Compliance & Governance

AI that your legal team will actually sign off on.

Autonomous agents create real liability exposure in regulated environments. We architect the controls, audit trails, and data policies that make AI deployable — not just possible.

Regulated environments we serve

HealthcareHIPAA · PHI access controls · Covered entity architectures
Financial ServicesSBA lending · GLBA · SOX-adjacent audit trails
Legal & ProfessionalPrivilege safeguards · Conflict-of-interest controls
eCommerce & RetailPCI-DSS alignment · Consumer data governance
Compliance controls we implement Einstein Trust Layer PII Masking Zero Data Retention Audit Trails Role-Based Agent Access Data Residency Policy HIPAA Alignment

The governance gap

Most AI implementations stall at the compliance review.

The technical build isn’t the problem. The problem is that autonomous agents making decisions on regulated data create obligations that most platform vendors don’t address — and most consultants don’t think about until it’s too late.

Legal needs to know what the agent saw and why it acted. Compliance needs to know where PHI or PII went. Risk needs to know what happens when the agent is wrong. We build architectures that answer those questions before they become incidents.

The question every regulated AI project faces: “Can you prove what data the agent accessed, what decision it made, and why — for every single transaction?” If you can’t answer that, you don’t have a governance-ready system.

What uncontrolled AI risk looks like

  • Agents accessing PHI outside their authorized scope
  • Unmasked PII transmitted to external LLM providers
  • No audit trail for autonomous decisions affecting customers
  • Data retained by third-party AI after processing
  • Agent actions that conflict with contractual or regulatory obligations

The Governance Framework

Four layers every compliant AI architecture needs.

We implement governance at every layer of the stack — not as an afterthought, but as a structural constraint the agent operates within from day one.

Data Layer
Unified, classified data with enforced retention policies, PHI/PII field-level tagging, and role-based access at the record level before the agent ever queries it. Built in Salesforce Data Cloud or your existing CRM schema.
Trust Layer
Einstein Trust Layer enforces zero data retention by external LLMs, automatic PII masking on every prompt, toxicity screening, and full audit logging of every agent action and data access event.
Agent Layer
Scoped agent personas with explicit permission sets, guardrail topics that define what the agent cannot do, and deterministic tool definitions that constrain what systems it can call and what data it can return.
Observability
Continuous audit trails, decision-path logging, anomaly alerting, and structured exports for compliance review. Every agent interaction is attributable, retrievable, and explainable.

Service Capabilities

What we actually build.

HIPAA Architecture Design

Salesforce and Agentforce environments architected to meet HIPAA Technical Safeguard requirements — access controls, audit controls, integrity controls, and transmission security.

  • PHI field classification and masking
  • Covered entity / BA agreement alignment
  • Minimum necessary access enforcement

Agent Guardrails & Scoping

Explicit permission boundaries, topic restriction lists, and tool-level access controls that define precisely what an autonomous agent can see, say, and do — and what it cannot.

  • Einstein Trust Layer configuration
  • Guardrail topic definition
  • Escalation path design

Audit Trail Architecture

Structured, queryable logs of every agent decision, data access event, and system action. Built for legal hold, compliance review, and incident response — not just system monitoring.

  • Decision-path attribution
  • Legal hold-ready export format
  • Anomaly alerting

Data Residency & Retention

Policy-enforced controls on where regulated data is stored, how long it is retained, and what happens when retention periods expire. Aligned to HIPAA, GLBA, or your contractual obligations.

  • Zero-copy architecture where applicable
  • Automated retention policy enforcement
  • Cross-border data flow documentation

Compliance Readiness Assessment

A structured review of your current Salesforce environment, AI deployments, and data flows against the regulatory framework that governs your business — with a prioritized remediation roadmap.

  • Current-state gap analysis
  • Prioritized remediation roadmap
  • Regulator-ready documentation

Role-Based Access Design

Salesforce permission set architecture, profile design, and agent-specific access controls that enforce least-privilege access for both human users and autonomous agents operating on sensitive data.

  • Permission set and profile audit
  • Agent persona scoping
  • Privileged access review

Risk Mapping

Regulatory exposure by industry.

