AI Modernization vs AI Technical Debt
At Infosys AI Day, Nandan Nilekani highlighted:
• 60–80% IT spend locked in legacy maintenance
• AI-driven modernization now economically viable
• Risk of AI-generated legacy systems within 5 years

Governance themes:
• Usage guidelines
• Quality gates
• Explainability standards
• AI disclosure policies
Parallel example: Australia’s Fair Work Commission overwhelmed by AI-assisted filings — institutional impact already visible.

Question to the community:
Are you implementing internal AI audit trails or output validation pipelines?
Source: https://www.theregister.com/2026/02/23/asia_tech_news_roundup/

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#AIGovernance #TechRisk #EnterpriseSecurity #DigitalTransformation #AICompliance #TechDebt #Infosec #AutomationRisk

An experimental AI project is using prompt-based code blocks to intentionally alter chatbot outputs, raising important questions around model alignment, misuse potential, and guardrail robustness.

While largely creative in intent, the concept highlights how AI systems can be steered through instruction layers - reinforcing the need for clear safety boundaries and responsible deployment.

How should security and AI teams evaluate such experiments?

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Source: https://www.wired.it/article/pharmaicy-droghe-digitali-chatgpt-intelligenza-artificiale/?utm_source=linkedin&utm_medium=social&utm_brand=wired&utm_social-type=owned

#AISecurity #ModelAlignment #PromptAbuse #ResponsibleAI #Infosec #TechRisk

"The cloud's 'infinite scale' hides financial risks. A $72K bill from a simple error shows how quickly costs balloon. Prioritize FinOps: track margins, optimize architecture, and automate cost controls. #CloudCosts #FinOps #TechRisk"

https://saysomething.hashnode.dev/infinite-scale-infinite-spend-why-cloud-billing-is-a-costly-trap-for-unchecked-growth