AI is supposed to make your business faster, smarter and more competitive, but most projects are lacking. The Cloud Security Alliance (CSA) says the real problem is that companies stuff themselves into old, rigorous processes that can't maintain AI.
“The adoption of AI in business and manufacturing is at least twice as often as successful,” CSA writes. “Companies are trying to integrate AI into an outdated, rigorous process structure that lacks transparency, adaptability and real-time data integration.”
CSA introduces a model called Dynamic Process Landscape (DPL). It is a framework that moves AI adoption from fragmented automation to structured, compliant, strategically aligned workflows.
Overview of dynamic process landscapes (source: CSA)
Governance gap
Most automation efforts fall apart because organizations lack process transparency. The dynamic process landscape requires teams to understand the core workflow before they can deploy AI. In other words, mapping dependencies, defining the role of human monitoring, and ensuring data flow are well understood.
For CISOs, governance interests are high. Improperly deployed AI can expose sensitive data, break compliance rules, and erode operational security. The DPL framework is designed to embed explanability and auditability in all AI decisions, and supports tamper prevention logs, loop (HITL) checkpoints, and escalations to trigger triggers when anomalies occur.
This is a model that takes compliance seriously while allowing AI to operate autonomously within structured guardrails.
Uncontrolled power is responsible
The CSA advocates for distinguishing between innovation and recklessness. Just because AI can be deployed doesn't mean that it is a particularly unnegotiable place where human accountability is particularly regulated.
“AI does not design a landscape of processes,” the author warns. “Their power is to automate processes, make real-time and data-driven decisions, enable in-situ anomaly detection, and enable timely intervention and continuous verification of the system.”
This approach brings responsibility back to security and governance leaders. If your AI system is operating without visibility, traceability, or monitoring, you are not innovating. You are gambling.
Three paths to implementation
Rather than prescribing a single implementation method, CSA outlines three strategic options for adopting a DPL model.
1. Greenfield: Ideal for new business units and startups. This allows you to build a dynamic process landscape from scratch without legacy constraints.
2. Parallel sandbox: Runs DPL with existing processes in a shadow environment. It is suitable for highly regulated industries such as healthcare and finance.
3. Event trigger adoption: Implement DPL in the target area if changes are already in progress due to compliance updates or competitive pressures.
All three methods require stringent controls including predefined KPIs, escalation paths and success criteria before moving your AI system into production. The CSA emphasizes that automation should not exceed governance maturity.
“CISOS must carry out a thorough gap assessment of processes (business) and data (information),” said Dr. Chantal Spleiss, co-chair of the CSA AI Governance and Compliance Working Group. However, technical capabilities alone are not enough. A successful transition to DPL depends heavily on leadership buy-in and the change culture across the company. “If the transition is fully supported by business and leadership, then businesses are ready for DPL,” explains Dr. Spleiss. “The culture of change is critical when employees, compliance and quality departments, and data management teams are part of the crew.”
This transformation is not just about implementing automation. This is a strategic change that can boost your overall business. However, without a basic framework, DPL risks being liable. “If the basic framework is not properly implemented with standards, best practices and regulations to maintain the simplest and reliable possible, bolted DPLs can collapse under their own complexity,” warns Dr. Spleiss.
For organizations in a regulated industry, strict sandboxing is not an option. That is a legal requirement. “Sandboxing is essential and legally necessary, covering peak scenarios, edge case workflows and full audit trail reviews,” Dr. Spleiss said. Although not required for other sectors, we strongly recommend that you apply the same approach to ensure resilience and reliability.
First build the foundation
Many organizations lack the digital maturity that AI needs to flourish. This includes reliable data pipelines, process visibility, and executive buy-ins. The CSA warns that skipping these basics can interfere with AI initiatives no matter how advanced the model is.
Researchers outline core preparation questions:
- Is your workflow clearly mapped and understood?
- Is your data governance robust?
- Are you in place with the HITL process?
- Can I explain and reverse AI decisions?
These are important questions for CISOs who often carry the burden of defending the deployment of AI to regulators and boards.
Why is this important?
New regulations such as the EU's AI Act and the NIS2 Directive increasingly retain responsibility for the systems that organizations and their executives deploy. The CSA is calling for this trend. “It is worth noting that European law emphasizes the personal accountability of senior management.”
In other words, if an AI system makes a bad decision, it's not something the vendor explains to the auditor. It will become you.
