Cybersecurity programs have a clearly defined mission to reduce risk and limit threat exposure at a cost proportionate to the organization’s risk tolerance. Traditional risk reduction methods concentrate on identifying vulnerabilities in large volumes and tracking remediation efforts to ensure the timely resolution of these security issues.

Until recently, cybersecurity teams had limited capability to scale their human-centric cybersecurity resources to meet the ever-increasing number of cyber assets needing security analysis. Software development is growing exponentially, and the infrastructure supporting modern software systems is expanding to match the services being provided. Endpoints and APIs are increasing rapidly, and vulnerabilities and exposures in the enterprise environment have reached an unsustainable level, necessitating a new approach to the challenge.

In the programmatic era we now operate in, the focus must shift to supporting automated risk discovery, contextual and holistic prioritization of issues, and programmatic remediation of exposures. This new approach is made possible by the rise of API-accessible technology stacks, cloud-based infrastructure, large-scale data analysis capabilities, and, most recently, agentic AI. The automated discovery of cyber assets and event states, a cybersecurity data fabric that stores and normalizes asset, exposure, and threat data, along with an AI-based prioritization engine and remediation capability, represent the next iteration of enterprise security platforms.