The governance controls we implement are calibrated to the specific regulatory framework your business operates under.

IndustryPrimary RegulationKey AI RiskOur Control
HealthcareHIPAA / HITECHPHI exposed to external LLM; agent accesses beyond minimum necessaryPII masking, scoped agent personas, zero data retention enforcement
SBA LendingSBA SOP 50 10 / GLBANon-public borrower data used in AI training; adverse action without explainabilityZero-copy architecture, decision-path logging, audit-ready exports
Financial ServicesGLBA / SOXUncontrolled data retention, agent decisions affecting account accessRetention policy enforcement, guardrail topics, role-based controls
Legal & ProfessionalState bar rules / privilegePrivileged content accessed by AI; conflict-of-interest screening gapsField-level access controls, agent scope restrictions, audit trails
eCommerce / RetailCCPA / PCI-DSSConsumer PII in agent prompts; payment data in AI context windowsPII masking, data classification, consent and opt-out enforcement

Our Process

How we build governance-ready systems.

01

Regulatory Mapping

Identify the specific frameworks governing your business — HIPAA, GLBA, CCPA, SBA SOP, or contractual obligations — and translate them into concrete technical requirements for your Salesforce and AI environment.

02

Data Classification & Flow Audit

Map every data element your AI systems touch, classify PHI/PII, document cross-system flows, and identify where regulated data currently moves without adequate controls.

03

Control Architecture Design

Design the layered governance architecture — data, trust, agent, and observability layers — with explicit decisions at each layer documented and defensible to regulators or auditors.

04

Implementation & Configuration

Configure Einstein Trust Layer, implement permission architectures, build audit logging, enforce retention policies, and test guardrails against adversarial inputs before go-live.

05

Documentation & Ongoing Review

Produce regulator-ready documentation of your governance architecture, and establish a review cadence to keep controls current as your AI deployments and regulatory landscape evolve.

Already running Agentforce or planning to? Governance controls are significantly cheaper to build in than to retrofit. Every week of ungoverned AI operation in a regulated environment is a week of compounding exposure. The right time to architect compliance is before the first agent goes live — the second-best time is now.

Frequently asked questions

Common questions about AI compliance & governance

Does the Einstein Trust Layer make Agentforce HIPAA compliant?

The Einstein Trust Layer is a critical component — it enforces zero data retention by external LLMs, automatic PII masking, and audit logging. But it is not HIPAA compliance by itself. HIPAA compliance requires a full architecture: proper data classification, access controls, Business Associate Agreements with applicable vendors, audit controls, and documented policies. The Trust Layer handles the transmission and LLM side; Archer Ajax designs the complete HIPAA-aligned stack.

What is zero data retention and why does it matter for regulated industries?

Zero data retention means that when Salesforce sends a prompt to an external LLM provider (like OpenAI or Anthropic), the provider contractually agrees not to store, log, or use that data for training. This is a prerequisite for using AI in HIPAA-governed environments, because PHI cannot be retained by unauthorized third parties. The Einstein Trust Layer enforces this at the platform level; Archer Ajax verifies the configuration and documents it for compliance purposes.

How do agent guardrails work in Agentforce?

Guardrail topics in Agentforce are explicit definitions of what the agent must never do or say — for example, providing specific medical diagnoses, discussing competitor pricing, or accessing records outside its assigned account scope. These are configured at the agent persona level and enforced by the Atlas Reasoning Engine before any action is taken. Archer Ajax defines guardrail topics based on your regulatory obligations and business risk profile, then tests them against adversarial inputs.

What does a compliance readiness assessment cover?

Our assessment reviews your current Salesforce org configuration, any AI or agent deployments, data flows involving regulated data, permission and profile architecture, and existing data retention policies. We map the current state against your applicable regulatory framework and deliver a prioritized remediation roadmap with estimated effort for each control gap. The output is actionable, not just a list of findings.

Do you work with organizations that aren’t yet on Agentforce?

Yes. Some engagements start with a governance assessment of an existing Salesforce org before any AI is deployed — establishing the right data architecture and access controls first means AI can be introduced safely when the organization is ready. It’s significantly more cost-effective than retrofitting governance after agents are already operating on live data.