More and more companies are considering incorporating Artificial Intelligence into their processes, but many are unsure where to start, whether the data they handle is truly secure, or how to comply with regulations outside of Latin America.
If this sounds familiar, stick around because this will help you. 🚀
Step 1: Evaluate the Type of AI and its Risk Level
Before you jump into implementing AI, it’s important to understand how it’s classified according to its risk level under regulations such as the EU AI Regulation and US state laws:
🔹 Prohibited AI: Systems that manipulate behavior, mass surveillance, social scoring, or sensitive inferences without consent.
🔹 High-Risk AI: Applications in healthcare, education, credit, contracting, public safety, and critical infrastructure. These require audits and transparency.
🔹 Low-Risk and General Use AI: Chatbots, virtual assistants, and personalized recommendations, which must comply with explainability and transparency.
✅ Action: Before incorporating AI into your company, carefully review which category it falls into to ensure it complies with regulations.
Step 2: Transparency and Oversight
If your AI makes decisions that affect people, you need to make sure they’re clear and fair. There’s no room for AI to be a “black box” that no one understands.
🔹 Keys to compliance:
- Traceability: Document what data you use, how you make decisions, and what criteria you apply.
- Explainability: Enabling users to understand the decision-making process.
- Human supervision: In critical cases, there always has to be someone checking.
✅ Action: Implement internal AI audits and ensure all documentation is in order.
Step 3: Data Security and Cybersecurity
Companies that use AI have a responsibility to protect the data they handle, complying with regulations such as GDPR (EU) and DPDPA (Delaware, USA), among others.
🔹 Good security practices:
- Data minimization: Don’t collect more information than you really need.
- Encryption and anonymization: Protect your information to prevent leaks.
- Explicit consent: Don’t use data without clear authorization from users.
✅ Action: Apply security controls from the design stage and implement protocols to respond quickly if an incident occurs.
Step 4: Governance and Accountability
Having AI in the company is not just a matter of technology; it also involves defining internal policies to avoid legal or reputational issues.
🔹 What you can’t miss:
- AI Responsible Team: An area or point of reference that oversees regulations and ethics.
- Codes of conduct: Defining clear rules for the use of AI.
- Complaint channels: So that users and employees can raise questions or complaints.
✅ Action: Create an AI ethics committee to monitor projects and ensure they align with values and regulations.
Step 5: AI with Business Impact
AI isn’t magic or a whim. It must add real value and be aligned with the company’s strategy.
🔹 Key areas of impact:
- Operational optimization: Predictive maintenance, logistics.
- Customer Service: Advanced Chatbots, Personalization.
- Marketing and sales: Demand prediction, segmentation.
- Talent management: Automated selection, adaptive training.
✅ Action: Create an AI roadmap, prioritizing the use cases with the best returns.
Step 6: AI Training and Culture
For AI to truly work in the enterprise, it’s key that the team understands and uses it well.
🔹 How to build an AI culture:
- Train teams in the responsible use of AI.
- Avoiding bias through algorithmic fairness training.
- Encourage experimentation and continuous learning.
✅ Action: Implement AI workshops and promote the technology as part of the company’s DNA.
Step 7: Responsible Innovation and Regulatory Compliance
You can innovate with AI without crossing ethical or regulatory boundaries. There are no excuses.
🔹 How to do it right:
- Explore regulatory sandboxes: Test AI in controlled environments before launch.
- Join ethical AI initiatives to align yourself with best practices.
- Collaborate with regulators to stay up-to-date with regulations.
✅ Action: Work with AI and legal experts to ensure you comply with all regulations before launching a model into production.
If you want AI to be a strategic asset in your company and not a liability, you have to do things right from the start. By following these steps, you’ll be able to take full advantage of AI’s potential without unnecessary risks.
Is your company already working with AI? How are you approaching it? Tell us!
And if you need advice to implement DATA/AI solutions securely, do not hesitate to send us a message.