This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations exposed, and developers will struggle to backtrack in tracing and identifying where - and how - a security gap occurred.
A secure software development life cycle means baking security into plan, design, build, test, and maintenance, rather than sprinkling it on at the end, Sara Martinez said in her talk Ensuring Software Security at Online TestConf. Testers aren't bug finders but early defenders, building security and quality in from the first sprint. Culture first, automation second, continuous testing and monitoring all the way; that's how you make security a habit instead of a fire drill, she argued.
Modern software systems are exposed to a constant stream of disclosed vulnerabilities. Thousands of new issues are published every year across operating systems, runtimes, libraries, and frameworks. Treating all of them as equally urgent is not realistic, and trying to do so often leads to ineffective security work. To manage this volume, the security community relies on two foundational mechanisms: CVE and CVSS. They are frequently referenced in advisories, scanners, dashboards, and patch workflows, but they are also frequently misunderstood.
