In the era of Generative AI (Gen-AI), data sovereignty has become the basis of digital trust management. In the simplest terms, it is the idea that data is subject to and will be governed by the laws of the country where it was generated. In terms of Gen-AI, it means that digital data is governed by the legal and regulatory principles of the country where the data physically resides. Organisations that train or operate such AI systems must navigate a labyrinth of laws and industry-specific regulations, such as RBI’s FREE AI (Framework for Responsible and Ethical Enablement of Artificial Intelligence) Report. There is no doubt that the stakes are high. Training datasets require large amount of data to be fed into AI models and may contain sensitive information. These AI models may inadvertently expose such information.  

Adding to this complication are autonomous agents. They are advanced AI programs that can, through chain of thought logic, break down complex tasks into smaller subtasks and perform them independently. Unlike generic chatbots, these agents continuously learn and adapt to circumstances and can handle multi-step tasks across compliance, finance, healthcare, and customer service, among other domains. The momentum in this sphere is building at a rapid pace. According to the 2024 NAVEX State of Risk and Compliance Report, 56% of organisations across industries, plan to use gen AI within the next 12 months, and according to Cloudera, 96% plan to expand their use of AI agents. 

However, this momentum collides with what privacy professionals call the ‘Privacy Paradox’. The Privacy Paradox is the tension between the demand for data-driven personalised services and the need for data protection. A sense of convenience, personalisation, and a lack of transparency of data use often push companies down a path of risky data practices. However, breaches such as this can result in massive fines, damage to reputation, and loss of consumer trust. 

Gen AI and Autonomous Agents: New Landscape for Data Control 

Aside from Large Language Models (LLMs), the Gen-AI ecosystem also includes decision engines, which are systems that streamline the decision-making process by determining how larger outcomes can be achieved through a series of smaller decisions, and robotic process automation (RPAs), which automate repetitive office tasks through APIs and UI scripting. 

These systems do not exist in isolation. Through communication with AI agents, personal information inevitably gets disclosed, and this could range as widely from product information to the food the user has for dinner. Autonomous agents pull data from internal databases, external APIs, cloud platforms, and real-time user interactions to create large data flows. As agents operate globally, they may process data across multiple jurisdictions simultaneously. So, an agent can pull Personal Data from India, analyse it on a cloud server in Germany, and deliver insights to teams in the US. 

For multinational enterprises, the rise of autonomous agents is a double-edged sword. On one hand, they could provide significant benefits through process automation, compliance monitoring, better customer service, and deeper data insights. On the other hand, they expose enterprises to the ever-present question of accountability for autonomous decision-making. 

3. Legal Frameworks and Compliance Requirements

Enterprises deploying autonomous agents must ensure that they comply with the key laws and regulations around data sovereignty. The two legislations in Europe are the European Union Artificial Intelligence Act (EU AI Act) and the General Data Protection Regulation (GDPR), while in India, the applicable law is the Digital Personal Data Protection Act, 2023 (DPDP Act).  

The EU AI Act, effective from July 2024 with phased enforcement through 2027, applies to any AI system that is placed on the EU market, is used within the EU, or impacts EU citizens, notwithstanding where the provider is based. Autonomous agents, whether multi-step executors, RPA systems, or decision-making engines, all fall under the Act’s definition of AI systems. Depending on their usage and application, such agents may be treated as minimal risk or limited risk AI for low-stakes tasks such as email sorting or spam filtering, or high-risk AI if deployed in sensitive domains such as healthcare, finance, jobs, credit, or recruitment. 

The GDPR is the backbone of European data protection, and autonomous agents must align with it alongside the AI Act. What it means for autonomous agents is that they must establish a lawful basis of data use, comply with restrictions on cross-border data transfers, and provide individuals against fully automated decision making. So, while the AI Act governs how the system is built and operated, the GDPR ensures data sovereignty and individual rights. 

In India, the DPDP Act establishes a consent-based system for processing digital personal data and applies both domestically and extraterritorially. Under Section 10 of the Act, certain enterprises deploying significant AI systems may be designated as Significant Data Fiduciaries based on factors such as the volume and sensitivity of data processed, risk of harm to individuals, national security, or public order and attract additional obligations like audits and appointment of Data Protection Officers (DPO). The Act also empowers the government to control Cross-Border Data Transfers through a whitelist/blacklist mechanism. Alongside this, the IT Rules 2021 and the forthcoming Digital India Act are expected to add AI-specific accountability requirements.  

 Aside from these, organisations should also look to international AI governance benchmarks such as the NIST Artificial Intelligence Risk Management Framework (AI RMF), a voluntary and lifecycle-based guide to integrating trustworthiness into AI systems, and ISO/IEC 42001, the world’s first AI Management System standard, which provides a structured way to manage risks and opportunities associated with AI and to establish and continually improve AI governance across an organization. 

The Privacy Paradox in Autonomous Agents 

Autonomous agents pose a challenge to traditional data governance as they make decisions in real-time and without oversight. They do not follow fixed workflows, but decide for themselves what data to access, how to process it, and for how long to retain it. While this autonomy certainly increases efficiency, it makes a centralised form of oversight vulnerable. 

This is where the core of the Privacy Paradox exists. Agents provide the personalisation and automation that customers want, but they require intensive data collection. The stark paradox is that 71% of consumers say they would break business ties with a company if it gave away sensitive data without their permission. Yet, many users continue to share data with AI systems for the sake of their convenience or without realizing how their Personal Data may be used for training of AI Models. 

