Solutions

Securing enterprise AI, end to end

AI moves your data across more boundaries than any technology before it. Here's how we map those boundaries, secure every crossing, and turn compliance into something your platform enforces automatically.

Architecture

Where your data actually goes when you use AI

A typical corporate AI interaction crosses four trust boundaries in milliseconds — from an employee's browser to an agent, to a model, into your most sensitive data, and sometimes out to third parties. Each crossing is an attack surface. We secure all four.

Corporate trust perimeter USERS & ENDPOINTS Employees & workforce Customers & partners Devices & browsers AI APPLICATIONS Copilots & chat apps AI agents & workflows Business systems AI PLATFORM Models & inferenceAzure OpenAI · Bedrock · Vertex RAG & vector store Guardrails & filters ENTERPRISE DATA Databases Documents & SaaS Data lake 1 2 3 4 prompts inference grounded retrieval filtered responses Outside the perimeter: third-party model APIs · public tools · internet boundary 4 controls all egress ▲
Boundary 1

User → Application

Conditional access and phishing-resistant MFA, device trust, session controls, and DLP on what users can paste or upload into AI apps.

Boundary 2

Application → AI Platform

Agent and workload identity, least-privilege tool permissions, API gateways, prompt inspection, and rate and cost controls per identity.

Boundary 3

AI Platform → Data

Private endpoints, document-level authorization on RAG retrieval, data classification and masking, encryption, and full provenance on what grounded each answer.

Boundary 4

Perimeter → Third parties

Egress allow-listing for external model APIs and tools, tenant isolation guarantees, no-training data agreements, and outbound DLP on responses.

AI-powered compliance

From use case to enforced compliance — automatically

Compliance shouldn't be a document that goes stale the day it's written. We feed your business use case into an AI compliance engine — ours, or one we build inside your tenant — and out comes an enforced, continuously verified posture.

USE CASE "AI claims processing" business intake · data classes AI COMPLIANCE ENGINE In-house model — or one we build inside your tenant maps use case → risks → controls trained on industry frameworks Security requirements derived from NIST · SOC 2 · ISO traceable to each control Policy as code preventative guardrails deployed to Azure · AWS · Google Cloud CONTINUOUS POSTURE monitor · prevent drift auto-remediate · evidence findings and drift feed back into the engine — posture improves with every cycle

Grounded in the frameworks your auditors use

The engine doesn't invent requirements — it derives them from the industry frameworks that apply to your use case and data classes, and keeps every generated control traceable back to the clause that demanded it.

NIST CSF 2.0 NIST AI RMF SOC 2 ISO/IEC 27001 ISO/IEC 42001 HIPAA PCI DSS
See the NIST AI RMF mapped category by category
  • Security requirements generated per use case — reviewed and signed off by our architects, not just the model
  • Preventative guardrails deployed as policy-as-code across Azure, AWS, and Google Cloud
  • Drift detected and corrected continuously — posture that stays compliant between audits
  • Evidence generated as a by-product, so audit prep shrinks from months to days
The operating model

Assurance as a loop, not an event

Annual audits and point-in-time pen tests can't keep up with an AI-accelerated estate. The end state we build for every client is a loop that never stops running.

Always-on assurance 1 · Detect ML anomaly + posture scans 2 · Prioritize risk-quantified, business-aware 3 · Remediate automated fixes & guardrails 4 · Prove auditor-ready evidence

See it against your own architecture

Bring us one AI use case. We'll map its trust boundaries and show you the compliance posture it needs — usually within two weeks.

Take the AI Readiness Check Book a working session