Alankit operates across financial services, e-governance, and healthcare. How do you architect a unified data governance framework across such diverse verticals?
At Alankit, data governance is a core strategic priority that enables seamless operations across our diverse verticals. Given the scale and diversity of our services, we focus on building a governance framework that ensures consistency, compliance, and interoperability, while still allowing flexibility for sector-specific requirements.
Our approach is built around the following key pillars:
Governance Model
- We employ a federated governance architecture, with centralised policymaking and execution devolved to individual business units.
- This model secures consistency of principles whilst allowing operational flexibility across diverse sectors.
- Governance is regarded as a strategic capability, directly connected to business outcomes and digital transformation.
Siteshwar Srivastava, Chief Technology Officer, Alankit Limited
Standardisation and Enterprise Controls
- We establish organisation-wide standards for data classification (sensitive, critical, public), security protocols, lineage tracking, and compliance requirements.
- Common governance policies are defined to serve as a baseline across all verticals.
- Regulatory alignment is maintained across sectors including BFSI, healthcare, and public services.
Domain-Level Customisation
- Each vertical applies governance through domain-specific frameworks, tailored to regulatory and operational requirements.
- We enable context-aware controls, ensuring relevance without compromising enterprise standards.
Technology and Interoperability
- We invest in shared governance platforms, including data catalogues, quality tools, and access control systems.
- APIs and integration layers are used to ensure seamless data exchange across systems.
- Interoperability and data consistency are promoted across applications and platforms.
Accountability and Organisational Alignment
- Clear roles are defined: data owners (accountable), data stewards (operational), and custodians (technical).
- A central governance council provides oversight and enforces policy.
- Governance is embedded into business workflows, KPIs, and performance metrics.
This discipline is especially critical given our role in managing high-volume citizen and financial transactions across platforms.
With increasing scrutiny around data privacy, how does Alankit balance data accessibility with strict ownership rights?
At Alankit, we view data accessibility and privacy not as competing priorities but as complementary principles that must be designed to work in harmony. Our approach centres on enabling secure and responsible data usage, while ensuring that ownership and user rights are safeguarded at all times. This begins with a privacy-by-design and privacy-by-default philosophy, where data protection is treated as a core architectural principle from the outset, never as an afterthought.
Access is governed through granular controls, including role-based (RBAC) and attribute-based (ABAC) mechanisms, with the principle of least privilege enforced to ensure users access only what is strictly necessary.
To protect sensitive data, we apply end-to-end encryption both at rest and in transit, alongside masking, tokenisation, and anonymisation techniques that safeguard personally identifiable information while still enabling secure data sharing. Our security systems and controls are continuously strengthened in line with evolving regulatory and cybersecurity standards across financial services and e-governance.
Consent management systems are integrated into our platforms, giving users meaningful control over how their data is used, while audit trails and documentation are maintained to reinforce accountability and ensure compliance with evolving data protection regulations.
Underpinning all of this is a commitment to real-time monitoring for unauthorised access or anomalies, regular audits, risk assessments, and compliance reviews, all of which form the foundation of the transparency and trust we work to build with our customers and stakeholders.
As Alankit integrates AI into its services, how do you ensure that data feeding these systems is accurate, unbiased, and well-governed?
As we progressively integrate AI into selected services, we recognise that the quality and governance of data directly shape outcomes. Our priority is to ensure that the data driving these systems is reliable, transparent, and aligned with principles of responsible use. This is achieved through a structured approach built on the following pillars:
Data Quality and Integrity
- End-to-end data quality pipelines are established, covering validation, cleansing, normalisation, and deduplication.
- Data is continuously monitored for accuracy, completeness, and consistency before being fed into AI systems.
Traceability and Lineage
- Full data lineage is maintained, enabling traceability from source to output systems.
- Auditability of data transformations is ensured, enhancing transparency and accountability.
Bias Mitigation and Fairness
- Diverse and representative datasets are used to minimise systemic bias.
- Periodic reviews and testing are conducted to identify and reduce unintended outcomes.
