GenAI and AppSec Advisory for Regulated Industries

Your CISO needs to sign off. Your engineers want to ship. That gap is the engagement.

Be Digital advises technology and security teams at banking, insurance, asset management, and healthcare payer organizations on how to deploy generative AI systems that satisfy compliance requirements without stalling the engineering roadmap.

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Why regulated industries need a different approach

Deploying generative AI that drafts, monitors, and scans inside a regulated environment is not a model selection problem — it is a controls problem. Banking institutions under SR 11-7, insurance carriers with NAIC guidance, and healthcare payers subject to HIPAA all operate under model risk management frameworks that require explainability, auditability, and documented human oversight. Dropping an LLM into a workflow without addressing those requirements creates regulatory exposure that the security team will discover at the worst possible time.

The Model Context Protocol (MCP) server built for this advisory practice is a reference implementation of exactly those controls. It runs eight tools through five ordered guardrails — input sanitization, prompt injection detection, policy enforcement, output filtering, and rate limiting — before any response reaches the application layer. Three pluggable LLM providers (Anthropic, OpenAI, and AWS Bedrock) mean you are not locked into a vendor, and you can route traffic based on data residency requirements without rewriting the integration.

End-to-end OpenTelemetry observability — traces into Tempo, logs into Loki, dashboards in Grafana — means every inference request is a structured event with a trace ID. Each guardrail and tool call runs as a child span under the request's parent span, so the propagated trace context stitches the whole pipeline into one timeline, and the same trace ID is injected into every log line so traces and logs join up instead of living in separate silos (OpenTelemetry instrumentation). That trace is your audit trail. When a regulator asks what the model was asked, what guardrails fired, and what was returned, you answer with correlated logs rather than reconstruction.

The advisory engagement applies those patterns to your specific system: your data classification, your approval workflows, your existing security tooling. The goal is a deployment your CISO can sign off on without blocking the team that built it.

Platforms and tooling

• → Amazon Q Business — enterprise knowledge assistant deployment: data source integration, IAM Identity Center SSO, retriever configuration, guardrails, and custom plugins
• → Amazon Q Developer — org-wide coding assistant rollout with repo-trained customizations
• → Kiro — spec-driven development environment for structured requirements, design, and task execution
• → MCP servers — custom Model Context Protocol implementations with ordered guardrails and OpenTelemetry observability

Published reference implementation

8 tools. 5 ordered guardrails. 3 pluggable LLM providers (Anthropic, OpenAI, Bedrock). End-to-end OpenTelemetry observability into Tempo/Loki/Grafana.

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More published work

Reference implementations and case studies from enterprise platform engineering and security engagements.

Sample engagement types

Engagements start at $18,000/month. Scope and cadence defined at kickoff.

Ship GenAI your CISO will sign off on.

The gap between what engineers want to deploy and what security will approve is a scoping problem, not a technology problem. Let's close it.

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