AI Journey: The Path to Safe and Productive AI Use

Artificial intelligence is transforming work practices, processes, and business models faster than any other technology in recent decades. The question is no longer whether AI will be used, but how consciously we manage its use: Which use cases create measurable added value? What data should be fed into AI systems? And who is responsible for the results, risks, and decisions?

• Those who wait too long will lose practical experience, valuable know-how, and possibly competitive advantages.

• Those who implement AI without clear rules simultaneously open up new vulnerabilities: Confidential information can leak unchecked into external services, flawed results can influence decisions, and regulatory requirements can fall by the wayside.

A structured AI journey bridges these two perspectives. It outlines the step-by-step path to productive, secure, and transparent AI usage.
Good AI governance supports innovation. It clarifies responsibilities, protects sensitive data, and creates the framework within which teams can safely develop, test, and deploy AI applications.

AI is transforming every company, often unnoticed

AI is changing work practices, decision-making processes, and how knowledge is managed. Marketing creates campaigns, Sales generates proposals, Development uses coding agents, HR generates application documents, and Finance automatically evaluates contracts.

Nevertheless, many companies believe they are not yet using AI. In fact, employees have often been using AI services for quite some time via personal accounts, browser extensions, or AI features already enabled in Microsoft 365 and other SaaS solutions—without this having been a conscious decision.

Making existing AI usage visible and manageable is therefore the first step on the AI journey.

The AI journey does not begin with technology

but with transparency. Before companies invest in tools and licenses, they should therefore answer three questions: Which AI services are already in use? What data is flowing out of the company as a result? And which use cases offer the greatest benefit?

An AI security assessment helps identify actual usage, realistically assess risks, and prioritize investments. Guidelines such as ISO 42001, MITRE ATLAS, NIST AI RMF, and the CSA AI Security Guidelines serve as a reference.

The following comparison illustrates just how large the gap can be: While officially only three to four AI solutions are often approved, network traffic and SaaS admin consoles can sometimes reveal more than 40 AI services.

This discrepancy is the starting point for targeted measures. Only what is known can be effectively managed.

The Five Dimensions of a Successful AI Journey

A successful AI journey requires more than just technology. It combines strategy and governance, data and information protection, technology and security, processes and business value, as well as people and culture. Only the interplay of these five dimensions lays the foundation for secure, productive, and scalable AI usage.

Table 1: Five Dimensions of a Successful AI Journey

Governance provides the guiding framework: Clear responsibilities, risk-based reviews, and technical guardrails enable teams to implement AI use cases quickly and securely.

• If the right balance is missing, governance becomes an obstacle rather than a catalyst:
  Too little governance: Good ideas fail due to a lack of approvals or become uncontrolled risks.
• Balanced governance: Clear, practical guidelines enable teams to rapidly test innovations and safely scale successful use cases.
• Too much governance: Rigid rules that are out of touch with reality prevent the very innovation they are meant to enable.

Getting Started for Businesses: Launching the AI Journey in Five Phases

A common misconception is: “Before we can deploy AI, we first need a comprehensive AI governance framework.” In practice, governance grows most effectively alongside the first use cases. This creates guidelines that are based on real risks and actual benefits.

Phase 1 – Make AI usage visible

A robust inventory in the form of an AI security assessment provides transparency regarding the AI tools in use, data flows, and risks, thereby laying the foundation for an initial risk assessment. It also identifies embedded AI functions and informally used services, thereby specifically depriving “shadow AI” of its breeding ground.
To achieve this, multiple information sources are consolidated:

• Network and proxy logs: Analyze access to known AI domains
• SaaS admin consoles: Check for activated AI features and add-ons in Microsoft 365, Salesforce, or Google Workspace
• Surveys of business units: Identify informally used AI tools that may not be visible in technical logs
• Data flows and use cases: Evaluate data processing, assess risks, and inventory identifiable use cases

How well is your company positioned for the use of AI? With a gap analysis based on ISO/IEC 42001:2023, you can identify gaps in your AI management system, assess risks, and receive concrete, prioritized recommendations for action.

