As autonomous AI systems reshape enterprises, India’s privacy strategy enters a decisive new phase—where governance, transparency, and identity control become the foundation of national digital trust.
On a crisp January morning, as India marked Data Privacy Day, the country found itself at a turning point. For years, data protection had lingered on the edges of policy conversations important, but rarely urgent. Compliance audits were performed, privacy notices were written, and regulators occasionally intervened. Yet underneath the surface, a profound shift had begun.
The arrival of agentic AI systems that learn, adapt, and act autonomously has rewritten the rules of digital risk. And India, a nation simultaneously scaling digital infrastructure and AI innovation at breakneck speed, is now forced to confront a new reality: data privacy is no longer a checkbox. It is the backbone of trust in the digital economy.
This is the story of India’s data privacy evolution fueled by legislation, shaped by cyber threats, and accelerated by enterprises awakening to the new era of AI governance.
“Privacy isn’t optional anymore it is the core infrastructure for responsible AI.”
— Peter White, Chief Product Officer, Automation Anywhere
The Rise of the Autonomous Agent
For decades, automation advanced in linear steps scripts became bots, bots became workflows, and workflows eventually became intelligent assistants. But the last two years have introduced a seismic shift: the rise of autonomous digital agents.
These agents can execute tasks, make decisions, analyse sensitive datasets, and even trigger actions across financial, healthcare, and enterprise platforms without human intervention.
Peter White, Chief Product Officer at Automation Anywhere, has seen this transformation first-hand. His warning is unambiguous. “As agentic systems operate with increasing autonomy and process unprecedented volumes of sensitive data, privacy is no longer optional. It is core infrastructure for responsible AI and automation.”
“You can’t build trusted AI on opaque data or unclear ownership.”
— Maurizio Garavello, SVP, APJ, Qlik
In India where digital public infrastructure, banking systems, telecom networks, and e‑commerce platforms generate enormous datasets the stakes are especially high. Autonomous systems are not just tools; they are becoming decision-makers.
White adds: “Embedding security and privacy by design at the architectural level will define which systems can scale from pilot to production. When privacy is built‑in, organizations can move fast without breaking trust.”
Companies are listening. Indian CIOs are pushing for encryption at rest and in motion, differential privacy layers, and stricter controls on data used for training AI models. The philosophy is simple yet transformative: If data is the fuel of AI, then privacy is the engine oil without it, the entire machine seizes.
The Trust Layer India Can No Longer Delay
As AI becomes more sophisticated, enterprises are realizing something uncomfortable: they cannot secure what they cannot see. Shadow data, unclassified datasets, unclear ownership models, and outdated governance frameworks create massive blind spots.
Maurizio Garavello, SVP for Asia Pacific & Japan at Qlik, puts it bluntly: “As AI becomes more autonomous, data privacy stops being a compliance checkbox and becomes a design principle.”
He goes further, articulating the essential dilemma: “You can’t build trusted AI on opaque data or unclear ownership. Organisations must know where data lives, who can act on it, and how decisions are governed especially as agents start acting on their behalf.”
“Governance must now extend to digital identities acting independently.”
— Bernard Montel, Field CTO, Tenable
This “trust layer” is emerging as one of the most important pillars of India’s privacy strategy.
Across sectors, organisations are now investing in:
- data lineage systems to track the life cycle of data
- real‑time governance layers for AI models
- metadata intelligence tools to classify sensitive information
- role‑based access controls for human and machine identities
For Indian enterprises, the message is clear:
No trust = no scale.
No transparency = no AI.
When Cybercriminals Weaponise AI
While enterprises innovate, attackers innovate faster.
Over the past year, India has seen unprecedented growth in AI-generated scams deepfake extortion attempts, automated phishing campaigns, synthetic identity fraud, and micro-targeted social engineering that bypasses traditional security tools.
“Privacy and cybersecurity rise or fall together real protection demands real‑time visibility.”
