Ensuring Quality and Security in Generative AI Integrations for Enterprise SaaS Platforms
Generative AI is rapidly transforming enterprise SaaS platforms, unlocking new levels of automation, intelligence, and user experience. However, deploying large language models (LLMs) in production systems introduces complex challenges around quality assurance, security, and compliance - especially in regulated environments.
This paper presents a pragmatic framework for integrating generative AI into cloud-native enterprise software while maintaining high standards of reliability and trust. We share how our team validated AI-generated outputs through functional testing, prompt-output consistency checks, and hallucination detection techniques.
To meet enterprise-grade security expectations, we extended DevSecOps pipelines to include AI-specific safeguards such as prompt injection detection, output sanitization, and runtime policy enforcement. Observability was enhanced to monitor AI behavior in real time, detect anomalies, and ensure compliance over time.
We also applied chaos engineering practices to simulate failure scenarios and stress-test the resilience of AI components under production-like conditions. Our approach enabled secure, scalable, and auditable deployment of LLM features across critical SaaS workflows.
This session offers practical insights into building safe, reliable, and governable AI systems for enterprise applications.
Sneha Mirajkar
Sneha Mirajkar is a Principal Software Engineer with over 16 years of experience building secure, scalable systems across cloud-native and enterprise platforms. Her work sits at the intersection of software quality, security engineering, and innovation-designing and integrating AI-driven capabilities into regulated environments while upholding the highest standards of reliability and compliance.
Sneha has led cross-functional teams through the adoption of DevSecOps, secure AI practices, and platform hardening initiatives, with a focus on production-grade integration of emerging technologies such as large language models (LLMs), threat modeling, and automated quality gates. She is passionate about bridging the gap between innovation and trust, especially when it comes to deploying cutting-edge features in environments where safety, correctness, and user trust are paramount.
Outside of work, she enjoys mentoring early-career engineers, contributing to open conversations around AI safety, and exploring the future of intelligent systems through a security-first lens.
Vittalkumar Mirajkar
Vittalkumar Mirajkar is a Senior Engineering Manager with over 15 years of experience driving large-scale cloud and on-prem infrastructure initiatives focused on cost optimization, operational excellence, and compliance in enterprise environments. With a strong background in systems engineering and platform management, he has led global teams in building scalable solutions that maximize resource efficiency while ensuring adherence to regulatory and security standards.
At the heart of Vittalkumar's work is a commitment to continuous improvement-whether through right-sizing cloud workloads, automating compliance controls, or improving observability and resilience in complex hybrid ecosystems. His strategic and hands-on leadership has enabled organizations to reduce technical debt, improve service delivery, and align engineering investments with business outcomes.
He is passionate about building high-performing engineering cultures, mentoring teams through digital transformation, and championing best practices that bridge technology and governance.