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Tejasvi Addagada on Muck Rack

Tejasvi Addagada

As seen in: Medium, The Startup, ETCIO, Towards Data Science, Finextra, Analytics India Magazine, DATAVERSITY, ET Insights, CDO Magazine
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Articles

The AI Guardrails in Financial Services: Why Independent Validation Cannot Be Assumed

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By Tejasvi Addagada
| Finextra Verified
If your institution has deployed a generative AI system in production, you have a guardrail layer. The question is whether you have independent evidence that it works for your regulatory context, your customer base, and your specific risk taxonomy. Mostly, What exists is a vendor assurance that the guardrail has been evaluated or tested, without a precise answer to the follow-up question that matters: tested against what, and for which risks?
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Guardrails for GenAI in Indian banking: Why domain-native validation cannot be an afterthought

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By Tejasvi Addagada
| ETCIO Verified
GenAI deployments in Indian banking have moved well past the exploratory stage. Financial institutions are running intelligent document processing pipelines, credit assessment summarisation tools, regulatory Q–A assistants, and voice-enabled conversational agents in or near production. As any technology risk practitioner will tell you, the deployment decision is often the easier part.
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Governing Generative AI: Foundational Mechanisms for Responsible Adoption

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By Tejasvi Addagada
| DATAVERSITY Verified
Generative AI is evolving at a rapid pace – from automating document extracts to reviews to powering customer interactions. But with speed comes greater risks, including privacy leakage, intellectual property leakage, bias in outputs, regulatory frameworks, and unclear accountability. Unlike traditional IT systems, generative AI adoption is often bottom-up with employees’ vibe coding with models, making it hard to control technically while foundation models evolve unpredictably.
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