As Generative AI moves from experimentation to everyday business use, MSMEs are increasingly exploring its potential to improve productivity, streamline workflows and support decision-making. However, wider adoption also brings concerns around data privacy, security, governance and accountability.
Arun Meena, Co-Founder & CEO, RHA OneAI by RHA Technologies, in an exclusive interaction with Abhineet Kumar of Elets News Network (ENN), discusses the key risks businesses need to consider when adopting GenAI, practical safeguards for protecting sensitive information, the importance of human oversight and how MSMEs can build a secure and governed framework for enterprise-grade AI adoption.
Edited excerpts:
As GenAI moves from experimentation to everyday business use, what are the biggest data security and privacy risks MSMEs should be aware of when adopting AI tools?
The biggest risk is that businesses begin using GenAI before understanding what happens to the information they share with it. An employee might upload a customer list, an internal proposal, a financial document or a product plan simply to get a quick summary. Once that information enters an external platform, the organisation may not have complete visibility into how it is stored, processed, retained or accessed.
The main concerns for MSMEs are exposure of confidential information, misuse of personal data, leakage of intellectual property and employees using unauthorised AI tools. Relying on AI for important decisions and business deals could also lead to inaccurate or fabricated responses. NIST identifies privacy, information security, intellectual property and unreliable outputs as significant risks associated with generative AI.
I would suggest that every business evaluate an AI application service provider as it would any other organisation, rather than treating it simply as a search engine. Before using it, the business should understand what information is being shared, who can access it, how long it is stored and whether it may be used to improve the provider’s models. AI can deliver tremendous value, but speed and convenience should not come at the cost of confidentiality or accountability.
Many MSMEs use publicly available GenAI platforms without fully understanding where their data goes. What practical safeguards should businesses put in place before integrating AI into sensitive workflows?
Businesses should begin by creating clear boundaries around the information employees are allowed to share with AI tools. A simple classification of data as public, internal, confidential or highly sensitive can make everyday decisions much easier.
The next step is to approve tools for particular purposes. Employees must know which platforms they may use, what kind of information can be entered and when they need permission from the IT or management team. The organisation must review the provider’s privacy and security terms before using the service for work.
Important questions include whether customer inputs are used for model training, where the data is stored and how long it is retained, whether the data can be deleted, and what protections are available for user access. Businesses must also use passwords, multi-factor authentication, restricted access, data masking and activity logs wherever possible.
For example, if an employee needs AI to analyse customer feedback, names, phone numbers, email addresses and account details must be removed before the file is uploaded. The initial implementation should also be tested in a controlled environment, with a human reviewing the output before it reaches a customer or influences a business decision.
The objective is not to stop employees from using AI. The objective is to make usage simple, visible and safer than taking an unnecessary risk. Secure AI deployment guidance also recommends access controls and limiting system permissions to only what is required.
Also Read: “Global Governance Needs a ‘Capacity by Design for Governance’ Revolution”
For an MSME with limited IT and cybersecurity resources, what does a secure and governed AI adoption framework realistically look like?
For an MSME, a secure AI framework should be practical enough to follow without requiring a large cybersecurity department. It can begin with a small governance structure and develop as the organisation gains experience.
The first requirement is ownership. Every AI use case should have a person responsible for defining its purpose, approving its data sources, reviewing its performance and addressing problems. The business should also maintain a basic record of the AI tools it uses, the departments using them and the type of information involved.
The company can then divide AI projects into different risk levels. Writing a draft email might require only basic review, while applications involving finance, legal matters, hiring or customer decisions would require greater oversight and approval.
Employees should receive clear instructions on how to use AI properly. They should know what information they cannot share and how to check and verify the content AI generates. The company should also have a way for people to report mistakes, privacy concerns or anything that seems incorrect.
The framework should be reviewed regularly. AI tools, company processes and security threats all change over time, so managing AI cannot be something done once and forgotten. NIST’s guidance also says that ongoing monitoring, data management, human oversight, privacy protection and security are important throughout the life of an AI system.
For a company, using AI safely is not about building everything on its own. It is more about establishing rules, assigning responsibilities, choosing the right tools and introducing AI step by step.
What are some of the most common mistakes businesses make when implementing GenAI, particularly around data governance, employee usage and compliance, and how can these be avoided?
