Data privacy in the age of Agentic AI
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This article is written by the iPleaders team.

Introduction

AI is not just moving fast but changing shape. You will not find SaaS platforms just offering handy tools that help users click through the task. What seemed futuristic is in the market now. What we are talking about now is agentic AI systems that set goals, make decisions, and act with minimal human oversight.

For instance, it was found by the Irish Data Protection Commission in November 2023 that Microsoft Ireland breached Articles 12 and 17 of the GDPR. Why? They mishandled erasure requests properly and failed to inform complainants of their right to a judicial remedy. If giants like Microsoft can miss on complying with something as fundamental as consent, erasure, and transparency, then this is a wake-up call for every platform building or deploying AI   

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Yet, enthusiasm is hard to miss. In a survey conducted by PwC in 2025, it was found that about seventy-three per cent of executives believe that AI agents are going to give them tough competition in the coming years. And not to miss, about fifty-seven per cent are already experimenting with the in customer service, sales, and IT. But where does the issue lie? Many companies are racing without a clear framework.   

This autonomy in AI promises speed, creativity, and scale, yet heightens legal exposure around consent, profiling, fairness, and oversight. As laws like the GDPR, CPRA (California), and India’s Digital Personal Data Protection Act, 2023 begin to intersect with agentic AI’s capabilities, one question becomes urgent: are today’s laws enough? And more practically, what compliance frameworks can SaaS platforms adopt now to stay ahead?

Regulatory landscape

The rise of Agentic AI across SaaS platforms has created a real gap between technology and the law. The speed of regulatory reforms is not matching the autonomy of AI. Yes, we do have frameworks that offer some guidance. But things get blurry very quickly when it comes to self-learning and decision-making systems.   

General Data Protection Regulation (GDPR)

Let’s start with GDPR, because when we talk about AI-driven decision-making, it is still the gold standard. Starting with Article 5, which mandates principles like transparency and data minimisation. The basic need, like a lawful consent, is laid down by Article 6. But the real game-changer is Article 22. This gives the right not to be subject to decisions made solely by automated processing if those decisions have a significant effect.      

This provision was central in SCHUFA (C-634/21, CJEU, 2023), the court ruled that automatically generated credit scores constitute “automated individual decision-making” under GDPR Article 22 when third parties rely on them for contractual decisions. This highlights the need for transparency and safeguards in automated decision processes affecting individuals’ legal rights. Similarly, in Dun & Bradstreet Austria GmbH (C-203/22, CJEU, 2025), the court clarified that companies must provide “meaningful information” about the logic of automated assessments under Article 15, even when trade secrets are invoked. For SaaS firms deploying agentic AI, these rulings confirm that opacity in decision-making is a legal liability, not just a reputational one.

California Privacy Rights Act (CPRA)

California’s CPRA gives consumers the right to opt out of automated profiling where it impacts areas like employment or housing. Enforcement is also tightening. In 2022, Sephora was fined US$1.2 million under CCPA for failing to honour global privacy control signals. While not directly about AI, the case underscores regulators’ seriousness about data rights, which will extend to autonomous AI profiling as CPRA’s rule-making on Automated Decision-Making Technology (ADMT) advances.

India’s Digital Personal Data Protection Act, 2023

And then we have the DPDP Act of India, A very consent-centric law. It provides that people must know why their data is being collected (Article 5). With regard to consent, it provides that the consent must be free, informed and specific (Article 6). It also put responsibilities on the companies (or “data fiduciaries”) around security, accuracy and grievance redressal. 

But something is missing. What?

The law does not have explicit rules on automated decision-making or profiling, unlike GDPR or CRPA. This creates a grey zone for SaaS providers in India, raising compliance uncertainty.

Agentic AI use-cases in SaaS & associated risks

With the ability to make decisions on its own, Agentic AI is popping up everywhere on SaaS platforms. This may look like a huge win: faster workflow, smarter insights and fewer manual processes. 

But there is a flip side. What?  

