2026 is the year enterprise AI stopped being a science experiment. When JPMorgan reclassifies AI from R&D to core infrastructure, AI inherits the same security expectations as your databases and identity systems.
JPMorgan Chase formally moved its AI investment from experimental R&D to core infrastructure, backed by a ~$19.8B technology budget and thousands of staff dedicated to AI. Leading labs poured billions into enterprise deployment and began embedding “forward-deployed” engineers directly inside customer businesses — an admission that AI needs a human implementation layer, not just an API. Anthropic alone anchored a Wall-Street-backed venture with over $1.5B in commitments to push AI into portfolio companies.
What “core infrastructure” actually implies
Infrastructure carries infrastructure expectations: uptime, change management, access control, auditability, and — critically — named security ownership. A model wired into core workflows is now in scope for the same rigour you apply to production systems. “The AI team owns it” is not a security model.
The questions boards are starting to ask
- Who owns the security of our AI systems, and is it in the risk register?
- What data do these systems touch, and does that satisfy DPDP and our sector regulator?
- What can an agent do if compromised, and can we detect it?
- Do we test AI features the way we test the rest of our stack?
- If a vendor model goes down or changes behaviour overnight, what breaks?
Treating AI as production from day one
- Threat-model each AI system before launch, not after the incident.
- Gate privileges — least-privilege tools, scoped credentials, human approval on irreversible actions.
- Monitor & log prompts, tool calls, and outputs for detection and forensics.
- Test on every change, with both automated tooling and human red-teaming.
Indian enterprises are moving fast, and “move fast” without “secure by design” is how you end up explaining an incident to a regulator. See how RingSafe helps.
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