Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Businesses everywhere are moving fast to adopt AI. Yet many initiatives are fragmented, siloed, difficult to scale, and lacking adequate governance. New research from Forrester Consulting, commissioned by Tines, surveyed more than 400 IT leaders in North America and Europe on the challenges of scaling AI and the role IT can play. The findings show that while governance, security, and cross-functional alignment are top priorities, they’re also some of the biggest barriers.

AI now sits inside customer support, finance, human resources and product development. That reach brings value, and it also exposes personal and sensitive data in new ways. The question is no longer whether to adopt AI. The question is how to adopt it responsibly, with AI data privacy built into the system rather than tacked on after a test run. This guide explains the core concepts, definitions and best practices you can use to design, ship and scale AI with privacy in mind.

Software supply chains are the attack vector for cybercriminals, and the challenge isn’t just finding vulnerabilities; it’s fixing them fast while ensuring security, compliance, and developer productivity. As supply chains grow in complexity, traditional tools aren’t enough; organizations need intelligent, autonomous assistance embedded directly into developer workflows.

The rapid pace of AI innovation is driving new possibilities for every organization. Yet, for many, the journey from inception to reliable, production-ready AI applications is riddled with hidden challenges: proliferation of models, security blind spots, and a desperate need for consistent governance. You want to harness the power of AI, but not at the expense of control, security, or compliance.

PCI DSS 6.4.3 requires organizations to maintain integrity controls over all JavaScript running on payment pages, while 11.6.1 requires continuous monitoring and alerting for script changes. For hospitality brands, compliance is harder than in other industries because: The result: Security teams struggle with fragmented visibility, manual evidence collection, and constant alerts during audits.

There’s a lot of noise right now about what AI will become. Everyone has a prediction.