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How does AI security differ in BFSI vs SaaS product companies vs consulting?

BFSI — regulatory-heavy. RBI's emerging AI guidelines, DPDP Act compliance. AI use cases: fraud detection, credit scoring, customer chatbots. Security focus: model bias auditing, explainability for regulatory review, data residency. SaaS product companies — product-shipping focus. AI use cases: in-product GenAI features (Postman AI, Razorpay AI). Security focus: prompt injection, data isolation per tenant, output sanitisation. Consulting (Accenture, Wipro, Deloitte) — multi-client AI security advisory. Skills needed: cross-industry awareness, framework fluency (NIST AI RMF, ISO 42001), auditor mindset. Salary trends: BFSI pays 10-15% premium; SaaS pays equity; consulting pays per-engagement.
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Q. What's the AI security stack a Bangalore product company typically uses in 2026?

Layered stack: (1) Model layer — typically combination of OpenAI/Anthropic/Google APIs + smaller fine-tuned local models. (2) Guardrails — NeMo Guardrails or Llama Guard. (3) Input/output validation — custom classifiers …
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