Prompt and data security
Prompt injection checks, sensitive-data handling, source controls, retrieval boundaries, and approved system instructions.
Responsible AI governance
Control AI risk before agents touch sensitive workflows
DigiScience helps enterprises define responsible AI controls for prompt security, agent governance, human approval, hallucination risk management, model usage, audit trails, and ongoing observability.
Governance is not paperwork
Controls must be embedded into AI workflows, approvals, monitoring, and operating cadence.
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.
Recommended package: AI Pilot Growth or Governed AI Platform
Governance is especially important for legal, healthcare, BFSI, insurance, HR, and public sector AI workflows.
Discuss responsible AI controls