Generative AI in BPO: Transforming Security in the AI Era

Business Process Services

Artificial Intelligence

Last Updated: November 26, 2025

Introduction

As the business process outsourcing (BPO) industry begins to explore and deploy solutions around generative AI (GenAI), it faces a significant challenge: ensuring data protection and privacy in an era where AI, including agentic AI or generative AI in business process outsourcing, is becoming increasingly powerful and pervasive.

Back in 2019, OpenAI withheld the full version of its powerful natural language processing model, GPT-2, due to concerns about its potential misuse for creating deepfakes and spreading disinformation. Another concerning incident was the doctored video of Ukrainian President Volodymyr Zelenskyy, circulated on social media and placed on a Ukrainian news website by hackers, which was later exposed as a deepfake and taken down. 2024 saw a generative AI feature in iOS 18 erroneously summarizing a BBC story and indicating that the UnitedHealthcare CEO’s shooter had killed himself. Imagine similar misinformation emerging in a BPO-managed customer service channel—it could spread rapidly through automated communication systems, damaging brand reputation and eroding client trust. As BPOs integrate GenAI into voice support, content generation, and customer interaction, they must do so with a heightened focus on BPO data protection, auditability, and ethical AI usage.

With responsible AI practices and the adoption of private LLMs, BPOs can not only safeguard client data but also drive innovation. Here’s what makes generative AI in BPO worth the journey:

Enhanced data quality and privacy
Personalized customer interactions
Improved operational efficiency
Competitive edge in digital transformation

In this blog, we will explore how generative AI in BPO is revolutionizing data security and privacy, ultimately transforming it into a strategic asset for digital innovation and sustained competitive advantage.

The Current State of Privacy and Data Security in BPO

The BPO industry handles sensitive data, from personal information to financial transactions. This data is vulnerable to cyberattacks, data breaches, and human errors. As per the U.S. Department of Health and Human Services, the healthcare sector alone reported 442 incidents of breaches during Q1–Q3 2025, affecting nearly 33 million individuals. According to IBM’s Cost of a Data Breach Report 2025, the global average cost of a data breach is $4.4 million. The report added that extensive use of AI in security could result in $1.9 million in savings, compared to organizations that do not use AI solutions. These statistics underscore the necessity for robust BPO data protection measures and adherence to various regulations and standards to mitigate these risks.

The Rise of Generative AI: Opportunities and Risks

Generative AI, a type of artificial intelligence capable of creating new data or content such as images, text, or audio, is transforming the BPO industry. For instance, synthetic data, which is artificially generated rather than obtained from real-world observations, can significantly enhance privacy and security practices within BPOs. By reducing the reliance on real data, GenAI minimizes the risks associated with handling sensitive customer information and supports data security in outsourcing.

Generative AI can also enhance data quality by producing accurate, complete, and consistent data, thereby aiding BPOs in making better decisions. Furthermore, generative AI facilitates data anonymization, a crucial aspect of BPO data protection, by generating data that cannot be linked to specific individuals.

Beyond privacy and security, generative AI in BPO can improve customer experiences by generating personalized and relevant content. For example, OpenAI’s ChatGPT Store, launched in 2024, allows users to create, customize, and share GPTs tailored for specific tasks, revolutionizing customer interactions. Microsoft’s Copilot Pro, introduced in 2024, offers advanced models and features like real-time voice conversations, enhancing user engagement. Anthropic’s Claude, a multimodal generative AI model, provides scalable access to advanced capabilities for businesses, improving operational efficiency. By analyzing customer data, generative AI can create content tailored to individual preferences and needs, thereby enhancing customer satisfaction and engagement.

Here are some implications for what lies ahead:

New risks and challenges: Generative AI in BPO will create new risks and challenges, such as data misuse, bias, fraud, or manipulation. BPOs must closely monitor their generative AI solutions and respond quickly to any issues or incidents.
New opportunities and innovations: Generative AI will also create new opportunities and innovations for BPOs, such as new services, products, or markets. BPOs must embrace generative AI as a strategic asset and a source of competitive advantage. For example, generative AI can enable BPOs to create high-value artifacts such as videos, narratives, training data, and even designs and schematics for their clients. Consequently, the impact of generative AI on the user experience in the pre-purchase stage can be significant. This also means that while BPOs explore marketing operations as a service, content moderation will become a critical aspect of the offering.
New skills and competencies: Generative AI will bring about new skills and competencies for BPOs, such as data science, machine learning, or linguistics experts. BPOs must also leverage external partners and providers to access AI innovations and capabilities. They need to start building tech centers of excellence (CoEs) consisting of citizen developers/users, and implement prompt engineering and model tuning by reinforcement learning from human feedback (RLHF).

