Digital trust is built in enterprises when identity verification, document workflows, consent and transaction processes work together reliably. In an exclusive conversation Abhinav Parashar, Co-founder & CEO, Digio informs Bhavya Bagga, Business Reporter, APAC Media & CXO Media that Digio’s approach is to make digital interactions simpler for users while helping enterprises establish greater confidence in the identities, information and transactions they handle.
With the DPDP Consent Manager milestone approaching, what are the key technology challenges enterprises need to address to move from compliance planning to implementation?
The biggest challenge for enterprises is that consent cannot be implemented in isolation. Before building a consent management layer, organizations need a clear understanding of what personal data they collect, where it resides, how it moves across systems, and which internal teams or third parties can access it.
Many businesses operate with fragmented technology stacks comprising onboarding platforms, CRMs, payment systems, cloud applications and legacy databases. Establishing a reliable connection between consent records and these different systems is therefore a significant undertaking. Another challenge is managing consent throughout its lifecycle, including recording permissions, enabling changes or withdrawals, and ensuring that the corresponding processing activities are addressed across relevant systems.
The November 2026 milestone relates specifically to the registration and obligations of Consent Managers under the DPDP framework. Enterprises should use this as an opportunity to advance their broader implementation preparedness, rather than treating it as the deadline for all consent-related obligations.
Our view is that organizations should begin with data discovery and classification, followed by data-flow mapping, purpose identification and system integration. Technology can make these processes more systematic and auditable. Ultimately, the objective is not simply to collect consent, but to establish a demonstrable link between the permission given, the purpose of processing and the actual use of personal data.
How can organizations integrate consent management into their existing technology systems to track consent, manage withdrawals and maintain compliance across internal platforms and third-party service providers?
Consent management needs to be designed around an organization’s existing data ecosystem, rather than treated as another standalone application. The first step is to identify the systems that collect, store, process or share personal data, including onboarding platforms, CRMs, payment applications, analytics tools and external service providers. This is where data discovery and consent management need to work together.
Data discovery helps organizations understand where personal data resides, how it flows across systems and who has access to it, while consent management establishes the permissions governing its use. When these capabilities are connected, organizations can build a more robust audit mechanism to map consent against actual data processing activities and identify potential gaps between the permissions obtained and how personal data is being used.
Digio’s CoTrust can be positioned in this context, bringing consent management into the broader data protection and compliance workflow. Organizations can then establish a centralised consent record that captures the relevant permission, the purpose for which it was obtained, the associated notice, and any subsequent changes or withdrawals.
Through APIs and workflow automation, this record can be connected to the systems where the corresponding data processing takes place. Withdrawal management is particularly important. When an individual withdraws consent, the organization must be able to identify the affected processing activities, communicate the change to relevant systems and processors, and maintain records of the action taken. Processing that must continue under another applicable legal basis or statutory retention requirement needs to be distinguished from processing that relied on the withdrawn consent.
Third-party integration is equally critical because data does not remain within organizational boundaries. Contracts, technical interfaces and operational processes must work together to support appropriate instructions, acknowledgements and oversight. The goal is to make consent actionable across the data lifecycle.
When data discovery and consent management connect, organizations can move beyond maintaining consent records to building a more traceable, auditable view of how they collect, process, and share personal data. This reduces dependence on manual intervention and helps organizations demonstrate that their stated privacy commitments are reflected in their actual data-handling practices.
As enterprises adopt AI and agentic AI, how can they ensure responsible data usage, protect personal information and maintain accountability across AI-driven processes?
AI and agentic AI create new opportunities for enterprise productivity, but they also make data governance more complex. AI systems may draw on information from multiple sources, while agentic systems can initiate actions across applications with limited human intervention.
Organizations therefore need visibility into the data these systems access, how it is used, and the decisions or actions they generate. A responsible approach begins with data minimization. Enterprises should ensure that AI systems access only the personal data necessary for a defined purpose, supported by appropriate permissions, access controls and retention practices.
As enterprises increasingly use multiple external AI models, they should consider centralizing access through a common gateway. Governance controls at the gateway level, including data masking, sensitive-data filtering and access policies, can help protect personal and confidential information before it reaches external models.
This also provides a consistent point to enforce rules and monitor interactions across different AI services. Organizations also need clear accountability. This includes assigning ownership for AI-driven workflows, maintaining appropriate logs, reviewing system permissions and introducing human oversight for consequential actions.