The more the system is autonomous, the bigger the privacy and compliance headaches. Let’s see some examples.

EdTech: GoGuardian Beacon

An AI-driven student safety platform, GoGuardian Beacon, is used by over 10,000 schools and monitors about 25 million students across the US. It analyses online activities to detect and prevent bullying, self-harm and catch other distress indicators. Since its launch in 2020, it has prevented about 18,623 students from physical harm.

But critics have something else to say. They claim that there is a lack of transparency in this system, which often works with explicit consent from students and parents. 

In fact, this is more dangerous for the LGBTQ+ students, as this could unintentionally expose their highly personal data.

CRM platforms: Obsidian security’s oversight on AI tools

Corporations are plugging GenAI tools into their CRM platforms. But Obsidian Security has flagged pretty serious risks. What risks are we talking about? These tools run with little to no oversight. It can tap into sensitive data without a proper guardrail. And this data may extend up to personal financial details and healthcare reports. 

This issue gets worse because many CRM systems do not have a clear privacy policy and strong governance measures.  And because of this, many big problems may occur.

If the real-time monitoring and strict controls do not come into place, then what started as a move to boost efficiency will turn into a costly liability.

E-commerce shopping agents and personalisation risks

Did you know that agentic-AI is deeply integrated into the operations of retail giants like Amazon and Walmart?

Approx thirty-five per cent of the revenue is driven by the product recommendations given by personalised engines of Amazon, which works as a quite powerhouse. The backend agent that a built on SageMaker is also handling the pricing model, managing fulfilment and customer segmentation. 

Coming to Walmart, its Luminate platform uses AI agent to manage restocking and personalise the shopping experience in real time.   

But the concerning part is that customers are often not informed of how and why their data is being used. So many times, they are unaware that they are being monitored. This raises concerns regarding some dangers that occur because of this invisible personalisation, like profiling, tracking and segmentation of people without consent.      

The problem is not agentic AI itslef, but the way it is governed. To determine whether it is a powerful asset or a liability, three factors are responsible: consent, culture and communication. 

SaaS platforms, when skipping consent checks, neglect explainability or blur data boundaries, risk compliance failure and lose user trust. Every autonomous agent needs a guardrail, designed around this purpose, the type of data it touches and the impact it has on people.

Compliance framework: a risk-based approach

It is quite clear from the examples that the real problem is launching AI without thinking through the risks. Compliance is about giving innovation a backbone with structure and accountability. That is why a risk-based mindset is a must.

Data Protection Impact Assessments (DPIAs)

A DPIA is basically a privacy health check that helps to spot risks before any AI tool launches. Especially those that interact with personal and behavioural data. Skipping it would be a trouble.

Let me take you through a case that happened in 2020, where Vodafone was fined by the Italian Data Protection Authority for about 12.25 million Euros. 

But what did it do?

It launched an AI-powered marketing campaign without conducting a DPIA. It also overlooked the consent, data handling and opt-out options, and guess what? They paid the price. 

What is the smarter approach? You should not ask if you need a DPIA. Ask why one is not on the table? 

Giants like Microsoft even say it is a good practice, even when not legally required.  In fact, the regulators in the Gulf are starting to make them mandatory for high-risk AI. 

From policy to practice

Of course, even on paper, audits go so far. These AI systems need to be built with guardrails. Let’s see one vulnerability case of Slack that happened in August 2024. A security researcher disclosed a vulnerability in the AI toolset. It was shown how a prompt injection could trick its AI into phishing employees and exposing their sensitive data.   

And here is where red teaming and audit trails come into play. Red teaming is stress-testing AI before launch and an audit trail is logging every action for accountability. Hence, the message is clear: compliance is not an afterthought but a part of building a trustworthy AI system from day one.

Policy and governance measures: building trustworthy AI

Good engineering controls are important, but if there is no accountability, then they will not go very far. Hence, governance is not an option, but it works as the foundation of trust of people in AI. 

