Illustrative Scenario: Source-Code DLP for AI-Assisted Development in a Series C SaaS Company

Consider a Series C SaaS company archetype that has grown rapidly to 500-1,500 employees, with 400-1,000 developers building and maintaining a cloud platform serving enterprise customers across financial services, healthcare, and government verticals. The engineering culture in this archetype typically encourages AI tool adoption - developers use GitHub Copilot for code completion, ChatGPT and Claude for debugging and architecture discussions, and various specialized AI tools for code review, documentation generation, and test creation.

The security team in this archetype has long suspected that AI tool usage is creating data exposure risks, but lacks any mechanism to measure or control it. A targeted investigation typically confirms the fear: developers routinely paste proprietary source code - authentication modules, payment processing logic, and API integration layers - into AI chatbots for debugging assistance. API keys, database connection strings, JWT secrets, and cloud provider credentials are frequently embedded in those code snippets. Internal architecture documents, system design diagrams described in text, and customer-specific configuration details are also shared freely. This pattern maps directly to the OWASP Top 10 for LLM Applications LLM06 risk category: Sensitive Information Disclosure.

Existing secret scanning tools in this archetype typically only cover git repositories and CI/CD pipelines and have zero coverage for AI interaction channels. With enterprise customers increasingly requiring SOC 2 attestations that specifically address AI data handling, and pending deals often contingent on demonstrating AI governance controls, the security team needs a solution that can secure AI-assisted development without destroying the developer productivity gains that AI tools provide.

A company in this archetype faces a layered challenge that combines secret hygiene, IP protection, customer-contract obligations, and developer experience:

• Credential exposure (OWASP LLM06): AWS access keys, GCP service-account keys, Azure connection strings, JWT tokens, OAuth secrets, and SSH private keys pasted into AI prompts are effectively published outside the company's control boundary. Industry benchmarks for AI-channel secret exposure in engineering-led organizations of this size routinely place daily interception volume in the tens to low hundreds per day once instrumented.
• Source code IP loss: Code containing internal package imports, proprietary framework references, customer-specific logic patterns, and distinctive architectural patterns represents trade-secret IP that, once disclosed to an external model, may compromise both customer contractual confidentiality and trade-secret protection.
• SOC 2 and customer security review pressure: Enterprise customers increasingly require SOC 2 attestations and security questionnaires that specifically address AI data handling. Pending deals contingent on demonstrating AI governance controls are common in this archetype.
• Developer velocity is non-negotiable: Any solution that adds perceivable latency, breaks IDE workflows, or requires per-tool integration will be routed around by developers within weeks. The governance layer has to be effectively invisible.

The Head of Security and VP of Engineering in this archetype need a control plane that can govern AI usage without breaking developer experience, intercept secrets at the prompt level before they reach external providers, and produce evidence that SOC 2 auditors and enterprise security reviewers will accept.

For this archetype, Areebi's design fit comes from inspecting AI interactions at the network level - without requiring changes to individual developer tools. A single deployment is designed to govern Copilot, ChatGPT, Claude, Cursor, and any other AI tool that communicates over the network, rather than requiring per-tool integrations. The golden image deployment model means the platform sits inside the company's existing security boundary.

The DLP engine is designed to be configured with three categories of detection patterns tailored to software development contexts. The first category covers secrets and credentials: API keys across major cloud providers (AWS, GCP, Azure), database connection strings, JWT tokens, OAuth secrets, SSH private keys, and internal service-account credentials. Pattern matching is calibrated against a customer's actual credential formats to minimize false positives on example code or documentation strings. The second category targets proprietary source code: detection rules can identify code containing internal package imports, proprietary framework references, customer-specific logic patterns, and code blocks matching the company's distinctive architectural patterns. The third category covers architectural and infrastructure data: internal service names, infrastructure topology details, database schema descriptions, and customer environment configurations. This three-layer approach is the practical implementation of OWASP LLM06 (Sensitive Information Disclosure) controls at the egress boundary.

Workspace policies are designed specifically for developer workflows. Rather than blanket blocking, the platform is configured to mask detected secrets in-line while letting the surrounding code context pass through to AI models. A developer can paste a code block for debugging help and Areebi will automatically replace the embedded AWS access key with a placeholder before the prompt reaches the AI provider - preserving the debugging context while eliminating the credential exposure. The shadow AI detection layer identifies all AI tools in use across the engineering organization and provides a centralized dashboard showing usage patterns, data exposure attempts, and policy enforcement actions.

A deployment in this archetype is designed to produce immediately measurable results. The targets below are modelled against the OWASP Top 10 for LLM Applications, published industry benchmarks for AI-channel secret exposure, and IEC 62443 supply-chain protection expectations for engineering data; they are not outcomes delivered to a paying customer today.

Areebi is designed to intercept on the order of 2,000 or more secrets and credentials per month that developers attempt to send to external AI tools in an archetype of this size. Industry benchmarks routinely report that the AI channel alone surfaces more secret-exposure events in a single month than git-based secret scanning surfaces in a quarter, simply because AI prompts are short-lived and were never previously instrumented.

The design intent is for the company to record zero proprietary source code exposures through AI channels post-deployment. Code containing internal imports, proprietary frameworks, and customer-specific logic should be automatically detected and either masked or blocked, depending on sensitivity classification. The audit trail is designed to provide complete visibility into every AI interaction across the engineering organization, giving the security team the evidence base required for SOC 2 attestations and enterprise customer security reviews.

Developer reception is the success criterion that determines whether the program survives. The design target of 75%+ developer adoption within 60 days is conditional on Areebi's sub-50ms DLP latency keeping the governance layer effectively invisible in the developer workflow. The in-line secret masking feature is designed to actively improve workflow by catching credential leaks that developers would not have noticed otherwise. Verified adoption metrics, interception volumes, and SOC 2 outcomes from a named design partner will replace these design-target framings once those pilots go public.