For years, organizations have struggled with the challenge of truly *continuously* monitoring their risk posture. Traditional GRC approaches often rely heavily on issue tracking as a proxy for risk – essentially treating work orders as indicators of underlying control failures. While necessary for remediation, this indirect method is inherently delayed and inefficient. Imagine a scenario where a server patching control isn’t functioning optimally – the issue might surface as a vulnerability scan finding, requiring a ticket to be created, assigned, and resolved. This process can take days or weeks, leaving the organization exposed during that time. Furthermore, correlating those issues back to the actual risks they impact – like data breach or system downtime – requires significant manual effort and is often incomplete. This also creates a confusing overlap: are issues truly reflecting the severity of the risk, or simply representing standard operational tasks like routine maintenance? The cost of manually tracking thousands of issues and validating their completion simply isn’t scalable, leaving many organizations operating with a potentially inaccurate and outdated understanding of their true risk exposure, hindering informed decision-making and potentially increasing regulatory scrutiny.
Our AI-powered GRC system offers a paradigm shift by moving beyond issue-based tracking and focusing on the *controls* themselves. At its core, the system builds a dynamic knowledge graph that intelligently connects the dots between inherent risks (identified during risk assessments), target residual risks (the desired level of risk after controls are implemented), the policies governing those risks (like data security or access control policies), the controls designed to mitigate them (such as multi-factor authentication or vulnerability scanning), and the evidence demonstrating control operation (audit logs, system configurations, test results, etc.). This interconnectedness allows the system to dynamically evaluate residual risk. It doesn’t just check if a control *exists*, but assesses its *effectiveness* (both design and operation-wise) based on the corresponding policies and evidence. Crucially, the AI algorithms could recognize that not all controls contribute equally to risk reduction – a critical firewall rule will have a far greater impact than a minor logging configuration. The system also understands that discrepancies don't always signify complete failure; a minor deviation in a process might only partially impact control effectiveness. By directly examining control design (alignment with policy) and operational effectiveness (based on real-time and historical evidence), the system calculates an *actual* residual risk, highlighting any deviation from the targeted level and quantifying the magnitude of that deviation. Furthermore, the system could also generate those work orders to fix the issues, as necessary, providing additional information about what exactly needs to be done.
The result? A near real-time risk profile, driven by continuous control evaluation. Instead of relying on time-delayed and inaccurate proxy (issues or work orders), organizations can have a near realtime view of the risk posture. For example, if the AI detects a decreasing trend in successful MFA authentications, it can flag a potential control weakness before it leads to a compromised account. By directly evaluating the controls, rather than interpreting risk through the lens of work orders, we provide a significantly more accurate and actionable understanding of your security posture. This enables faster, more informed decision-making, better resource allocation, prioritized remediation efforts, and a truly preventative approach to GRC. Furthermore, the system provides a clear audit trail for compliance reporting and demonstrates a proactive risk management culture. This empowers organizations to strengthen resilience, achieve business objectives, and confidently navigate an increasingly complex threat landscape.