If you’ve been in the trenches of enterprise risk and compliance for any length of time, you’ve heard the pitch: “Automate your compliance and save your team hours.” Dozens of vendors have said it. Most have meant well. And nearly all of them have failed to deliver at the scale that enterprises require.
The core issue? These solutions were designed to make compliance look easier, not be more intelligent.
Automation in cybersecurity compliance is not a UX problem. It’s not a workflow problem. It’s a data interpretation and evidence validation problem, compounded by the complexity of today’s enterprise environments. Vendors built tools that could pull simple data through APIs or help startups manage SOC 2 checklists. But they weren’t built to operate in an environment where evidence is fragmented, control states change by the hour, and risk needs to be tied to business outcomes, not just audit findings.
We’re now at an inflection point. Advances in AI, specifically the emergence of vision models and dynamic control scoring, are providing us with the tools to finally solve both sides of the compliance automation problem. But to use them effectively, we need to understand why we’ve failed up until now.
Most early “compliance automation” tools focused on small organizations. Their environments were simple: a few cloud services, minimal on-prem infrastructure, and a tight team using standard tools. Pulling evidence via API was enough to check a box.
But in the enterprise, that model collapses under its own weight. APIs only get you so far when:
So we started to see large organizations shoehorn these lightweight tools into enterprise use cases, adding layers of manual validation, spreadsheet tracking, and audit prep on top of their “automated” systems. The result: more noise, not less. Automation fatigue.
True continuous compliance automation in the enterprise requires two things working in lockstep:
Let’s break these down.
It’s one thing to collect data. It’s another to validate that the data actually contributes to control effectiveness and is even mapped to the proper control.
APIs can tell you what a tool says. But what about what it shows? That’s where AI vision models are now playing a transformative role. These models can ingest screenshots, exported reports, PDF scans, and evidence that would typically require a human auditor to review and extract the relevant state information to enrich controls.
This opens the door to validating systems that were previously opaque. Vision models enable us to integrate data from legacy or third-party systems into the automation loop without requiring brittle integrations or custom connectors. They mimic how a human would visually assess a system and ask: Does this show the control is in place and operating as intended?
When you combine that with structured data from APIs, you obtain a more complete, accurate picture of your compliance posture, one that reflects reality, not just assumptions.
The second piece is just as critical: scoring controls continuously based on the most current data available.
Most systems still score controls based on static assessments, such as quarterly reviews, interviews, and point-in-time evidence. But controls degrade. Systems change. New assets spin up. Threats evolve.
Automated control scoring involves evaluating the effectiveness, implementation, and consistency of controls in real-time and adjusting those scores as underlying data changes. This is the basis of Continuous Control Monitoring (CCM). It’s not just about knowing if a control exists; it’s about knowing how well it’s working, right now.
This is what turns compliance from a checkbox exercise into a living, breathing part of your risk program.
So why have so few vendors delivered on this dual mandate?
AI is not a silver bullet, but for compliance automation, it’s the first real shot we’ve had at scale. When implemented thoughtfully, it enables us to solve both sides of the automation compliance challenge:
For large enterprises managing dozens of frameworks and thousands of systems, this is the only viable path forward.
However, we must build it differently this time. Not with dashboards and workflow overlays, but with platforms that understand context, adapt over time, and reflect the real-world complexity of modern infrastructure and regulation.
The future of compliance automation isn’t about faster audits. It’s about better decisions. And the organizations that embrace this shift toward continuous, intelligent, and context-aware compliance will not only meet their obligations faster but also run more resilient and agile businesses as a result.
Dive into Agentic Evidence Collection with our solution brief.
Compliance automation refers to the use of technology to streamline the processes of collecting evidence, validating control effectiveness, and demonstrating adherence to cybersecurity frameworks. It reduces manual effort, improves accuracy, and helps security teams meet regulatory obligations more efficiently.
AI is enabling a new generation of compliance automation by:
Traditional tools focus on automating checklists and documentation through API calls and rules-based logic. AI-driven platforms, on the other hand:
Continuous Control Monitoring (CCM) refers to the automated, ongoing validation of control effectiveness across your environment. It utilizes real-time data to assess whether controls are correctly implemented and functioning as intended, which is essential for modern, agile risk and compliance management.
Yes, with the help of computer vision models and natural language processing (NLP). These technologies enable platforms to extract meaning from non-standardized evidence, such as PDFs, screenshots, emails, and configuration files, allowing for comprehensive and accurate compliance assessments.
Controls may be “in place” in one part of the business but not in another. Context determines whether evidence is sufficient or if a control is effective. AI platforms that understand and apply this context enable more informed and effective compliance decisions.
Look for platforms that:
Integrate easily without requiring extensive custom development. Avoid tools that focus only on workflows, dashboards, or narrow checklist fulfillment.