Securing AI at Scale: Why Cloud-Native Trust, Not Just Code, is the Strongest Defense 

AI adoption is accelerating rapidly and reshaping industries at record speed—but for all its potential, it’s also expanding the surface for new kinds of risk. In his latest guest article for The AI Journal, Tekion’s CIO and CISO, Teza Mukkavilli, explores how organizations can scale AI securely by treating trust as a core architectural principle, not an afterthought.

As global AI investment accelerates — projected to surpass $630 billion by 2028 according to IDC — threats such as phishing scams, deepfakes, and data misuse are rising just as quickly. True resilience requires security, governance, and transparency built in from day one. 

Key Takeaways 

Trust starts with architecture 

Security can’t be added later. AI systems must be designed for protection, compliance, and visibility from the ground up. 

AI governance gap 

Few organizations have formal AI governance frameworks—resulting in increased risk exposure, stalled adoption, and decreased ROI. 

Transparency builds resilience 

Centralized “Trust Portals” and shared accountability between providers and partners ensure continuous oversight and confidence. 

The human layer matters 

People remain the most common attack vector. Continuous education and real-time alerts are critical defenses. 

Compliance isn’t optional 

With new AI regulations emerging globally, responsible governance is quickly becoming a business advantage. 

Why It Matters? 

As Teza writes, “Scaling AI securely requires trust to be built into the architecture from the start — before a single line of production code is deployed.” 

Organizations that embed trust early won’t just protect themselves — they’ll set the standard for responsible, scalable innovation. 

Read the full article in The AI Journal.