Phase 2 – Define Guidelines and Responsibilities

Governance rarely fails due to a lack of will, but rather due to unclear responsibilities. Four roles establish clear responsibilities:

• AI Security Officer: technical security review and operational approval within defined guidelines
• Data Protection Officer (DPO): Assessment of data protection risks
• Use Case Owner: Subject-matter expertise in the respective area
• AI Board: Strategic oversight and decision-making in high-risk cases (e.g., in a monthly meeting) or when defined approval thresholds are exceeded

The AI Board combines strategic, operational, and oversight functions. It develops the AI vision, defines guidelines and standards, prioritizes projects, allocates resources, and supports pilot projects through to scaling. It also monitors the performance and risks of AI systems, ensures compliance with ethical and data protection requirements, and reports regularly to relevant stakeholders. The committee should bring together strategic leadership, AI expertise, subject matter experts, and data protection specialists. The review process is risk-based:

Table 2: Risk-Based Review and Approval Process for AI Applications

Phase 3 – Ensuring the Technically Secure Use of AI

Technical guardrails protect data, restrict access, and ensure transparency in AI usage:

• Role-based permissions

• Multi-factor authentication (MFA)

• DLP rules specifically for AI prompts (traditional DLP systems typically scan emails and uploads, but not text entries in browser tools)

• API protection (e.g., proprietary API and use of API keys from external AI providers)

• Comprehensive logging and monitoring

Cost transparency through budget limits and usage analytics is also important, as is documentation of the model versions used. This facilitates traceability, compliance, and the investigation of security incidents.

For AI agents, additional protective measures are required: isolated runtime environments such as sandboxing or containers, clearly defined access rights to data and network resources, and a zero-trust approach. This approach consistently verifies every request and every access attempt rather than assuming trust by default.

Retrieval-Augmented Generation (RAG) systems deserve special attention. They supplement AI responses with information from internal or external knowledge sources. Outdated or manipulated content can lead to erroneous results. RAG systems must therefore regularly validate sources and consistently inherit permissions: An AI assistant may only search documents that the person making the request is actually authorized to access.

“The goal is not to prevent AI, but to make it safe and productive to use.”

Phase 4 – Targeted Skills Development

General AI training for everyone falls short. What matters most are formats that address the typical tasks and risks of each role:

• Employees: safe use of generative AI based on their own work tasks
• Development teams: prompt injection, secure AI development, and AI security using concrete examples
• Managers: liability, responsibility, and regulatory requirements

A network of AI Champions (one to two designated individuals per department) fosters the exchange of experiences between teams. This ensures that insights emerge early on and are continuously incorporated into processes and governance.

Phase 5 – Scaling Successful Use Cases

Only now does the actual transformation begin, and pilot projects evolve into productive solutions. New processes emerge, and AI agents take over recurring tasks.
Every productive AI use case requires:

• a business owner

• a technical owner

• defined key performance indicators, such as time savings or maximum tolerated error rate

• a documented risk profile regular reviews

For autonomous AI agents, targeted red teaming complements these controls. Before the system goes live, it tests whether manipulated inputs can cause an agent to perform undesirable actions—such as exporting data or approving a transaction.
This transforms individual experiments into a sustainable AI program. Uncoordinated siloed solutions lose ground, while proven use cases can scale securely.

Governance Accelerates the Secure Use of AI

Many companies believe that governance is a barrier to innovation. In practice, the opposite is usually true:

• Clear responsibilities speed up decision-making: It is unambiguous who reviews and approves AI use cases.
• Defined approval processes ensure security: Teams know which requirements apply to new ideas.
• Technical guardrails enable the secure use of AI: They protect data, restrict access, and ensure that controls are traceable.
• Awareness prevents avoidable errors: Employees identify risks early and use AI responsibly.

Governance builds trust by ensuring transparency, clear responsibilities, and a reliable approach to data and risks—thereby laying the foundation for acceptance and innovation.

Lay the foundation in 90 days? It’s possible!

You can prepare for the AI journey in a structured way within 90 days. All it takes are a few initial, clearly prioritized steps:

• Establish transparency regarding existing AI usage

• Assess data flows and risks for the first time

• Define responsibilities and a simple AI policy

• Launch role-specific training

• Identify two to three pilot use cases with designated owners and success criteria

After that, the AI journey can be expanded step by step. Every control must be deliberately designed from the start; perfection can grow along with the requirements.

Conclusion: Integrate AI responsibly into the value chain

The danger today lies not only in using AI, but also in using it in an uncontrolled manner—or not using it at all. Those who wait too long lose expertise and valuable learning time. Uncontrolled AI use, on the other hand, increases risks to data, compliance, and business processes.

The AI Journey combines innovative drive with effective governance, speed with security, and new technology with clear accountability. Companies don’t have to wait for a perfect overall strategy—they can get started today!

The key is a deliberate, structured start with guidelines that evolve alongside the first use cases. After all, the organizations that succeed are those that not only adopt AI quickly but also integrate it into their business processes in a secure, responsible, and trustworthy manner.

Embark on your AI Journey with us! We’ll help you make your use of AI safe and productive.

Caption: Image generated by AI