Securiti AI Alternatives for LLM Request Enforcement
Securiti provides data-security capabilities for AI use, including data controls and visibility across enterprise AI activity. DeepInspect evaluates policy on each HTTP request between an authenticated caller and an LLM API, creating signed audit evidence per decision. Buyers should compare data-security governance with request-level authorization on the API path.
TL;DR
- Securiti focuses on data security and governance for enterprise AI use, including controls around sensitive data and AI activity.
- DeepInspect acts on HTTP traffic between authenticated callers and LLM APIs, where it evaluates identity, route, content classification, and policy.
- Securiti fits data-security teams mapping AI use to their sensitive-data program.
- DeepInspect fits teams that need an authorization decision and signed audit record for each model API request.
Securiti starts with data control
Securiti presents its AI Security offering around protecting data used by AI systems. Its published material connects data discovery and classification to AI applications and agents. It also connects access controls and governance with those applications. That orientation matters in an enterprise where the first hard question is where sensitive information lives and which systems can access it. The next question is how an AI use case changes that exposure.
The data-control perspective gives a CISO a way to bring AI use into an existing privacy and data-security program. A customer-data platform, a document repository, an AI application, and its model route can be examined through a common data policy. Securiti also publishes material on data controls for generative AI, which is useful reading for a buyer defining data handling requirements before deployment.
That work establishes the conditions around data use. It can help a team identify the information that requires protection and the governance process that should apply to it. A separate review is needed at the moment an LLM request is sent, because that is where an authenticated caller presents a specific action to a model endpoint.
The AI request boundary
DeepInspect has a narrower operational point and a more specific decision. It is a stateless proxy on the HTTP path between an authenticated user or agent and an LLM API. Before the model receives a prompt, DeepInspect resolves identity, evaluates a per-role and per-route policy, classifies request content, and makes an allow, redact, or block decision.
Each decision generates a signed, tamper-evident audit record with the caller identity, policy version, classification, outcome, and timestamp. That matters when a review asks about one conversation turn or one agent request rather than a data asset in the abstract. The policy is enforceable at the request boundary. Prompt-level classification and AI traffic as a first-class data channel explain why an LLM request needs its own inspection point.
The practical difference appears in the evidence each control produces. A data-security program can show how information is categorized and governed. A request policy point can show what happened when a caller attempted a model action. The two records answer different questions during an architecture review or compliance investigation.
Data governance and request authorization are separate layers
A fair evaluation separates the data-security layer from the LLM request layer.
- Securiti's center of gravity: Securiti brings AI use into a data discovery and governance program. The buyer starts with sensitive data and access policy across enterprise systems.
- DeepInspect's center of gravity: DeepInspect evaluates an authenticated HTTP request headed to an LLM API. The buyer starts with an identity attempting a particular model action.
- Decision evidence: A data inventory shows what data exists and where policy applies. A signed per-decision audit record shows the authorization result for a specific LLM request.
- Operational owner: Securiti often aligns with privacy teams and data-security teams. DeepInspect aligns with platform, application security, and compliance teams responsible for model API access.
Neither layer removes the need for the other. A data program can identify sensitive material before a model call. The runtime policy point then determines whether a caller may send that material through the configured LLM route. The request record supplies evidence after the decision.
This separation also prevents a common buying mistake. A team may confirm that sensitive data has been classified and still lack a control that evaluates the caller and route at request time. Conversely, a team may record every request decision while lacking the broader data context needed to define which content requires special handling. The comparison should therefore map each requirement to the layer that can enforce or document it.
Buyer fit for Securiti AI alternatives
Choose Securiti when AI data discovery, classification, access governance, and privacy obligations need to become part of a shared data-security program. That buyer needs visibility across repositories and AI use cases, with data controls that connect to the systems of record already under governance.
Choose DeepInspect when an enterprise is exposing LLM APIs to employees and internal services. Agents are another caller type. The enterprise needs a fail-closed policy decision for every HTTP request. The buyer should be able to answer a Principal Engineer's direct question: which identity was permitted to use this model route, and what record proves it? That requirement sits close to EU AI Act record-keeping, where traceability depends on decision-level evidence.
The selection can also depend on where the unresolved control sits. If the gap is an incomplete view of sensitive information across enterprise repositories, Securiti is the closer fit. If the gap is the absence of an enforceable decision immediately before an LLM receives a request, DeepInspect addresses that boundary.
DeepInspect
DeepInspect complements data-security governance with an inline enforcement point for HTTP AI traffic. It does not replace data discovery or privacy workflows. It receives LLM API requests after a caller is authenticated, applies identity-aware policy at the request boundary, and writes the decision to a signed audit record independent of the application.
For a Securiti buyer, the practical test is simple: trace a sensitive prompt through the configured architecture and identify the component that makes the authorization decision before the LLM sees it. DeepInspect provides that request-level control and evidence path. Book a technical deep dive at deepinspect.ai.