Most organizations deploy AI agents without governance - and discover the problems at the worst possible time. PII leaking through prompts, unaudited model decisions driving business actions, agents with unchecked tool access, compliance violations buried in LLM logs no one ever reads.
Governance isn't a layer you add later. It's architecture. We design security, privacy, and compliance controls into your agent systems from the ground up - so they ship safe and stay that way.
PII flows unfiltered into LLM prompts
No audit trail for AI-driven decisions
Agents with unconstrained tool access
Prompt injection vulnerabilities
Compliance exposure (GDPR, HIPAA, SOC2)
PII scrubbed at ingestion before LLM sees it
Every agent action logged and auditable
RBAC controls what each agent can do
Input/output filtering and validation
Compliance boundaries enforced in architecture
Data sanitization gateways
Audit logging infrastructure
Agent permission boundaries
Injection prevention layers
Compliance reporting pipelines
Prevent sensitive data from reaching LLMs unnecessarily.
PII/PHI detection and pseudonymization
Data residency and sovereignty controls
Tokenization before LLM ingestion
GDPR right-to-erasure compliance
Protect agents from adversarial inputs and prompt attacks.
Prompt injection detection
Input validation and sanitization
Output filtering and guardrails
Agent sandboxing and isolation
Define exactly what each agent can see and do.
Tool-level permission boundaries
Role-based capability scoping
Least-privilege agent design
Human-in-the-loop checkpoints
Full traceability of every AI decision and action.
Immutable interaction logs
SIEM integration for AI events
Decision attribution and lineage
Compliance reporting dashboards
PHI handling architecture for AI agents operating in healthcare environments. BAA compliance, minimum necessary data principles, and audit trail requirements built into agent design.
Data subject rights enforcement, consent management, purpose limitation, and cross-border transfer controls for AI pipelines operating in regulated jurisdictions.
Change management, access control, availability, and security monitoring requirements for AI agents in SOC 2 audited environments.
Audit your current agent deployments. Identify what data flows where, what tools agents can invoke, and where the compliance exposure lives.
Design the governance architecture: data flows, sanitization layers, permission boundaries, audit infrastructure, and compliance controls.
Build the controls into your agent infrastructure. Sanitization gateways, RBAC configuration, logging pipelines, and SIEM integration.
Establish governance as a continuous practice: drift detection, regular audits, policy updates as agent capabilities expand.