When companies try to use GenAI, they often run into more trouble with how they manage GenAI than with the actual technology. The first major mistake is how people handle data. Many companies allow employees to put private data into public GenAI tools that do not have the right rules or safety checks. If a team puts customer records, confidential plans or code into a GenAI platform, it can create significant privacy, security and compliance risks.
The second mistake involves the people using the tools. In many organisations, employees start using GenAI independently without seeking approval. This is what many people call shadow AI. When employees use GenAI without checking with the IT or legal teams, it creates significant gaps in security and accountability.
The third issue is compliance. Many companies think that if a GenAI tool is helpful, then it must also be safe. That is not necessarily the case. Compliance requires organisations to consider privacy, ownership, where data is stored and specific industry laws. Sometimes companies even need to be transparent about when and how they use GenAI. For example, if a business uses GenAI to communicate with customers, it might need a person to review the content before it is sent.
Organisations can lower these risks by building a GenAI governance framework. This means deciding how to use GenAI, setting rules for data, training staff and monitoring how GenAI is being used. This also means training employees on what they can and cannot put into GenAI tools and regularly monitoring their use. Organisations should treat GenAI as a part of the company and not just a quick way to get more work done. The companies that do well will be the ones that find a way to be creative while still staying in control.
How can organisations build trust in AI-driven workflows while ensuring that human oversight, accountability and data protection remain central to decision-making?
Building trust in AI-driven workflows starts with one idea: AI should support decisions, not own them. Companies need to be very clear about where automation adds speed and where a person must stay in control. This is especially true in areas such as customer communication, compliance, hiring, finance and sensitive operational decisions.
From a leadership perspective, trust comes from clear rules and accountability. That means deciding who is responsible for the output, which data can be used, how the system is monitored and what happens when something goes wrong. If people do not know how a decision was made or who signed off on a decision, trust will break quickly.
Data protection is just as important. Businesses cannot ask staff or customers to trust AI systems if those AI systems are not built with strong data controls. The foundation has to be access management, clear data classification, restricted use of sensitive information and regular checks on how data moves through the workflow. In practice, that means making sure confidential customer details, internal documents or regulated data are never put at risk.
Human oversight should not be treated as a checkbox. You should build oversight into the workflow at appropriate points. For example, AI can help draft, analyse, summarise or flag issues. A qualified person should review the final decision when the outcome affects the business, the law or the company’s reputation. That is how companies maintain accountability while still benefiting from the speed of AI.
Trust also comes from being able to see what happened inside the workflow. Organisations should be able to trace what data an AI system accessed, what it produced, what actions it took and who approved the final outcome. Regular monitoring and review can then help identify problems early and ensure that the system continues to work as expected.
The organisations that will earn trust are the ones that use AI responsibly, explain the role of AI clearly and keep people accountable for the final decision. Ultimately, that balance between new ideas and control is what separates experimentation from being truly ready for enterprise-scale adoption.
Looking ahead, how can MSMEs prepare for enterprise-grade AI adoption without making large infrastructure investments, and what role can platforms such as RHA Technologies play in enabling this transition?
MSMEs should not think of AI adoption as an infrastructure problem. The better approach is to start small, pick one or two business problems where AI can make a difference and scale gradually. These could include customer support, internal knowledge access, document handling, sales follow-ups or routine reporting. Once the problem is clear, the main focus should be on using cloud-based tools, deciding who can access which data and ensuring that the process is set up with responsibility from the start.
The biggest error many small companies make is trying to use AI without a governance framework. Even if the cost is low, the rules for managing it do not go away. Protecting data, setting user access, maintaining auditability and establishing clear accountability and ownership are still very important. AI should help teams work faster and better. Important choices should still be checked by people, especially when finance, legal or customer-facing issues are involved.
Platforms such as RHA OneAI by RHA Technologies can play an important role by helping MSMEs adopt enterprise-grade AI without investing heavily in infrastructure or building complex systems in-house. The platform delivers an AI-powered digital co-worker that helps employees write, analyse, search, summarise, automate workflows and make faster business decisions, while maintaining enterprise-grade security and governance. Its multi-model routing lets organisations select the best foundation model for each task, optimising for accuracy, latency or cost, while a single secure interface enforces role-based access controls, audit trails and deployment choices, including private cloud or on-premises deployment. Integrations with core existing systems may further reduce implementation friction and eliminate the need to replace existing technology stacks.