped records with inputs and the identities involved, produced independently of the application. A per-decision audit record bound to identity is designed to meet it, while an evaluation report is not.

Does DeepInspect work in front of self-hosted models?

DeepInspect is model-agnostic and sits in front of any HTTP-based LLM endpoint, including self-hosted Llama or Mistral deployments and on-prem inference. The identity, policy, and audit behavior stays the same regardless of which provider or model runs behind the proxy.

Robust Intelligence Alternatives: Three Options and Where Runtime Authorization Fits

In October 2024, Cisco paid a reported $400 million for the company that shipped the first AI Firewall, and that firewall plus its algorithmic validation engine became the core of Cisco AI Defense. At RSA 2026 Cisco packaged the validation motion into an Explorer Edition, a self-service version of the same engine that runs multi-turn adversarial tests against models and agentic workflows for Global 2000 customers. The technology that pioneered pre-deployment AI red-teaming now ships inside Cisco's security fabric.

Teams shortlist alternatives for two reasons. Some want the model-security and validation capability without standardizing on Cisco for network and security. Others want a different control point, an authorization decision on the live call rather than content inspection and pre-launch testing. The three options below cover the first group honestly, and the closing sections cover the second. Each of these vendors was acquired in the last two years, so I have named the parent company in every case, because for a regulated buyer the owner of your audit record is a due-diligence question, not a footnote.

What separates one alternative from the next

Cisco AI Defense pairs pre-deployment validation with a runtime firewall that reads prompts and responses for injection and data exposure. When you replace or sit beside it, four questions sort the field.

Placement decides what a tool can see. A model scanner reads artifacts in your registry, a runtime firewall reads live traffic, and an inline proxy reads the request before it leaves your environment. Scope comes second: pre-deployment testing, runtime protection, or both. Identity is the third axis, and most of these tools skip it, since resolving which caller under which role made a specific request is harder than scanning the text of it. The record is the fourth, because scoring a model produces a different output from a per-decision log an auditor can pull months later.

CalypsoAI, now part of F5

CalypsoAI started in 2018 on national-security use cases and built its Inference Platform to secure model calls at runtime. It runs defensive scanners for prompt injection and data leakage in front of live traffic, and its Inference Red-Team fires thousands of adversarial prompts at a model to surface weaknesses in hours. F5 acquired the company in September 2025 to fold inference-layer security into its application and API portfolio, so a buyer evaluating it in 2026 is evaluating part of the F5 stack.

Pick CalypsoAI when a security team wants runtime content scanning and automated red-teaming in one product, and an F5 footprint makes the adjacency useful. It overlaps Cisco AI Defense on the runtime-firewall function and on offensive testing. The decision both make is about content: is this prompt or response dangerous. I walk that distinction in more depth in DeepInspect vs Calypso AI.

Lakera, now part of Check Point

Lakera was founded in 2021 in Zurich and built its name on Gandalf, an online game where players try to trick an AI into revealing a password. Gandalf drew more than a million players and generated over 80 million adversarial prompts, which fed the data behind Lakera Guard, its prompt-injection and data-leakage defense. Check Point acquired Lakera for a reported $300 million, closing in the fourth quarter of 2025, and made it the foundation of a Global Centre of Excellence for AI Security in Zurich.

Pick Lakera when prompt injection is the threat you most need covered and you want the depth that comes from one of the largest public corpora of attack prompts. Its focus is the content of the exchange, catching the manipulation attempt in the prompt or the leak in the response. That is a real and hard problem. It answers a separate question from whether the caller was entitled to make the request.

Protect AI, now part of Palo Alto Networks

Protect AI built its reputation on the machine-learning supply chain: scanning model artifacts for malicious code, tracking ML lineage, and open tools like ModelScan that check a serialized model before it loads. Palo Alto Networks completed its acquisition on July 22, 2025 for more than $500 million, and Protect AI now anchors Prisma AIRS, which combines model scanning, posture management, AI red-teaming, runtime protection, and agent security.

Pick Protect AI when your exposure lives in the model artifacts and the pipeline that ships them, and when a Palo Alto footprint makes Prisma AIRS a natural home. Its center of gravity is the supply chain and the model itself rather than the identity of the caller at runtime. For a team whose first worry is a poisoned model or an unvetted artifact pulled from Hugging Face at 2am, that emphasis fits.

Where identity-bound enforcement fits

The three options above, and Cisco AI Defense itself, answer content, model, and supply-chain questions. They scan artifacts, test models, and inspect live prompts. None was built to answer the authorization question at the moment a request leaves your environment: is this authenticated caller, in this role, permitted to send this prompt to this model right now, and where is the record that says the ruling was made.

That gap matters because authentication is not authorization. A logged-in engineer, or an agent holding a valid token, can send data to a model it should never touch, and a clean prompt with no injection passes every content scanner on the way. The post-authentication gap is the space between "this caller is who they claim to be" and "this request is allowed." Content inspection and model validation leave it open, because they read the payload and the model rather than the caller's entitlement.

For a regulated program, that authorization decision and its record are often what a market surveillance authority or an internal auditor asks to see. EU AI Act Article 12 requires automatic logging over the lifetime of a high-risk system, with timestamps, inputs, and the identities involved. A red-team score does not satisfy that. A per-decision record bound to identity does.

DeepInspect

This is the gap DeepInspect closes. DeepInspect is a stateless proxy that sits inline between your authenticated users and agents and the LLM endpoints they call. For every request it evaluates who is asking, what role they hold, what data classification is in play, and organizational policy, then makes a pass or block decision before the traffic reaches the model. Enforcement runs under 50 milliseconds in internal testing, invisible against the 500 milliseconds to several seconds a model takes to respond, so there is no throughput argument against putting it inline.

It is model-agnostic, sitting in front of OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or a self-hosted model on the same terms. And it commits a per-decision audit record before the response returns, written by something other than the application that made the call, which is what turns a log into evidence. DeepInspect does not replace model scanning, red-teaming, or content firewalls; run CalypsoAI, Lakera, Protect AI, or Cisco AI Defense for those jobs and put identity-bound authorization and an independent audit trail underneath them. If you want the wider control-point picture, the best AI gateway guide for 2026 lays out where each layer sits. With the EU AI Act high-risk obligations landing August 2, 2026, that record is the part most teams have not built. Book a technical deep dive at deepinspect.ai.