- Datasets and processes are continuously refined to improve fairness and reliability.
Human Oversight and Controls
- Human-in-the-loop frameworks are implemented, particularly for high-impact or sensitive decision-making scenarios.
- Review and escalation mechanisms are established for outputs generated by automated systems.
AI Governance Approach
- Governance is embedded across the AI lifecycle — from data preparation through deployment and monitoring.
- Documentation, version control, and approval workflows are maintained.
Responsible AI principles guide our approach, ensuring transparency, accountability, and regulatory compliance.
How do you integrate legacy systems into modern governance architectures?
Given the scale and history of our operations, legacy systems remain an integral part of our ecosystem. Our approach is to modernise these systems in a structured, non-disruptive manner, while bringing them under a unified governance framework. Rather than pursuing wholesale system replacement, we follow a phased, risk-based transformation approach that prioritises incremental modernisation to avoid operational disruption.
To connect legacy systems with modern platforms, we employ APIs, middleware, and microservices, creating a bridging layer that enables seamless data flow without requiring changes to core legacy infrastructure.
Data standardisation is equally central to this process. Legacy data is carefully mapped and transformed into a unified enterprise data model, ensuring consistency, accuracy, and compatibility across systems. Modern governance policies like covering security, access, and data quality, are extended to legacy environments, with these systems required to adhere to current compliance and audit standards.
Where direct integration is limited, we rely on data synchronisation, replication, and staging techniques to maintain continuity. Looking ahead, systems are continuously evaluated for upgrade, optimisation, or phased decommissioning, always with a careful balance between cost efficiency and the long-term scalability and governance maturity that our operations demand.
What are the biggest challenges Alankit has faced in implementing governance frameworks at scale?
Implementing data governance at scale is as much an organisational challenge as it is a technological one. It demands alignment across teams, clarity of ownership, and a sustained focus on cultural transformation. The key challenges we have addressed include:
Cultural Transformation
- Driving a shift from viewing data as a by-product to recognising it as a strategic asset.
- Encouraging ownership, accountability, and data-driven thinking across teams.
Organisational Alignment
- Aligning multiple stakeholders, departments, and leadership priorities.
- Managing differing levels of maturity and readiness across verticals.
Ownership and Accountability
- Defining and enforcing clear roles and responsibilities.
- Eliminating ambiguity in data ownership and stewardship.
Operational and Technical Complexity
- Integrating diverse systems, including legacy infrastructure.
- Managing large-scale, distributed data environments.
Balancing Governance with Agility
- Ensuring governance frameworks do not impede innovation or business operations.
- Maintaining the right balance between control and flexibility.
Execution and Adoption Strategy
- Addressing challenges through strong leadership commitment, phased implementation, and measurable KPIs.
- Investing in continuous training, awareness programmes, and governance tools.
- Driving ongoing monitoring and improvement cycles.
How do you see the future of data ownership evolving in India over the next decade?
India’s digital ecosystem is evolving rapidly, and we anticipate a clear shift towards more user-centric models of data ownership over the next decade. Individuals will gain greater control over their personal data through consent-based frameworks, driven by rising awareness and growing demand for transparency and data rights. This will be supported by the expansion of secure, interoperable digital public infrastructure and broader adoption of data-sharing frameworks across sectors.
At the same time, the emergence of decentralised identity systems and verifiable credentials will gradually reduce reliance on centralised data storage models, fundamentally changing how identity and data are managed.
For organisations, this evolution will mean transitioning from a position of data ownership to one of custodianship and stewardship, with heightened accountability for ethical data usage and protection.
Strong governance will increasingly become a key differentiator for brand trust and reputation, and regulatory compliance will evolve from a baseline requirement into a genuine strategic advantage. Ultimately, the organisations that will lead in this new landscape are those that prioritise user empowerment, transparency, and secure data exchange, because long-term success will depend not just on technological capability, but on responsible innovation, governance maturity, and the ability to sustain trust over time.
-Author is Siteshwar Srivastava, Chief Technology Officer, Alankit Limited
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