Where personal data is processed on the basis of consent, organizations must ensure that its use remains aligned with that consent and applicable legal requirements. AI governance cannot be separated from data governance. Limited visibility into data sources, access rights and downstream sharing can create risks, regardless of how sophisticated the AI model is.
Ultimately, responsible AI adoption requires safeguards to be built in from the outset. Enterprises must be able to monitor and govern how data is accessed, processed and shared throughout AI-driven workflows, alongside measuring productivity gains.
Digio works across digital identity, KYC, eSignatures, onboarding and consent management. How is the company using AI and automation to improve digital trust, strengthen fraud prevention and simplify enterprise workflows?
At Digio, our approach is to make digital interactions simpler for users while helping enterprises establish greater confidence in the identities, information and transactions they handle. Digital trust is built when identity verification, document workflows, consent and transaction processes work together reliably.
Automation reduces repetitive manual intervention across onboarding, document processing and verification, helping enterprises improve efficiency while minimizing friction for users. AI can further strengthen these workflows by identifying suspicious patterns, inconsistencies and potential risks that may otherwise go undetected.
Fraud prevention is an important area of focus, particularly as threats evolve to include synthetic identities, deepfakes and forged documents. AI-enabled verification workflows can support synthetic identity checks, deepfake detection and document forgery checks by identifying inconsistencies in identity information, signs of document manipulation and potential attempts to impersonate genuine individuals.
Combining these checks with robust KYC processes and appropriate escalation mechanisms can help enterprises make more informed verification decisions. However, fraud prevention cannot rely on AI alone. It requires reliable data, layered verification, clear decision rules and human intervention where necessary. The objective is to identify potential risks without making legitimate customer journeys unnecessarily cumbersome.
Our focus is on building digital trust infrastructure that connects identity verification, consent and document workflows across enterprise journeys, with privacy and accountability embedded into the process. As enterprises adopt greater automation, the priority is to make digital processes faster and more seamless while strengthening their ability to detect fraud, protect personal information and maintain trust.
Many organizations still lack clear visibility into where personal data is stored and how it moves across their systems. How can technologies such as data discovery, automation and consent management help businesses address this challenge?
Data discovery is the foundation of effective data governance. Organizations cannot reliably manage consent or protect personal information if they do not know where that information resides, how it moves or who can access it. Data may sit across structured databases, cloud applications, document repositories, employee devices and third-party platforms, making manual inventories difficult to maintain.
Technology-led discovery and classification can help identify personal data across these environments and map its movement from collection to storage, access and sharing. This creates a more reliable view of an organization’s data footprint and can support a record of processing activities, including the purpose of processing, applicable legal basis, retention approach and external recipients.
Consent management becomes more meaningful when connected to this visibility. Once an organization understands which data supports which activity, it can better link consent records to the relevant processing, identify gaps and respond more effectively when an individual changes or withdraws consent.
Automation can also help monitor changes, flag unexpected access and route identified issues to the appropriate teams for action. However, technology does not replace the need for clear ownership, sound policies and human oversight.
Our perspective is that consent management without data discovery leaves a fundamental gap. Enterprises need to establish what data they hold and how it is used before they can confidently govern the permissions associated with it. This creates a stronger foundation for privacy, operational accountability and digital trust.
As India’s data protection framework evolves, what technology investments and capabilities should enterprises prioritize over the next three years to strengthen data governance, privacy and digital trust?
Over the next three years, enterprises should prioritize investments that establish visibility, control and accountability across the data lifecycle. The first priority is data discovery and classification: organizations need to identify personal data across applications, databases, documents and third-party environments. Without this foundation, subsequent investments in consent management and privacy automation may address only part of the problem.
The second priority is connecting governance with operational systems. Consent records, purpose information, access controls, retention processes and mechanisms for handling individual requests should work together rather than operate as disconnected tools. Integration with legacy platforms and external processors will be particularly important.
Third, enterprises should strengthen security and identity capabilities. Reliable identity verification, role-based access, appropriate safeguards, audit trails and incident-response processes help reduce exposure and improve accountability. Organizations adopting AI should also invest in data-access governance, monitoring and oversight for automated workflows.
Finally, privacy readiness should be treated as an ongoing capability rather than a one-time compliance project. Businesses need clear ownership across legal, technology, security and business teams, supported by measurable indicators such as data coverage, unresolved governance gaps and the time taken to act on relevant requests.
India’s DPDP framework provides an important impetus for this shift. The organizations that prepare well will be those that embed privacy and trust into how their systems are designed and operated. Over time, these capabilities can support not only regulatory readiness but also stronger customer confidence and more sustainable digital growth.
