Trust becomes infrastructure.
For DPOs, compliance heads, CISOs and CDOs at banks, NBFCs, fintechs, hospitals, edtech companies and government departments: privacy, consent, data governance, AI governance and vendor risk run on one system of record — Avisha Trust — so compliance is a continuous state, not an annual scramble.
Most organisations do trust work. Few can show it.
Privacy notices get written, consent boxes get added, vendors get questionnaires. Then the evidence scatters across inboxes, spreadsheets and screenshots — and the next audit, regulator query or enterprise customer starts the reconstruction all over again. If two or more of these describe your organisation, it is time to treat trust as infrastructure rather than a project.
Sound familiar?
- DPDP readiness lives in a spreadsheet three people have edited and nobody owns.
- You cannot say, today, which systems hold a given customer’s personal data.
- Consent was collected in a form somewhere; withdrawal requests go to a shared mailbox.
- Marketing, product and a vendor each hold a copy of the same customer record with different retention rules.
- An AI model went live and nobody recorded who approved it, what data trained it or who monitors it.
- Vendor due diligence is a questionnaire sent once at onboarding and never revisited.
- Every audit starts from zero: a week of collecting screenshots and emails as evidence.
One system of record for privacy, consent, governance and evidence.
Avisha Trust is our Digital Trust platform. It holds the inventories, registers, consent records and evidence that prove how your organisation handles personal data and AI — so the answer to “show me” is an export, not a project.
- Personal data inventory and data-flow map across systems, teams and vendors
- Consent capture, enforcement and full audit history per data principal
- AI system registry with owner, purpose, data lineage and governance status
- Vendor risk register with tiering, review cadence and open actions
- Evidence store: dated, exportable, mapped to the frameworks you report against
Illustrative preview of the Avisha Trust dashboard.
Know every place personal data lives — and what you promised about it.
Privacy management starts with an inventory you can defend: which systems hold which categories of personal data, for which purpose, on which legal basis, for how long. From that record flow the notices, the retention schedule and the ability to answer a data principal within the SLA you committed to.
- Data discovery and inventory across core systems, SaaS tools and file stores
- Records of processing with purpose, basis, retention and owner per activity
- Privacy notice drafting that matches what the systems actually do
- Data principal request workflow: access, correction, erasure, grievance — with tracked SLAs
- Breach-response playbook aligned to DPDP notification duties
Consent captured at the point of collection, enforced downstream, provable later.
A tick-box is not consent management. Consent has to be purpose-specific, recorded with its notice, honoured by every system that uses the data, and withdrawable as easily as it was given. We design that flow and Avisha Trust keeps the record.
- Consent design for onboarding, KYC intake, payments, apps and call-centre scripts
- Consent records: who, when, which notice version, which purposes
- Withdrawal workflow with a defined SLA and downstream propagation to marketing, analytics and vendors
- Consent for children and other higher-duty categories handled as distinct flows
- Audit history per data principal, exportable on request
Data, AI and vendors — governed from the same record.
Each module is a service we run and a register Avisha Trust maintains. Start with one; the others plug into the same inventory.
Data Governance
Ownership, classification and retention for the data you hold, so privacy commitments are enforceable rather than aspirational.
- Data classification scheme and owner assignment per dataset
- Retention and deletion schedule with evidence of execution
- Access policy review for high-sensitivity data
- Cross-border and sharing register
AI Governance
A register of every model and agent in use, who owns it, what data it touches and how it is monitored — the foundation for responsible AI you can evidence.
- AI system inventory with purpose, risk tier and approver
- Policy set: acceptable use, human oversight, model change control
- Model access logs and monitoring evidence linked to each system
- Hand-off to AI security testing for high-risk systems
Vendor Risk
Your data processors are your exposure. Tier them, assess them on a cadence and keep the evidence where the auditor can find it.
