Automating Daily Compliance & Control Monitoring
Governance, Risk, and Compliance (GRC) teams spend their days chasing down audit evidence across fragmented systems. We build Continuous Control Monitoring (CCM) platforms that integrate deeply with your tech stack.
Senrok generates immutable audit logs automatically, bridging the gap between your real-world state and strict regulatory policies.
Automated Evidence Collection
Our agents securely connect to your internal systems (AWS, GitHub, HRIS) to pull real-time state configurations without human intervention.
Deterministic Policy Mapping
We map the unstructured system state against your strict internal compliance policies (YAML/JSON) using constrained LLM generation.
Real-Time Exception Flagging
When a system drifts from policy, the agent deterministically halts and routes the non-compliant evidence directly to the GRC queue.
{
"resource": "s3-bucket",
"id": "corp-audit-logs",
"public_access": false,
"encryption": "AES-256",
"mfa_delete": false
}policies:
- id: CC6.1
resource: s3-bucket
require:
public_access: false
encryption: AES-256
status: PASSWhy Standard Tools Fail
The 'Glorified Spreadsheet' Flaw
Traditional GRC tools are just static databases. They require humans to manually take screenshots of AWS consoles and paste them in as 'evidence'.
Fragile AI Wrappers
Out-of-the-box LLMs hallucinate compliance mappings. They lack the deterministic state-machine constraints required for true enterprise security.
The Senrok Approach
Production-grade Go microservices on Kubernetes. We build deterministic rules engines combined with LLM judgment for impenetrable audit trails.
How we help
Three ways to make complex operations easier to run.
Some teams need AI workflow automation. Some need better internal software for operators and reviewers. Some need cleaner backend and platform foundations before automation can work reliably.
Insights
AI & Product Engineering Insights
Practical essays for teams deciding what to automate, what to redesign, and what needs stronger software foundations first.
Approach
Senior builders, directly accountable.
No account managers translating the work from the outside. You work directly with the people designing and building the system, so strategy, architecture, and delivery stay connected.
Map the real workflow
We study the decisions, exceptions, handoffs, and unofficial workarounds your team already performs, not the idealized process in a slide deck.
Design the operating loop
Tools, memory, rules, review points, permissions, and evaluation criteria are defined before a single interface pixel is drawn.
Ship beside operators
The first release launches with the people who will use and trust the system. We instrument, iterate, and expand from evidence.
Leave control behind
Documentation, observability, and maintainability are part of delivery, so your team can operate and improve the system after launch.
Frequently Asked Questions
What is Continuous Control Monitoring (CCM) and how does AI help?
CCM is the practice of continuously verifying that your systems are operating within your defined controls, rather than spot-checking during audit cycles. AI agents read live state from your source systems, run the same control logic a human auditor would, and write evidence and exceptions to your GRC tool on a continuous schedule.
Can the system collect evidence when we don't have API access to a system?
Yes. The agent can be configured with read-only credentials, scheduled browser flows, or SFTP-based exports for systems without APIs. The integration layer treats them uniformly so the policy engine does not need to know which method was used.
How do you avoid false positives in automated compliance flagging?
Each control has a confidence threshold and a reviewer queue. The system only escalates to a finding when the rule fires consistently across the configured evidence window, and the reviewer can dismiss with a structured reason that feeds back into the policy logic.
Work with us
Discuss your compliance workflows
Walk us through your workflow and the systems involved. We'll provide an objective assessment of whether AI automation or traditional engineering is the best path forward.