AI adoption is outpacing regulation. Most companies have no governance, no security testing and no process for managing the risks. The question is: how long can that last?
If you think AI-generated code is saving time and boosting productivity, you’re right. But here’s the problem: it’s also introducing security vulnerabilities at an alarming rate. Our latest research reveals that 45% of AI-generated code contains security flaws, turning what should be a productivity breakthrough into a potential security nightmare.
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.
Testing your APIs after deployment is one of the biggest mistakes teams make. By the time vulnerabilities are discovered in production, the risk is higher, fixes take longer, and attackers may already exploit them. In this short clip from our API Security webinar, we explain why “shift-left” testing — securing APIs before deployment — is critical to prevent breaches.
Relying on manual API testing? Big mistake. Manual tools take forever to set up, forever to maintain — and they still break. In this clip, we explain why automation is the only way forward for API security.
Pen-testing once a year? Not enough. Shift left means automated API security testing inside your CI/CD pipeline — every commit, every release. In this video, we show why scaling security without automation is impossible.
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.
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.
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.