Microsoft has unveiled plans to incorporate Anthropic’s Claude Mythos Preview model and other AI models into its Security Development Lifecycle, embedding AI directly into the stages where code is written and tested. Rather than relying primarily on static analysis tools, Microsoft is adopting AI models capable of analyzing code dynamically and identifying complex vulnerabilities that might otherwise go undetected until later stages of development. Released on April 7, Anthropic’s Mythos model has already demonstrated a previously unmatched ability to uncover critical flaws across operating systems and widely used software. Anthropic claimed that the model’s ability to find security vulnerabilities is so advanced that it should not be released to the public. Microsoft gained access to the model through Anthropic’s Project Glasswing, a program that grants limited access to select tech firms for cybersecurity research. Within this framework, Microsoft is reporting measurable improve...
Most B2B applications collect incomplete data by design. A lead form captures a name and company. A recruiting tool surfaces a LinkedIn profile. An event registration system logs an email address and job title. The record enters your system and sits there, half-formed, waiting for someone to manually fill in the gaps before it can be acted on. This is an architectural problem, not a workflow problem, and solving it at the architecture layer is what separates applications that create operational leverage from ones that just digitize manual work. Understanding how to build contact enrichment into your application using professional data APIs changes how you think about the data ingestion layer entirely. Rather than passing incomplete records downstream and hoping someone fills in the blanks, you enrich at the point of entry, automatically, before the record ever reaches a human. The Architecture Problem Behind Incomplete Lead Records The gap between the data a user submits and t...