Build a controls monitoring program yourself using data lakes and BI tooling
Leverage the tools you already have to analyze the data you can see. Reduces upfront costs but drains resource to build, manage, and maintain.
- Limited up front costs
- All data connections and integrations managed
- Can apply AI/ML if you have the data science and engineering resource to build, train, and maintain models
- No business context or data enrichment capabilities
Buyer's checklist
What building your own controls monitoring program entails
Limit up-front costs and use your existing data lake repository already ingesting data from various sources across your organization. Advanced analytics tools and AI capabilities can be applied to help manage and enrich the data — although you and your team will still be responsible for all analysis, reporting, and governance.
The ongoing management and maintenance of self-built monitoring tools is a huge resource drain. Without automated updates, in-house solutions become unreliable — especially when managing multiple data sources — and fail to scale alongside business needs. Critically, self-built solutions lack the AI-native intelligence layer and out-of-the-box compound risk detection that purpose-built CCM platforms provide.
Pros
- Can handle large volumes of data and support complex analysis
- Customize solution to unique business requirements
- Minimizes initial costs to business
Cons
- Minimum six month average implementation schedule
- Difficult to manage scale with competition for resource hindering development and long-term support
- No AI-powered triage, compound risk detection, or Key Drivers analysis
- No out-of-the-box framework mapping for DORA, NIS2, or CIS Controls
- Escalating product management costs as requirements grow
Panaseer recommendation
This approach appears to limit costs but often becomes too complex too quickly. In the long-term, many organisations turn to a purpose-built CCM platform to help translate data into actionable insights using advanced data lineage, analytics, and AI-powered triage — capabilities that take years to build in-house and are available from day one with Panaseer. Some abandon their home-grown attempt entirely when it fails to scale or becomes too expensive to maintain.