Compliance Infrastructure Build
Compliance built for how AI-native systems actually work not retrofitted from a fintech checklist.
Enterprise buyers do not only evaluate whether your AI product works. They evaluate whether your company can prove control over the system, the data, the model, the infrastructure, and the risk. This engagement builds the compliance infrastructure behind your AI product so security teams, auditors, and procurement buyers can see a credible, documented, enterprise-ready control environment.
Overview
When an enterprise deal reaches the security team, the question is not whether your product is impressive. The question is whether your governance infrastructure exists. SOC 2 Type I does not ask whether your AI is good. It asks whether you can demonstrate consistent, documented, auditable control over how it operates.
Most AI-native companies prepare for that moment using a traditional SaaS compliance framework. That framework was designed for CRUD applications. It does not naturally account for RAG pipeline security, LLM API key rotation, vector database access enforcement, training data provenance, model governance documentation, or prompt injection controls.
A Compliance Infrastructure Build closes that gap. The engagement includes policy documentation, control implementation, AI-specific governance design, security architecture validation, and pre-audit evidence packaging built around what your stack actually is.
This is not a theoretical consulting deliverable. It is operational infrastructure, the documentation and control layer that helps make your AI product enterprise-ready, audit-ready, and procurement-ready.
Ideal For
- AI-native SaaS companies with an enterprise deal contingent on SOC 2 or ISO 27001
- Funded startups preparing for Series A due diligence that includes security review
- Teams in regulated sectors such as finance, healthcare, insurance, and government
- Companies operating under EU AI Act obligations requiring documented AI governance
- Founders who need compliance infrastructure built before an auditor, buyer, or board asks for it
What's Included
Comprehensive services tailored to your needs.
- Information Security Policy
- AI/ML Governance Policy covering training data, model management, and output oversight
- Data Classification and Handling Policy
- Acceptable Use Policy
- AI-adapted Incident Response Plan
- Vendor and Third-Party AI Risk Policy
- Access Control and Identity Management Policy
- Training data governance documentation
- Customer data exclusion and opt-in/opt-out documentation
- LLM API key rotation procedures
- Vector database access enforcement review
- Model provenance records and lineage documentation
- Prompt injection control documentation
- Embedding and cached chunk deletion procedure
- Zero Trust architecture validation
- Network segmentation and access control review
- Secrets management assessment
- CI/CD pipeline security integration review
- SAST and DAST checkpoint review
- Container and Kubernetes security baseline review
- Control mapping document aligned to the selected framework
- Evidence type and location documented for each control
- Control owner and review schedule assignment
- Pre-audit evidence folder structure
- Evidence mapped to controls with gap indicators
- Auditor handover support for Enterprise tier clients
The Process
A structured approach to deliver results with clear communication at every step.
Assessment & Scope Confirmation
We review your current control environment, confirm the target framework, map the deal or audit timeline, and identify the quick wins and critical path items.
Policy Development
The full policy suite is drafted and finalized around your actual operations. This is not copy-paste compliance documentation; each document reflects how your AI system actually works.
AI-Specific Control Implementation
We build and document the controls that traditional SaaS compliance often misses: training data governance, LLM credential management, vector database access, model provenance, and prompt injection controls.
Evidence Package Assembly
All evidence is organized against the selected control framework. Each control has its evidence reference, owner, location, and status documented.
Pre-Audit Review & Handover
We complete an internal walkthrough of the evidence package, close remaining gaps, prepare auditor handover documentation, and ensure the control environment is ready for formal review.
Tools & Technologies
Industry-leading tools and frameworks I use for this service.
Pricing
Transparent pricing options to fit your needs and budget.
- Core policy suite with 6 documents
- SOC 2 Type I scope definition
- AI-specific control documentation for 3 core controls
- Control mapping document
- Pre-audit evidence checklist
Best for: Early-stage teams with a first enterprise deal requiring basic compliance posture
- Complete policy suite
- Full SOC 2 or ISO 27001 control set
- Complete AI-specific governance layer
- Cloud security architecture review
- Full control mapping document
- Pre-audit evidence package
Best for: Funded AI-native startups preparing for a formal Type I audit
- Everything in Full Infrastructure Build
- Auditor liaison throughout the Type I process
- Evidence request management
- Auditor walkthrough preparation
- Post-build remediation support for 30 days
Best for: Companies with active auditor engagement or a board-mandated compliance timeline
All prices are starting points. Final pricing depends on specific requirements. Schedule a consultation for a custom quote.
Frequently Asked Questions
Common questions about compliance infrastructure build.
Does this require changes to our product codebase?
What framework do you build for?
Do you work with our auditor directly?
What happens after the build is complete?
Related Services
You might also be interested in these services.
A structured AI compliance assessment that maps your architecture against the controls enterprise buyers require before security review, procurement, or audit.
Ongoing AI compliance management for companies that need to maintain posture, respond to vendor questionnaires, monitor controls, and stay current with evolving AI regulations.
Security testing and quality assurance for AI-native applications, including API security, LLM interaction testing, prompt injection validation, and automated regression coverage.
Let's Discuss Your Build Scope
Let's discuss your requirements and find the right solution for your business.