Key Considerations for Secure Generative AI Adoption in BPOs

Adopting generative AI in the BPO sector requires a nuanced approach. As organizations strive for innovation, they must also anticipate the evolving threat landscape and regulatory environment. Here are some advanced considerations for secure GenAI adoption:

Model Selection: Decide between open-source or proprietary AI for better data control and compliance.
Data Residency: Ensure models comply with regional data laws; deploy locally when needed, to ensure data security in BPO.
Zero Trust Security: Apply zero trust to limit unauthorized access.
Threat Modeling: Regularly assess generative AI for security vulnerabilities like prompt injection.
Explainable AI (XAI): Use XAI for transparency and easier regulatory audits.

Let’s now dive deeper into how these considerations can be addressed through best practices.

Best Practices for Ensuring Privacy and Security in the BPO Industry

To leverage AI in business process outsourcing effectively and safely, it’s important to adopt some best practices for ensuring privacy and security in their operations. Some of these include:

Data quality: BPOs must verify that their generative AI solutions produce accurate, reliable, consistent data or content that meets their business objectives and customer expectations.
Data encryption: BPOs should implement encryption mechanisms for their data at rest and in transit using robust algorithms and keys. This ensures that even if data is intercepted, it remains unintelligible to unauthorized parties.
Data anonymization: BPOs should use techniques to anonymize data, making it difficult to link information back to individuals. This helps in maintaining data privacy while still enabling valuable insights.
Access control: BPOs should employ strict access control measures, such as role-based access and multi-factor authentication, to limit access to sensitive data and generative AI solutions to authorized personnel only.
Data logs: BPOs should maintain logs of all data and generative AI activities, such as creation, modification, deletion, or access.
Data governance: BPOs must establish clear policies and processes for managing synthetic data and other generative AI outputs, such as ownership, accountability, access, usage, retention, and disposal.
Regular audits and monitoring: Running a secure BPO demands regular audits of data protection measures and generative AI solutions by independent third parties. Periodic audits and security assessments help to identify vulnerabilities and gaps in the security infrastructure, and ensure the timely remediation of potential threats.
Employee training: BPOs should educate their employees about data protection best practices, the importance of safeguarding sensitive information, and the proper use of generative AI solutions. Training should cover phishing scams, social engineering tactics, and other cyber threats.
Incident response plan: BPOs should develop a well-defined incident response plan to respond swiftly and effectively in the event of a data breach. This minimizes the impact on both the firm and its clients.
Compliance with regulations: BPOs should stay updated with evolving regulations and standards pertaining to data privacy and AI usage. Compliance with laws, such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), or other regional regulations, is essential to avoid legal repercussions and maintain client trust.
Responsible AI: BPOs should adopt responsible AI practices to ensure ethical use of generative AI. This includes transparency in AI operations, bias mitigation, and adherence to ethical guidelines, including respect for human dignity and rights. Responsible AI practices help build trust with clients and stakeholders by demonstrating a commitment to ethical AI deployment.

Emerging Technologies and Solutions for AI Security in BPOs

As threats to AI systems become more sophisticated, a new generation of security technologies and solutions is emerging to help BPOs fortify their generative AI deployments:

AI in BPO Security Orchestration: Security tools powered by AI can autonomously detect and respond to abnormal behaviors within generative AI systems, dynamically adjusting controls to counteract potential breaches or misuse.
Confidential Computing: This technology enables data to remain encrypted not just at rest or in transit, but also during processing. Confidential computing environments ensure sensitive client data is never exposed, even to cloud service providers hosting the AI workloads.
Federated Learning: To minimize data exposure, federated learning trains AI models across decentralized datasets without transferring raw data to a central location.
Secure Model Watermarking: Watermarking generative AI outputs helps trace the origin of content and detect unauthorized use or tampering.
Automated Compliance Monitoring: Advanced platforms now leverage AI to continuously monitor regulatory changes and automatically assess AI deployments for compliance.

