Prompt and data security
Prompt injection checks, sensitive-data handling, source controls, retrieval boundaries, and approved system instructions.
Responsible AI governance
Control model and agent risk without slowing responsible innovation
DigiScience helps enterprises embed responsible AI controls for prompt and data security, agent permissions, human approval, model risk, evaluation, audit trails, incidents, and ongoing observability.
Controls embedded in delivery
Governance becomes actionable when policy, workflow controls, approvals, evidence, monitoring, and ownership operate together.
Governance control areas
Agent control
Human approval, allowed actions, blocked actions, escalation paths, tool permissions, and review checkpoints.
Model risk and observability
Evaluation sets, behavior monitoring, hallucination risk review, incident logging, cost tracking, and governance reports.
Deliverables
Governance work should produce controls that delivery teams can actually use.
Responsible AI control map
Policy, risk categories, owners, approval points, and evidence requirements.
Policy, risk categories, owners, approval points, and evidence requirements.
Agent operating rules
Allowed tools, human review triggers, sensitive actions, fallback behavior, and escalation.
Allowed tools, human review triggers, sensitive actions, fallback behavior, and escalation.
Audit and monitoring model
Logs, metrics, review cadence, cost visibility, incident handling, and reporting.
Logs, metrics, review cadence, cost visibility, incident handling, and reporting.
Governance operating model
Policy and accountability
Use-case inventory, risk tiers, model and data rules, accountable owners, review bodies, exception handling, and evidence requirements.
Evaluation and release gates
Quality, groundedness, safety, fairness, privacy, security, tool-use, failure-mode, and cost evaluations before promotion.
Production oversight
Behavior and drift monitoring, incidents, complaints, overrides, approvals, audit evidence, periodic reviews, and improvement backlog.
Make responsible AI controls usable by delivery and operations teams
Governance scope is tailored to the use cases, risk tier, industry obligations, agent capabilities, data sensitivity, and operating model.
Discuss responsible AI controls