— Drew Bagley, VP & Counsel, CrowdStrike
Bernard Montel, Field CTO at Tenable, has been tracking this evolution. “Protecting personal data is now about more than compliance; it’s about defending freedom and privacy.”
He warns that AI is making attacks not just more frequent, but more dangerous. “With cybercriminals weaponising AI, attacks are becoming faster, smarter, and harder to detect.”
At the same time, organisations are rolling out autonomous systems internally.
This creates a new kind of risk: digital identities that act independently within sensitive systems.
Montel emphasises that governance must evolve: “Effective governance now demands visibility into machine behaviour, not just human access.”
Identity governance once a mundane backend function has become the frontline.
Enterprises are moving toward:
- machine identity management
- behavioural baselining for AI agents
- cryptographically verifiable identity controls
- continuous authentication for automated systems
And Montel’s final recommendation is becoming an industry mantra: “Compliance should be the baseline, with prevention and resilience built in from day one.”
When Privacy and Cybersecurity Converge
Traditional enterprise structures separate privacy, cybersecurity, and technology development. But AI has changed that forever.
Automated systems move data continuously across clouds, workflows, and applicationsoften in milliseconds. Manual oversight is impossible.
Drew Bagley, VP & Counsel for Privacy and Cyber Policy at CrowdStrike, argues that the lines between privacy and cybersecurity have fully dissolved. “Privacy and cybersecurity rise or fall together, and those strategies must always be aligned.”
He points out that organisations now operate in a world of constant data fluidity. “AI is driving workflows across the enterprise. With continuous data movement, we almost take for granted the new paradigm of access and sharing.”
But the real danger lies in this complacency.
Bagley underscores the three pillars that now define effective protection: “Real protection depends on visibility, privacy by design, and resilience that operates in real‑time.”
This means enterprises must embrace:
- unified data maps
- real‑time AI monitoring
- privacy‑enhancing technologies (PETs)
- zero-trust frameworks
- threat intelligence integrated into privacy operations
The future belongs to companies that can fuse privacy and cybersecurity into a single discipline.
India’s National Strategy Is Taking Shape
With the Digital Personal Data Protection Act (DPDPA) laying the groundwork, India is now building a broader privacy ecosystem—one that supports:
- secure digital public infrastructure
- AI-driven governance tools
- cross-border data flow frameworks
- trusted data layers for AI models
- multi-cloud and hybrid infrastructure protection
India’s privacy strategy is informed by a single insight emerging from industry leaders: Privacy is not a regulatory burden it is a competitive advantage.
Organisations that embed privacy into their architecture gain:
- faster AI adoption
- better customer trust
- stronger resilience
- reduced legal risk
- global interoperability
Those who treat privacy as a “box to tick” will fall behind.
The Road Ahead What India Must Build Next
As India accelerates toward a trillion‑dollar digital economy, its privacy strategy must address emerging realities:
1. Governance for Human and Machine Identities Alike
As digital agents proliferate, identity governance must expand far beyond user access.
2. Autonomous Privacy Controls
Real‑time, AI-driven privacy systems will become essential.
3. Transparent Data Ecosystems
Clear data ownership, lineage, and classification will determine AI readiness.
4. Privacy by Design Enforcement
Not optional mandatory for scale.
5. National Trust Architecture
Interoperable, regulated, and future-proof.
India is not just catching up it is shaping a global model where privacy, innovation, and autonomy can co‑exist.
Conclusion
As autonomous AI becomes woven into the fabric of Indian enterprises, the country stands at a historic crossroads.
The experts agree on one thing: privacy is no longer a legal formality it is the foundation of digital trust.
When AI systems act on behalf of organisations, when decisions are automated, and when data moves continuously, trust becomes the most valuable asset.
And India’s future its digital economy, its AI ambitions, and its global competitiveness will depend on how well it protects this trust.
Privacy is no longer part of the story.