PII/PHI Redaction Gateways
Intercepting prompt payloads to identify and filter sensitive personal identifiers before hitting external API hosts.
Compliant AI Development & HIPAA Engineering
Compliant AI development requires embedding security, access controls, and data residency policies directly into the LLM orchestration layer. Our engineering teams design Retrieval-Augmented Generation (RAG) systems that respect granular role-based permissions, configure HIPAA-compliant private model hostings, and implement real-time output guardrails. We help you build production-ready AI applications that protect private data and comfortably pass customer SOC 2 audits.
Build compliant AIHIPAA and SOC 2 Compliant AI Systems
We build secure applications for software engineering and product leaders in highly regulated spaces. We help U.S. organizations engineer AI systems that satisfy HIPAA, SOC 2 Type II, and GLBA requirements from the first line of code.
Compliant AI Engineering Services
We implement automated PII/PHI redaction in prompts, audit logging for model transactions, data encryption at rest and in transit, and continuous output drift checks.
Key capabilities
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PII/PHI Redaction Gateways
Intercepting prompt payloads to identify and filter sensitive personal identifiers before hitting external API hosts.
Private Model Deployment
Configuring dedicated, single-tenant model environments on AWS, Azure, or GCP to ensure data isolation.
Granular RAG Permissions
Ensuring the retrieval database strictly honors source document access controls and user roles.
Real-Time Output Guardrails
Filtering model outputs for hallucination bounds, toxicity, prompt injection, and regulatory policy drift.
SOC 2 Evidence Pipelines
Automating the collection of trace logs, change logs, and validation outputs for security audits.
Compliant AI Use Cases
HIPAA-Compliant Medical Assistants
Scribing and clinical documentation tools that safely process Protected Health Information (PHI).
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HIPAA-Compliant Medical Assistants
Scribing and clinical documentation tools that safely process Protected Health Information (PHI).
Regulated Financial Chatbots
Pre-sales and service assistants matching strict GLBA and FTC Safeguards guidelines.
Secure Internal Knowledge Bases
Enterprise-wide search across sensitive HR, product, and legal document repositories.
Common development risk points
Data leakage risks
Employees or applications sending sensitive PII/PHI to public model endpoints.
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Data leakage risks
Employees or applications sending sensitive PII/PHI to public model endpoints.
Model hallucination failures
Uncontrolled AI outputs creating operational liability or policy breaches.
Audit evidence panic
Manually compiling logs and system descriptions to satisfy SOC 2 auditors.
Engineering outcomes
Secure, auditable generative AI applications in production
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Secure, auditable generative AI applications in production
HIPAA and SOC 2 compliant architecture patterns
Automated data leakage prevention at prompt level
Reliable, guardrailed model behavior with low error rates
Reduced compliance review times for product releases
Compliance & Security Controls
AWS Bedrock
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AWS Bedrock
Azure OpenAI
Pinecone VPC
pgvector & RDS
Auth0
LlamaGuard
What delivery includes
Security architecture design, model API gateway setup, output validation layers, permission mapping, and automated logging configurations.
Explore our full set of services on the Mobiloitte USA homepage, or see related industries.
For broader global engineering capability, explore the wider Mobiloitte platform. mobiloitte.com
Build your compliant AI systemRelated industries
Compliant AI Development is commonly scoped for teams in these sectors. Explore how we adapt delivery to industry constraints.
Explore related solutions
Many U.S. initiatives combine compliant ai development with other capabilities. These solutions are commonly delivered together.
Why U.S. organizations choose Mobiloitte USA
Compliant AI Development projects often underdeliver. The reason is rarely the technology. It is usually the delivery process.
What makes delivery succeed
- A clear business problem definition before work begins
- Careful discovery to find integration needs
- Governance and compliance built in from day one
- Stakeholder alignment across business and technical teams
Our U.S. engagement model
Business leaders, operations teams, and technical stakeholders work directly with our delivery team.
- We invest upfront time to understand your operating environment
- We understand your regulatory context and constraints
- We finalize the implementation plan only after that understanding is complete
Built for long-term value
Every engagement is designed to last. We do not just deliver and disappear.
- We document all implementation decisions
- We train internal teams on the delivered solution
- We set up monitoring so issues are caught early
- We provide structured post-launch optimization support
Ready to scope compliant ai development for your team?
Share your current workflow, systems, and goals. We will map a practical first phase with delivery steps and measurable checkpoints for your U.S. initiative.
Compliant AI Engineering Steps
We design and deploy secure, policy-aligned generative AI systems adhering to HIPAA and SOC 2 requirements.
1. Map Data Boundaries
Analyze sensitive personal data data flows (PII/PHI) across all model routes.
2. Build Redaction Gateways
Configure automated prompt filters to redact sensitive tokens before external API calls.
3. Configure Private Hostings
Deploy dedicated, single-tenant foundation models in secure cloud VPC environments.
4. Design RAG Security
Audit retrieval databases to match document access levels and user permissions.
5. Harden Output Guardrails
Embed real-time verification logs to prevent hallucinations and policy drift.
6. Run SOC 2 Mock Audits
Generate automated compliance trace trails to simplify external auditor reviews.
Compliant AI Development FAQs
Common questions from U.S. organizations considering compliant ai development as part of a broader delivery or modernization initiative.
What does Compliant AI Development usually help improve?
Compliant AI Development is typically used to reduce execution friction, improve consistency, support better user or operator experiences, and create clearer operational visibility.
Is Compliant AI Development suitable for existing systems or only new builds?
It can support both. Many engagements connect into existing tools and workflows rather than starting from a blank slate.
How do you scope a Compliant AI Development initiative?
Scoping usually looks at business goals, users, workflows, data needs, systems involved, and the fastest path to a valuable first release.
Can Compliant AI Development be launched in phases?
Yes. A phased rollout often helps teams validate assumptions, reduce delivery risk, and prioritize the highest-value use cases first.
Do you integrate Compliant AI Development with other business platforms?
Yes. Integration planning is usually part of the delivery model so the solution works with the broader operating environment.