The same can be explained through industry specific examples, such as: 

• Healthcare AI Agent: Offers life-saving features such as medication reminders, care coordination, but requires access to detailed patient records and data sharing across providers. Patients want comprehensive and personalised care, but worry about surveillance and their health information being processed by AI systems. 

• Financial Services AI Agent: Detects fraud, provides personalised financial advice and improves customer service based on its analysis of the user’s spending patterns, financial behaviours and transaction history. However, consumers may be uncomfortable with the extent of financial surveillance and automated decision-making that goes into this process. 

In both cases, the benefits of autonomous agents come with privacy and compliance risks, which require companies to strike a balance between innovation and accountability. 

Technical Solutions and Architectural Strategies 

Mere reliance on policies will not be sufficient in the age of autonomous agents. Organisations must adopt technical security measures to ensure data sovereignty in autonomous agent deployments. Privacy-Enhancing Technologies (PETs), thus, provide the foundation for this: 

• Federated Learning allows organisations to train models collaboratively without having to transfer raw data to a central location. Each client device/organisation keeps its own data and only shares model updates. This approach adopts the principle of Data Minimisation and helps mitigate many systemic risks compared to traditional, centralised machine learning. This makes it a much more practical choice for large enterprises operating across multiple jurisdictions. 

• Differential Privacy is a rigorous mathematical definition of privacy. It means that when an algorithm analyses a dataset, by looking at the output, one cannot tell whether any individual’s data was included in the original dataset or not. The results (like averages or medians) look almost the same whether an individual’s data is there or missing. In other words, a differentially private algorithm works almost the same whether an individual’s data is in the dataset or not, as the output could just as easily have come from either case. 

• Encryption-in-use refers to the practice in cybersecurity wherein in-use data, i.e., files that are actively being updated, read, accessed or processed, is encrypted. It provides security to data during processing and prevents it from being exposed in plaintext form. It allows computations to run directly on encrypted data and minimises the risk of attacks in sensitive workflows such as healthcare and finance. 

• Secure Multi-Party Computation enables multiple entities to jointly perform computations without exposing their private data. For example, banks can work together to detect fraud patterns while maintaining the confidentiality of their customer datasets. 

• Anonymisation techniques are gaining prominence as well. Organisations like Meta are increasingly promoting methods to strip personal identifiers from training data, either by generalising or removing identifiable elements, so that AI models can learn from useful patterns without retaining sensitive data that can be traced back to any individual. 

These techniques are consistent with Privacy-by-Design principles, specifically: 

• Purpose Limitation: Agents only use data for the stated purpose. 

• Storage Limitation: Data is only kept for the required period. 

• Use Limitation: Prevent agents from secondary use without consent. 

Operationalising Data Sovereignty in Gen-AI Deployments  

Ensuring data sovereignty in Gen-AI is the need of the hour. It requires the incorporation of privacy principles into the design, deployment, maintenance and monitoring of AI agents. Unlike traditional IT systems, autonomous agents constantly adapt, which makes governance a dynamic and evolving domain. This requires specialised consulting experience and strong organisational controls. Data Privacy Consultants must operationalise compliance by addressing AI-specific risks, and conventionally existing protocols must be extended to AI agents. 

Gap Analysis evaluates whether existing governance measures cover machine learning pipelines, model training data, and cross-border agent workflows, among others, and reveals blind spots like missing data lineage or the absence of AI explainability mechanisms. Risk Assessments examine AI-driven threats such as inference attacks, model inversion, algorithmic bias and discriminatory outcomes, exacerbated by autonomous agents’ dynamic learning. Policy Development focuses on AI-specific governance, such as consent protocols for continuous learning, minimisation rules for training data sets, vendor monitoring for third-party AI models, and multi-jurisdictional compliance. Incident Response Planning addresses uncontrolled agent behaviour, outputs, or failures in federated learning.  

Aside from consultation, enterprises must establish AI-specific controls over agent-generated outputs. Since recommendations or content generated by agents can directly impact customers, regulators, and reputation, focused monitoring is essential: 

• Output classification and review: Agent outputs should be classified based on risk, with automated scanning for sensitive data leaks and human review for high-risk decisions. 

• Quality assurance processes: Regular validation of model accuracy, detection of bias, and monitoring compliance with interpretability standards are essential. 

Data sovereignty in Gen-AI has now become central to digital trust and organisational resilience. Autonomous agents bring hitherto unprecedented opportunities for efficiency and automation, but they also deepen the Privacy Paradox as they constantly require access to Personal Data. Organisations are left with two choices; either embed sovereignty and accountability into every layer of AI design, or risk fines, reputational damage, and regulatory scrutiny across multiple jurisdictions. 

The way forward for leaders and privacy teams is action vis a vis the deployment of AI systems within their organisations. AI governance should be prioritised at the executive level, i.e., transparency must be demanded in data flows, and investments should be made in privacy-oriented systems. Privacy and compliance teams should run continuous risk assessments, align incident-response to global timelines, enforce purpose and storage limits, and monitor agent outputs for bias or data leaks. By making data sovereignty a lived practice, one that is monitored, updated, and enforced in real-time, enterprises can achieve compliance while leveraging the transformative power of autonomous agents.