Ship AI features enterprise buyers will
Enterprise customers want your AI feature, but they will not sign without proof that it handles their data correctly. Igris delivers tenant isolation, SOC 2 and GDPR Article 28 audit evidence, and per-customer cost visibility at the proxy layer, so every AI feature you ship comes with the evidence package the security review asks for.
AI Copilot for a Project Management Tool
The copilot is live. It summarizes tasks, drafts project updates, and surfaces risks buried in thread history. Then the first enterprise security review lands, and the question is not about the feature's quality — it is about isolation. How exactly is Company A's project data separated from Company B's? The security reviewer wants documented evidence, not a verbal explanation. The second question: what is this feature actually costing per customer? Token consumption does not map cleanly to any existing pricing model, and the team has no reliable way to tell whether the copilot is margin-accretive or margin-destructive for any given account.
Igris creates a dedicated connection per customer tenant, so every customer's copilot traffic runs through its own isolated channel with its own policy set, its own cost accounting, and its own audit log — structurally separated at the gateway layer. Igris Guard applies PII redaction policies on each connection, stripping employee names and internal project identifiers before any prompt reaches the LLM provider, enforcing data minimization that satisfies GDPR Article 5 and DPDP Act 2023 obligations. Igris Lens tracks token consumption and cost per connection, so the team has per-customer LLM spend data that feeds directly into margin analysis. When the enterprise security review arrives, the audit trail export provides documented evidence of isolation, redaction, and policy enforcement.
Enterprise customers get documented proof of tenant isolation, not an architecture diagram. The product team gets per-customer cost data that turns LLM spend from an unattributed line item into a tracked input to gross margin. The next enterprise security review has an evidence package waiting for it rather than a scramble to produce documentation under deal deadline pressure.
AI-Powered Customer Support Platform
A support platform that ships AI ticket classification and automatic response drafting immediately faces a commercial question from every enterprise prospect: what happens to our customers' support ticket data? Enterprise security reviewers want a specific answer: how is PII handled, how is one customer's ticket data isolated from another's, what is the retention policy, and where is the audit evidence? A verbal answer does not close the deal. A GDPR Article 28 data processor agreement without technical evidence of the controls it references does not either.
Igris Guard applies PII detectors to all ticket content flowing through the AI pipeline, redacting customer names, contact details, account numbers, and personal information in both the ticket text sent to the LLM and the draft responses returned. Schema isolation between tenant connections ensures that one customer's ticket data cannot interact with another's, satisfying isolation requirements that SOC 2 Type II controls and GDPR Article 28 processor obligations both require documented evidence for. Encrypted body logs with configurable retention limits give the compliance team a time-bounded audit record. The Lens audit trail exports in a format the team can attach to a security review response, include in a SOC 2 audit package, or produce during an EU AI Act Article 13 transparency documentation request. For Indian enterprise customers, DPDP Act 2023 compliance is addressed by the same controls.
Enterprise deals do not stall on the security review because the evidence package exists before the question is asked. The SOC 2 auditor gets documented evidence of AI data handling. The sales team stops losing deals to the answer: "we need to see your AI data protection documentation."
AI Code Assistant
Code completion features work by sending context to the LLM — the file the developer is editing, the surrounding functions, the imports. That context inevitably contains things that should never reach an external provider: hardcoded API keys, database connection strings, internal service URLs, authentication tokens, and entire configuration files pasted as context. Most teams have no technical control that catches these patterns before they leave the network. Code completion in a loop — a stuck autocomplete cycle, a retry pattern, an expanding context window — can drive cost anomalies invisible until the provider invoice arrives.
Igris Guard applies content guard policies built for code contexts: custom detection patterns that match API key formats, environment variable patterns, database connection string structures, and internal URL schemas. When a prompt contains any of these, Guard blocks it before it reaches the LLM — a policy enforcement action at the gateway. Token limits cap how much context can be sent in a single request, preventing accidental full-codebase dumps. Igris Lens surfaces cost anomaly alerts the moment a completion pipeline breaks its expected token consumption pattern, catching runaway loops before they become a billing event. Encrypted body logging gives the engineering team a safe way to inspect the actual prompt and response for any session where quality is reported as degraded.