- Vendor register with data categories shared and processing purpose
- Risk tiering that sets review depth and frequency
- Assessment questionnaires, evidence collection and follow-up tracking
- Contract clause checklist for processor obligations
Start with one module, not three. For most organisations the thinnest register is AI governance — models went live before anyone decided who approves them or what data they touch.
Read: building an AI system registerTrust becomes infrastructure when it runs all the time.
Payments and identity work because they are always on and nobody rebuilds them for each journey. Trust should behave the same way: a live inventory, consent enforced by systems, governance decisions recorded as they happen, and evidence that accumulates instead of being reconstructed.
- Continuous, not annual — posture is known on any given day
- Evidence by default — every action leaves a dated record
- One record across privacy, security and AI — no parallel spreadsheets
Compliance as a project vs trust as infrastructure.
Compliance as an annual project
- Data inventory is a spreadsheet that is stale within a quarter
- Consent is collected but not enforced by downstream systems
- AI systems go live without a recorded owner or approval
- Vendor assessments happen once, at onboarding
- Evidence is rebuilt for every audit and every enterprise customer
Trust as infrastructure with Avisha Global
- Living inventory and data-flow map, reviewed on a cadence
- Consent records that marketing, analytics and vendors actually honour
- AI registry with approver, data lineage and monitoring status
- Vendor tiers that set review depth and frequency
- Evidence exported in minutes, mapped to the framework asked for
If you want a number before a conversation: the DPDP gap assessment scores 18 questions across seven areas in your browser. No email, nothing sent anywhere, prioritised action list at the end.
Run the free gap assessmentSix deliverables — each one lives in Avisha Trust after the engagement.
Personal data inventory & flow map
Systems, categories, purposes, bases, retention and owners — and how data moves between them.
Consent framework
Notice templates, consent-record schema, withdrawal workflow and propagation rules.
Records of processing & retention schedule
Per-activity records with a deletion schedule your systems can execute.
AI system register
Every model and agent with owner, purpose, risk tier, approval and monitoring status.
Vendor risk register
Tiered processors, assessment results, open actions and contract clause coverage.
Evidence pack
Dated, exportable evidence mapped to DPDP obligations and the frameworks you report against.
Typically 8–12 weeks to a working system of record, then a quarterly rhythm.
Week 1–2 · Discover
Stakeholder interviews, system scan and a first-pass inventory. Priority gaps identified.
Week 3–5 · Map & design
Data flows, consent design for each collection point, governance policies and vendor tiers agreed.
Week 6–9 · Configure
Avisha Trust loaded with the inventory, consent schema, AI registry and vendor register. Teams trained.
Week 10–12 · Operate
First data principal request, first vendor review and first evidence export run end to end with your team.
Quarterly · Review
Posture review, inventory refresh, new AI systems registered, evidence pack updated ahead of audits.
A platform we build, a regulatory context we work in, and one team across AI, security and compliance.
Product-backed, not slideware
The engagement ends with a live system of record in Avisha Trust, not a binder. The registers keep working after the consultants leave.
Built for the Indian regulatory context
DPDP obligations, RBI and SEBI expectations for regulated entities, and sector rules for health and education shape every template we use.
Privacy, security and AI in one team
The same people who map your data also test your APIs and assess your models, so governance decisions reflect how the systems really behave.
Two ways in.
DPDP Compliance programme
A structured readiness assessment, data mapping and consent framework aligned to the Digital Personal Data Protection Act, 2023 — the most common starting point for Indian organisations.
Explore DPDP ComplianceAvisha Trust Seal
A visible mark for organisations that complete a defined privacy, security, AI governance or DPDP readiness assessment. Not a government or statutory certification — a signal backed by an actual assessment.
Explore the Trust SealBring the question you cannot answer today — which systems hold one named customer’s data, who approved the model in production, when vendor 14 was last reviewed. We will show you what it takes to make that answer an export.
Talk to a Digital Trust specialistFrequently asked questions
Make trust measurable.
Bring one regulator question you cannot answer today. We will show you how it becomes an export.