AI use and exposure assessment
Which AI services and agents are in use, by whom, and with what kind of data, including regulated Indian identifiers.
Capability 03
AI Security
Adopt AI with policy, assurance and evidence. We help enterprises see where AI is used, decide what it may do with which data, govern the agents that act, and prove it to a regulator.
- Delivered as
- Assessment, programme or with Rhinexa Niyantran
- Works with
- CISO, risk, data protection and engineering leaders
- Related perspectives
- AI agents need identity, policy and evidence
- Banning is not a strategy. It is a confession
- AI security is a loop, not a checklist
The challenge
AI has moved from answering questions to taking actions.
Staff paste customer data into assistants, developers connect coding agents to repositories and pipelines, and business units adopt AI features inside the SaaS they already use. Each is useful. Each also creates a non-human actor with access, context and, increasingly, the authority to act.
Banning is not a strategy. Governed adoption is: knowing what is in use, bounding it with identity and policy, authorising sensitive actions before they execute, and keeping evidence that stands up to review.
What we deliver
Offerings.
Take one offering on its own, or combine them into a programme with a named lead and agreed exit criteria.
AI governance framework and policy
Roles, acceptable use, data boundaries and approval paths, mapped to the DPDP Act and sector expectations. Mapping supports assurance; it is not certification.
Secure AI architecture review
Retrieval, memory, model access and data boundaries reviewed before an AI system reaches production.
Agent action governance
Identity for agents, per-action policy and signed evidence, delivered with Rhinexa Niyantran where enforcement is required.
AI red teaming and evaluation
Prompt injection, data leakage and tool-abuse testing of assistants, agents and the systems around them.
Safe use of coding assistants
Policies, pipeline controls and developer guidance so engineering teams keep the productivity without losing the source code.
Approach
How an engagement runs.
A named practice lead from scoping to close, with deliverables agreed before work starts.
Discover
Establish where AI operates, who uses it and what data it can reach.
Bound
Define data, tool, purpose and autonomy boundaries, and who owns each.
Authorise
Put policy at the moment of action for the systems that can act.
Prove
Retain evidence that supports assurance, audit and incident reconstruction.
Outcomes
What changes for you.
Measured outcomes from real engagements will be published here once clients consent.
You know where AI is used
An inventory of services, agents and data classes, kept current.
Policy where it matters
Regulated data protected and sensitive actions authorised, not assumed.
Evidence for the board and the auditor
Signed, hash-linked records that can be verified offline.
Platform · Rhinexa Niyantran
Govern AI use and agent actions in your environment.
Identity-based policy, regulated-data protection and signed evidence of every decision, deployed where your data lives.
Explore Rhinexa Niyantran
Sample dataQuestions
Frequently asked.
Do we need Rhinexa Niyantran to work with you?
No. The advisory and assessment work stands alone. Niyantran is the platform we bring when you need enforcement and evidence in your own environment.
Which AI tools can you govern?
Prompt and data governance covers the supported tools in our register. Per-action enforcement of a coding agent is live today for Claude Code and Codex, with other agents on the roadmap; we say exactly which is which.
Does this make us compliant with the DPDP Act?
No product or service can. We map controls and evidence to the obligations; your assurance and your auditors decide.
Can you assess a local AI environment, not just SaaS assistants?
Yes. Self-hosted models, retrieval pipelines and internal agents are often the least governed part of the estate, and the assessment covers them.