How to build AI into your business without breaking compliance

AI is supposed to make businesses faster, smarter, and more competitive, but most projects fall short. The Cloud Security Alliance (CSA) says the real issue is companies cramming AI into old, rigid processes that just can’t keep up.

“AI adoption in business and manufacturing is failing at least twice as often as it succeeds,” the CSA writes. “Companies are trying to integrate AI into outdated, rigid process structures that lack transparency, adaptability, and real-time data integration.”

CSA introduces a model called the Dynamic Process Landscape (DPL). It’s a framework that shifts AI adoption away from fragmented automation and toward structured, compliant, and strategically aligned workflows.

Dynamic Process Landscape overview (Source: CSA)

The governance gap

Most automation efforts fall apart because organizations lack process transparency. Dynamic Process Landscape requires teams to understand their core workflows before introducing AI. That means mapping dependencies, defining human oversight roles, and ensuring data flows are well understood.

For CISOs, the governance stakes are high. Improperly deployed AI can expose sensitive data, break compliance rules, and erode operational security. The DPL framework is designed to embed explainability and auditability into every AI decision, supporting tamper-proof logs, human-in-the-loop (HITL) checkpoints, and escalation triggers when anomalies occur.

It’s a model that takes compliance seriously while still allowing AI to operate autonomously within structured guardrails.

Power without control is a liability

The CSA makes a point to distinguish between innovation and recklessness. Just because AI can be deployed doesn’t mean it should be, especially in regulated environments or where human accountability is non-negotiable.

“AI doesn’t design the process landscape,” the authors caution. “Its power is to automate processes, to make real-time and data-driven decisions, and to allow for the detection of anomalies on the fly allowing timely intervention and continuous validation of the system.”

This approach puts the onus back on security and governance leaders. If your AI systems are operating without visibility, traceability, or oversight, you’re not innovating. You’re gambling.

The three paths to implementation

Rather than prescribe a single implementation method, the CSA outlines three strategic options for adopting the DPL model:

1. Greenfield: Ideal for new business units or startups. This lets you build Dynamic Process Landscape from scratch with no legacy constraints.

2. Parallel sandboxing: Run DPL alongside existing processes in a shadow environment. This is well-suited for highly regulated industries like healthcare or finance.

3. Event-triggered adoption: Implement DPL in targeted areas when change is already underway due to compliance updates or competitive pressures.

All three methods require tight controls, including pre-defined KPIs, escalation paths, and success criteria before moving AI systems into production. The CSA stresses that automation must not outpace governance maturity.

“CISOs need to perform a thorough gap assessment for processes (business) and data (information),” Dr. Chantal Spleiss, Co-Chair of the CSA AI Governance and Compliance Working Group, told Help Net Security. However, technical capability alone is not enough. A successful transition to DPL depends heavily on leadership buy-in and an enterprise-wide culture of change. “A company is ready for DPL if the transition is fully supported by the business and leadership,” Dr. Spleiss explains. “A culture of change, where employees, the compliance and quality departments and the data management team are part of the crew, is extremely important.”

This transformation is more than just the implementation of automation. It is a strategic shift that can elevate the entire business. But without a foundational framework, DPL risks becoming a liability. “If this is not done properly by applying standards, best practices and regulations to keep the basic framework as simple and reliable as possible, bolted-on DPL might collapse under its own complexity,” Dr. Spleiss warns.

For organizations in regulated industries, rigorous sandboxing is not optional. It is a legal requirement. “Sandboxing is essential and legally required and covers peak scenarios, edge-case workflows, and full audit-trail reviews,” Dr. Spleiss notes. While not mandatory for other sectors, he strongly recommends applying the same approach to ensure resilience and reliability.

Build the foundation first

Many organizations lack the digital maturity needed for AI to thrive. That includes reliable data pipelines, process visibility, and executive buy-in. The CSA warns that skipping these basics can sabotage any AI initiative, no matter how advanced the model.

The researchers outline core readiness questions:

• Are your workflows clearly mapped and understood?
• Is your data governance robust?
• Do you have HITL processes in place?
• Can AI decisions be explained and reversed?

These are essential questions for CISOs, who often bear the burden of defending AI deployments to regulators and the board.

Why this matters now

New regulations, such as the EU’s AI Act and NIS2 Directive, increasingly hold organizations and their executives accountable for the systems they deploy. The CSA calls out this trend: “It is worthy to note that the European legislations NIS2 and DORA emphasize the even personal accountability of senior management.”

In other words, if your AI system makes a bad decision, it won’t be the vendor explaining it to auditors. It will be you.