{"id":7132,"date":"2026-06-09T06:35:09","date_gmt":"2026-06-09T06:35:09","guid":{"rendered":"https:\/\/www.imt-soft.com\/?p=7132"},"modified":"2026-06-09T07:09:24","modified_gmt":"2026-06-09T07:09:24","slug":"scaling-ai-infrastructure-data-quality-storage-retention-challenges","status":"publish","type":"post","link":"https:\/\/imt-soft.com\/en\/2026\/06\/09\/scaling-ai-infrastructure-data-quality-storage-retention-challenges\/","title":{"rendered":"Scaling AI Infrastructure: Data Quality, Storage & Retention Challenges"},"content":{"rendered":"\n
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Scaling AI Infrastructure: Data Quality, Storage & Retention Challenges\n\n\n<\/h1>\n <\/div>\n
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Storing logs for 10 years is mandatory – how do you do that without breaking the bank?<\/strong><\/em><\/p>\n\n\n\n

If you’re running high-risk AI in financial services, healthcare, or any regulated sector, that question is no longer theoretical. The EU AI Act’s automatic logging and technical documentation requirements are real and they generate data volumes that most enterprise infrastructure was never designed to handle.<\/p>\n\n\n\n

The good news: organizations that design the architecture correctly from the start will keep costs manageable. The ones that bolt compliance onto an existing setup after deployment will pay far more – in storage bills, remediation effort, and regulatory risk.<\/p>\n\n\n\n

This article can analyse compression, data versioning, and secure storage strategies that satisfy 10-year record-keeping rules, while avoiding ballooning costs. If you haven’t yet mapped your AI systems to their EU AI Act risk tier, our EU AI Act compliance guide<\/u><\/a> is the right starting point before going further.<\/p>\n\n\n\n

1. The Scale Problem: How Much Data Are We Actually Talking About?<\/h2>\n\n\n\n
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High-risk AI systems generate far more data than most infrastructure teams plan for. Consider what automatic logging actually captures:<\/p>\n\n\n\n

\u2022 Every inference event – input data, model version, output, confidence score, timestamp<\/p>\n\n\n\n

\u2022 Every human override or escalation<\/p>\n\n\n\n

\u2022 Every data ingestion and transformation event upstream<\/p>\n\n\n\n

\u2022 Model retraining runs, evaluation metrics, version changes<\/p>\n\n\n\n

\u2022 Operational events: latency, errors, fallback activations<\/p>\n\n\n\n

For a mid-sized financial institution running an AI credit scoring system across tens of thousands of daily decisions, that adds up to hundreds of gigabytes per day. For a healthcare network processing AI-assisted medical imaging, the number is orders of magnitude larger – imaging files alone run in the gigabyte range per study.<\/p>\n<\/div>\n\n\n\n

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\nThe EU AI Act\u2019s Article 12 requires high-risk AI systems to have \u201clogging capabilities enabling automatic recording of events.\u201d For certain systems – particularly those in law enforcement, border control, and infrastructure – logs must be retained for a minimum of six months<\/strong>. For healthcare and financial services AI, sector-specific regulations extend this significantly. Swiss banks subject to FINMA oversight, and healthcare providers under EU health data regulation, should plan for ten-year retention obligations across audit-critical records.\n\n <\/p>\n<\/div>\n