Getting Your Data Ready for AI — Let’s Talk Security, Traceability and Pipelines

AI is only as good as the data that informs it. Yet, too many organizations are rushing into AI adoption without first ensuring that their data is ready — this includes ensuring it’s secure, traceable and structured. Without AI-ready data, organizations can experience unreliable models, compliance headaches and unforeseen risks.

When planning for an AI launch, I think of AI as a high-performance vehicle needing three critical elements to run efficiently: clean fuel (data), a dependable GPS (traceability) and well-paved roads (pipelines). If any of these elements are missing, an AI journey could run off course or hit a dead end.

Here are a few best practices for preparing your data for AI to be secure, traceable and well structured.

1. Data security: Keep your AI fuel safe

Reality check: Over 60% of IT decision-makers acknowledged that AI has heightened their need for cybersecurity, leading to stricter data storage and access measures, according to a Rackspace Technology® survey.

If data is the lifeblood of AI, security is its immune system. Without proper safeguards, AI can be vulnerable to data breaches, bias and regulatory non-compliance.

Here are steps to secure your data and avoid these problems.

• Encrypt all data: Whether at rest or in transit, data should be protected with strong encryption. Think of it as sealing confidential documents in tamper-proof envelopes.
• Employ role-based access control (RBAC): Not everyone needs full access to your data. Granular permissions help ensure that only the right people gain access to the data they need to do their jobs. RBAC helps reduce the risks of accidental leaks or malicious breaches.
• Use synthetic data and anonymization: When handling sensitive information, leverage AI-generated synthetic data or anonymize your datasets. This helps maintain privacy compliance (e.g., GDPR, HIPAA, CCPA) while preserving valuable insights for AI application training.
• Conduct regular security audits and monitoring: AI models constantly evolve, and so do cyberthreats. Conducting frequent security audits, penetration testing and continuous monitoring helps mitigate risks before they become full-blown data breaches.

2. Data traceability: Manage your data’s journey

Reality check: The EU’s 2024 AI Act mandates that AI systems must be developed and used in a way that allows appropriate traceability and explainability. If an organization cannot trace its data’s origins and transformations, it could face compliance issues.

Have you ever tried fixing a problem without knowing what caused it? That’s what happens when AI models learn from data without a clear history. Data traceability helps ensure transparency, so you can track data from its source to its final destination.

Here are best practices for adding traceability to your AI data.

• Create audit trails and origin tracking: Just like tracking a package, every piece of data should have a history log, including where it originated, how it changed and who accessed it.
• Tag data with metadata: This makes it searchable, organized and easy to track over time. It’s a vital step to support compliance, debug AI models and maintain explainability.
• Monitor data pipelines in real-time: AI models are only as reliable as the data they consume. Tools like Apache Airflow, Databricks and MLflow help teams detect anomalies, flag inconsistencies and correct errors before they impact AI performance.
• Create a governance framework for regulatory compliance: With new AI regulations emerging globally, companies must establish data lineage frameworks to ensure their AI models meet compliance standards, such as the EU AI Act and U.S. AI Bill of Rights.

3. AI-ready pipelines: Keep data moving

Reality check: AI initiatives can struggle to progress beyond the experimental phase if organizations do not effectively manage data access throughout the development and production lifecycle, according to MIT Sloan.

Just as high-performance vehicles need well-maintained roads, AI needs structured pipelines to move data smoothly and eliminate bottlenecks.

Here are best practices for creating AI-ready pipelines for your AI data.

• Choose your processing speeds: AI models typically require both real-time and batch data flows:
  • Real-time processing: This is ideal for fraud detection, chatbots and personalized recommendations requiring instant data ingestion and decision-making.
  • Batch processing: This works best for monthly sales forecasts, compliance reports and historical analysis, where data is processed in chunks at scheduled intervals.
• Establish scalable storage solutions: AI models consume vast amounts of data. Cloud-based storage (e.g., Amazon S3, Google Cloud BigQuery, Azure Data Lake Storage) helps organizations scale seamlessly while maintaining high availability.
• Automate data cleaning and quality assurance: AI hates bad data. Automated data-wrangling tools (e.g., Trifacta, OpenRefine) help remove duplicates, fix inconsistencies and enhance data quality. Ensuring data purity is critical to preventing bias and improving model accuracy.
• Implement data versioning and change management: AI models rely on historical and evolving datasets. Data version control using tools like Data Version Control (DVC) supports consistency, reproducibility and rollback options in case of issues.

What’s next for the future of AI-ready data

AI is evolving — and so is data management. What does the future hold? In the next few years, I predict we’ll see these updates:

• Autonomous AI agents: This is AI that manages its own data workflows, reducing human intervention.
• Zero-trust security models: It will require every data request to be verified before access is granted, supporting a more secure AI ecosystem.
• Self-optimizing AI pipelines: We’ll see AI that continuously learns and refines its own data ingestion, transformation and storage processes.
• AI-driven compliance and audits: Regulatory requirements will become stricter, making automated compliance monitoring and AI ethics audits a vital part of AI development.

Before diving into AI, ask yourself: Is our data ready for what’s next? If the answer is yes, your organization is ready to achieve AI success.

At Foundry for AI by Rackspace (FAIR™), we are working with organizations in all business sectors to unlock AI’s full potential. We have the expertise and tools to help ensure that data is secure, traceable and well structured. Let’s build a strong foundation for AI in your organization together.