Answers before you put AI in front of real data

Privacy Gateway is a control layer that sits between your company and the LLM. Before a prompt reaches ChatGPT, Claude, Gemini, or another model, the gateway inspects the request, detects sensitive entities, applies policy, and transforms what should not travel.

The goal is not to block AI usage. The goal is to let teams use AI with useful context while reducing unnecessary exposure of names, emails, contracts, financial values, customer records, and other sensitive details.

Most teams adopt generative AI faster than they build privacy controls around it. A normal prompt can include a customer's name, an email address, a contract ID, pricing notes, health information, legal context, or internal financial details.

Without a gateway, that information may leave the workspace before anyone has classified it, masked it, routed it, or recorded what happened. Privacy Gateway gives the organization a control point before the prompt reaches the model.

It is designed for companies that want AI to work with real business context but cannot allow sensitive information to move without rules. Typical users include operations, legal, finance, customer support, sales, healthcare, banking, and regulated teams.

It is especially useful when employees are already using AI informally and leadership needs visibility, policy enforcement, and a safer path to scale adoption.

The gateway can be configured to detect personal data, emails, phone numbers, addresses, account IDs, contract references, financial values, customer notes, regulated context, and business-confidential terms.

The exact coverage depends on the policy design. Some values should be masked, some pseudonymized, some routed to a safer model, and some blocked entirely. We define those rules around your use cases instead of applying a generic filter to everything.

Yes. The gateway is model-agnostic in concept: it can sit before external LLMs such as ChatGPT, Claude, Gemini, or a private/internal model route.

That matters because most companies do not use only one model forever. The control layer should stay consistent even when the preferred model, provider, deployment option, or routing policy changes.

Yes. In most cases, the safest first step is one contained workflow: for example customer-support summaries, contract-risk review, sales-call notes, or internal knowledge search.

We define the sensitive data classes, the model route, the masking rules, and the audit events for that first flow. Once it is stable, the same pattern can expand to more teams and use cases.

Protection happens before the model receives the prompt. The gateway identifies sensitive values in context, then applies the policy assigned to that flow: mask, pseudonymize, redact, block, route, or log.

For example, “Maria Lopez, contract CT-48291, amount EUR 48,200” can become “[PERSON_01], contract [CONTRACT_01], amount [VALUE_01]”. The model keeps enough meaning to produce a useful answer, but it does not need the real identity.

Yes. Policies can be scoped by team, role, tool, model route, risk level, or type of data. A legal workflow may need different rules from a support workflow, and a finance team may need stronger controls than a general internal assistant.

The important part is consistency: the same type of sensitive value should be handled the same way inside a defined workflow, and exceptions should be visible rather than hidden in individual prompts.

The audit trail can record what was detected, which policy was applied, whether the prompt was masked, routed, blocked, or allowed, and which workflow triggered the decision.

The audit log should avoid storing unnecessary sensitive content. The goal is to provide visibility and accountability without creating a second database full of the data you were trying to protect.

Yes. The gateway can apply different decisions depending on the risk of the prompt. Low-risk prompts may continue normally, prompts with sensitive values may be masked, and prompts with prohibited content may be blocked or routed to an approved environment.

Routing is useful when the company wants to use different models for different levels of sensitivity, cost, latency, or compliance posture.

No tool should be described as a compliance guarantee. Privacy Gateway is designed to support privacy-aware AI adoption by reducing unnecessary exposure before prompts reach external models.

It can help with visibility, policy enforcement, masking, routing, and auditability. Legal compliance still depends on your data flows, lawful basis, contracts, retention rules, internal governance, and sector-specific obligations.

Quotas can be defined by user, team, workflow, or model route. They can help control cost, avoid accidental overuse, and make adoption measurable.

For example, a pilot team may receive a controlled allowance while leadership reviews usage, quality, and risk signals. Once the workflow proves useful, limits can be adjusted without changing the whole architecture.

A first useful version should start small. The fastest path is usually a focused discovery, one workflow, a clear list of sensitive data classes, and a small number of model routes.

The timeline depends on the integrations and governance requirements, but the goal is not to build a huge platform before learning. The first version should prove the control pattern and give the team something concrete to evaluate.

The gateway is intended to make model changes less disruptive. If the company changes from one model provider to another, the privacy policy should not have to be reinvented from scratch.

We review prompts, policies, routes, and audit requirements when model behavior or provider terms change. That keeps the control layer useful as the AI stack evolves.

After launch, the important work is operational: review what teams are actually sending, tune false positives, add policies for new workflows, monitor adoption, and decide where the gateway should expand next.

We can keep supporting that process with policy iteration, integration work, analytics review, and product improvements as the organization learns how AI is being used in practice.

We combine product thinking, engineering, and agentic AI with a privacy and control layer when it matters. We do not sell consulting alone or code alone: we stay with you from scoping the use case through production, with oversight, traceability, and a clear improvement path.

We also help choose the right approach — agent, automation, custom software, or Privacy Gateway — instead of pushing the same solution every time.

Custom development usually fits when the process is stable, the rules are clear, and the outcome must be deterministic. An agent fits better when the work involves variable context, judgment, multiple information sources, or tasks that evolve with real usage.

They are not opposites: sometimes the right path is classic automation first and an agent where flexibility matters, or the reverse. At SINTETIKO we define together which parts need fixed code, which need reasoning, and which privacy and oversight controls must exist in production.