Use Cases: BPOs Successfully Implementing Secure Generative AI

Across the BPO landscape, several organizations are already harnessing generative AI in ways that exemplify both innovation and security. Hexaware’s case studies provide several compelling examples of BPOs successfully implementing secure generative AI. These include solutions for AI-powered contact center transformation, secure content operations, and enterprise-wide AI integration—all with robust security and compliance measures in place.

Here are some examples:

Enhancing User-generated Content Safety

Hexaware developed an AI-driven trust and safety operations center (TSOC) for a high-tech sector leader, automating content moderation for a short video app. Our expert content management team leveraged AI and ML to provide personalized content experiences, enhance content discovery, and ensure a safe and secure environment for the audience to access videos. The result: 99% accuracy with enhanced controls for critical quality parameters.

Remote Mortgage Origination Support with Compliance-first Delivery

Hexaware delivered a tightly scoped advisory engagement that stabilized a North American mortgage lender’s origination support with verifiable, security-first controls and no loss of efficiency. By combining domain assessments, AI in mortgage processing, and secure remote infrastructure, Hexaware enabled rapid onboarding and measurable throughput gains. The result: 99% accuracy, 40% faster decisions, and sustained 100% mortgage compliance.

GenAI Chatbot Assistant for Insurance

A Belgian insurer deployed Hexaware’s Azure‑OpenAI driven GenAI chatbot to replace fragmented info systems—providing instant product answers, boosting agent productivity, reducing response times, and surfacing rich customer insights from feedback.

Conclusion

The emergence of generative AI in business process outsourcing provides plenty of opportunities for businesses to improve their data protection practices by taking a proactive approach to risk assessment, compliance, and data management.The integration of AI and automation in business process services is expected to become even more pervasive. AI-driven customer support, such as advanced chatbots and virtual assistants, will handle increasingly complex inquiries, improving response times and customer satisfaction. Robotic process automation (RPA) will revolutionize back-office operations, ensuring greater accuracy and efficiency. Additionally, hyper-personalization will become the gold standard in customer experience, with AI enabling businesses to offer tailored solutions that meet specific customer needs.

As GenAI matures, it won’t just be a tool for automation—it will be a catalyst for reimagining BPO compliance, trust management, and customer engagement. The leaders of tomorrow will be those who view AI not as a threat, but as a strategic partner in delivering smarter, safer services.

Ready to dive in? Hexaware’s security-first approach to BPO services helps clients harness generative AI with confidence and compliance. Let’s talk.

About the Author

Sanjoy Singha Roy

General Manager—Presales

Sanjoy is a GenAI evangelist with extensive experience in enterprise AI, digital transformation, and intelligent automation. He leverages his deep understanding of AI and automation to drive innovative solutions. Sanjoy's customer-centric and collaborative leadership style fosters high-performing, multicultural teams. His strategic vision and innovative approach have established him as a trusted resource in the AI and digital transformation space.

FAQs

As a leading BPO services provider, Hexaware leverages its agentic AI and generative AI-driven approach for end-to-end business transformation. With a digital-first, domain-centric strategy, Hexaware blends adaptive automation and contextual intelligence to deliver scalable, future-ready outcomes. Its deep expertise across banking, financial services, healthcare, and insurance empowers clients to achieve sustainable growth and agility in a rapidly evolving digital landscape.

Generative AI is revolutionizing the BPO industry by automating repetitive tasks, enhancing customer service with 24/7 intelligent chatbots, and improving operational efficiency. It enables hyper-personalized customer interactions, reduces errors, and drives new service offerings, such as automated content creation and advanced analytics. These innovations enhance productivity, reduce costs, and position organizations for greater scalability and a competitive advantage.

AI-driven BPOs face significant data privacy challenges, including unauthorized data use, cross-border data transfer complexities, and risks of sensitive information leakage through AI outputs. Ensuring compliance with diverse international regulations, managing consent, and implementing privacy-preserving technologies like anonymization and federated learning are ongoing hurdles. Robust governance, regular audits, and continuous risk assessments are essential to safeguard client data.

BPOs using AI must comply with global data protection laws such as the EU’s GDPR, California’s CCPA, and HIPAA for healthcare data. The EU AI Act introduces strict requirements for high-risk AI systems, including transparency, risk management, and human oversight. Additional regional laws (e.g., Singapore’s PDPA, India’s DPDP Act) and industry standards like ISO/IEC 42001 and the NIST AI Risk Management Framework also guide compliance and governance.