The role of data protection officers and ai ethics governance

GDPR mandates those companies handling a large amount of personal and sensitive data to appoint a Data Protection Officer (DPO) under Article 35. But companies are moving a step further. To keep ethical oversight front and centre, companies are appointing an AI ethics officer or setting up responsible AI councils, since AI make more complex and high-impact decisions.  

Leading companies in AI governance

Microsoft 

Microsoft has embedded Responsible AI leads in their production teams. They collaborate with the Office of Responsible AI to flag risks, address ethical concerns and keep compliance on track.

Salesforce 

Salesforce rolled out a tiered AI governance framework that includes “red flags” review boards to check and prevent AI-related risks. 

Zoom 

Zoom has launched an AI companion with granular admin controls to tackle unauthorised AI tools. This allows businesses to check who uses AI and how.  

SAP

SAP took a multi-layered approach, combining an AI Ethics Advisory panel, an AI Ethics Office and an AI Ethics Board. These all worked together to ensure comprehensive oversight of AI initiatives. 

Why does it matter?

These measures bridge the gap between the deployment of AI and staying accountable. No doubt, appointing a DPO meets legal obligations, but appointing an AI Ethics Officer reflects strategic foresight.

Governance is not just a safeguard, but it is a driver of responsible innovation when legal, engineering and product teams collaborate early.

The path ahead: preparing for future scrutiny

Privacy issues in agentic AI are not just a prediction; it is already happening. We are seeing these real risks play out, happening from classrooms to hospitals to customer service desks. This means clear rules and strong oversight are no longer options, specifically in sensitive cases where the impact on people’s lives is highest.     

EU AI Act: a new regulatory landscape

The EU Artificial Intelligence Act came into effect on 2nd of August 2024. It has categorised AI tools such as customer support chatbots or software that detects emotions as “high risk” (Article 6(2) and Annex III).      

If any SaaS company uses such tools without proper checks and human oversight, from 2nd August 2026. In that case, it will be fined up to thirty-five million Euros or seven per cent of their global revenue, whichever is higher.  

The message is simple: if any decision made by AI affects the jobs, well-being and opportunities, then it is your duty to explain how it works and why. These are not just restrictions but also give a new baseline for building digital trust.  

India’s DPDPA: emerging oversight

India is not behind and is moving in the same direction with the DPDP Act 2023. We are expecting detailed rules and regulations by the Ministry of Electronics and Information Technology by 28th September 2025. The earlier draft released in January 2025 already calls for explicit consent, clear notices, quicker breach reporting, and shorter data retention periods. However, the Act does not yet tackle automated decision-making head-on. But this helps build a platform for tighter AI regulation in finance, healthcare and edtech.  

Strategic implications for SaaS providers

Now, it is high time for SaaS providers to stop seeing the EU as normal and regional rules, but should now start using and treating them as the global norms. It is always seen that when the rules and regulations are strict, then the platforms remain well-structured and won’t fall into any trouble.

Conclusion

The pattern of work in the SaaS platform has changed since Agentic AI came. Now it not only does what it has been instructed to do, but it can automatically run the workflows, talk to the users directly and make decisions. This is a reason why data privacy cannot be just ignored or taken lightly for compliance. The data privacy is embedded in the platform from the beginning. 

We’ve already seen real risks in practice: CRM tools wrongly tagging personal data, learning platforms tracking students without asking, and customer churn models making unclear automated choices. Big cases with Microsoft, Zoom, and WhatsApp show that poor oversight can lead to legal trouble and damage a company’s reputation. On the other hand, platforms that use tools like risk assessments (DPIAs), red-teaming, audit logs, and clear AI governance show that following compliance rules can actually help drive safe innovation. 

The focus now is not on whether regulation is needed; it is on how proactively SaaS providers prepare. With the EU AI Act setting a global benchmark and India’s DPDP rules evolving rapidly, platforms must embed accountability into their products.

It is the time that you check your AI systems. Also, ensure that transparency and privacy are embedded in it in every step and use the AI as a power, not as something which gives you doubt to think again. 

References

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