Secrets, API keys, and database credentials stay inside the network regardless of what the developer pastes as context. The billing team does not discover a runaway completion loop through the monthly invoice. When a developer reports a bad session, the engineering team can retrieve the encrypted log and inspect the actual call the same day.
AI Content Generation Platform
Content platforms using multiple LLM providers for different content types face a management problem that scales with every new provider added. Each provider has its own SDK, authentication, pricing model, and latency profile. Cost visibility requires pulling data from five separate dashboards and manually attributing spend to customers or content types that the provider has no visibility into. Rate limiting is applied inconsistently, so a single high-volume customer can exhaust a provider allocation meant to serve hundreds. Model selection is enforced by convention in the codebase rather than policy at the infrastructure level.
Igris acts as a single gateway in front of all LLM providers, so one integration in the codebase covers every backend — same SDK interface, same endpoint pattern, different routing policy. Igris Lens tracks token consumption and cost per provider, per connection, and per model in one dashboard. Rate limiting per customer connection enforces fair usage. Model restriction policies enforce which models are permitted for which content types — drafts route to economical models by policy, final output routes to premium models by policy. The SOC 2 audit trail records every provider call, every model used, and every policy enforcement event across all backends in one exportable log.
One integration replaces five. LLM cost per customer and per content type is a dashboard query. Model assignment is enforced by infrastructure policy. When the SOC 2 auditor asks for evidence of AI data handling across all provider integrations, there is one audit trail — not five partial ones.
AI-Powered Analytics and Reporting Feature
Analytics SaaS products adding natural language querying process customer data that enterprise customers consider among their most sensitive: revenue figures, user behaviour metrics, competitive performance data, and business KPIs. The isolation requirement is acute: a model holding context from Company A's revenue query when Company B's user asks a similar question is the product failing its core multi-tenancy contract. Enterprise buyers ask this question in security reviews and expect a technical answer. Without per-customer cost visibility, the team has no way to know whether the analytics AI is profitable at the per-account level or quietly destroying margin on the largest customers.
Each customer tenant gets an isolated Igris connection with its own context boundary. Igris Guard applies custom content policies that detect and block business-sensitive patterns before they reach the LLM provider. Igris Lens tracks token consumption and cost per analytics connection, giving product and finance teams the per-customer cost data needed to evaluate profitability and inform usage-based pricing decisions. The SOC 2 audit trail records every analytical query, every model used, and every policy enforcement action.
Enterprise customers get a natural language analytics feature with documented tenant isolation. The product team gets the per-customer LLM cost data that makes usage-based pricing commercially rational. And the security review question about how customer business data is protected has a documented answer before the deal reaches that slide.
AI Search and Retrieval-Augmented Generation Feature
SaaS products adding AI search over customer documents use retrieval-augmented generation pipelines where an agent first retrieves relevant documents, then passes them to the LLM to generate an answer. If the agent's tool calls are not governed by a strict scope boundary, it can retrieve documents the current user is not authorized to see, or in a multi-tenant environment, retrieve documents belonging to a different customer entirely. This is not a theoretical vulnerability — it is the architectural failure mode of every RAG implementation that does not enforce retrieval scope at the agent governance layer.
Igris Sentinel governs every tool call the search agent makes, enforcing an allowlist of permitted retrieval actions and document scopes so the agent cannot access anything outside the current user's authorized context. Tool calls that attempt to retrieve documents beyond the defined scope are denied at the governance layer before the retrieval executes. Guard applies content inspection to retrieved document snippets before they are passed to the LLM as context. The full Sentinel audit trail records every retrieval action, every document accessed, every tool call made, and every policy enforcement event — satisfying SOC 2 access control requirements, GDPR Article 5 data minimization obligations, and EU AI Act Article 13 transparency documentation.
The AI search feature retrieves only what it is permitted to retrieve — enforced at the governance layer on every query. Cross-tenant retrieval is architecturally impossible within the defined policy. The SOC 2 auditor, the GDPR data processor review, and the EU AI Act transparency request all have a complete tool call audit trail to examine.
Turn the security review into a formality
See how SaaS teams prove tenant isolation, export SOC 2-ready audit evidence, and track LLM cost per customer to protect margin.