Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Product, fraud, and trust and safety teams at online merchants and marketplaces have been fighting bots for a long time. While there were occasional disagreements about how “bad” bots were (a purchase is a purchase, some might say), the general consensus often ranged from suspicious to block them all. But not anymore. As AI-powered browsers and agents become more commonplace, online merchants have to prepare for a world where agentic commerce is a standard sales channel.

Data privacy in the workplace is not just compliance. It is how an organization protects employees, builds trust, and reduces business risk. Employees handle most workplace data, which makes them a major target for AI-powered threats like deepfakes and business email compromise (BEC). The best way to protect data is a mix of practical employee habits, realistic training, and strong controls like least privilege access, MFA, monitoring, and email authentication.

Generative AI systems are designed to work with real data that expects structure, rely on patterns, and infer meaning from formats, relationships, and consistency across inputs. While real data facilitates better outputs and advanced training, making these systems useful has a tradeoff – it carries privacy, security, and compliance risk. This puts business on a difficult conundrum – either you block sensitive data entirely and lose context, or accept the privacy risks of using real data.

Data security posture management (DSPM) and cloud security posture management (CSPM) both play vital roles in cloud security, but they serve distinct purposes. DSPM focuses on protecting sensitive data across SaaS, IaaS, and PaaS environments, while CSPM focuses on cloud infrastructure. For organizations managing sensitive data in multi-cloud setups, DSPM often offers superior visibility, real-time monitoring, and regulatory alignment.