When will the EU AI Act come into effect?

The AI Act will become fully applicable in 2026 (except for a few provisions) with a phased enforcement timeline that began on August 1, 2024. Various provisions came into effect after their effective date. Provisions on prohibited AI practices came into effect in February 2025, with various other obligations and chapters coming into effect gradually in 2025, 2026, and 2027.

Which AI systems are considered high-risk?

High-risk AI systems include any AI systems that pose significant impacts on health, safety, or fundamental rights. These include AI used in critical infrastructure, medical devices, law enforcement, recruitment, education, and financial services. Any providers or deployers of such systems must adhere to the requirements related to risk management, data governance, transparency, and human oversight.

How will the EU AI Act be enforced?

The newly created European AI Office will oversee the enforcement of the AI Act. This office will work with the various supervisory authorities in the EU member states and coordinate efforts related to compliance, audits, investigation of violations, and future recommendations.

What penalties exist for non-compliance?

Non-compliance with the AI Act can result in fines of up to €35 million or 7% of a company's annual turnover, whichever is higher. The penalties are tiered based on the severity of the violation. Violations of prohibited AI practices carry the highest penalties, while non-compliance with other obligations (such as those for high-risk systems) can result in fines up to €15 million or 3% of global turnover. Providing incorrect information to authorities carries the lowest penalties, up to €7.5 million or 1% of global turnover.

What is the difference between data discovery and data mining?

Data discovery and data mining are two interconnected terms. In simple terms, data discovery is the process of extracting data from varying sources, while data mining goes a step further and extracts patterns and trends in large datasets.

Can data discovery be used for big data analytics?

Data discovery can prove to be fairly productive in big data analytics. Big Data involves large volumes of datasets, and analyzing those datasets can be challenging. Data discovery can help identify and understand data across structured and unstructured Big Data formats, including variety and velocity of data. Teams can use those insights to derive valuable outcomes from large datasets.

What kind of businesses can benefit from data discovery?

Any business that deals with data requires data discovery to gain a complete view and understanding of its data landscape for various analytical, business, tech advancements, and other purposes. For instance, data discovery can play a vital role for businesses in the healthcare, manufacturing, e-commerce, finance, or customer relationship management industries.

How do I choose the right data discovery tool for my business?

An organization needs to review and assess various components when looking for a data discovery and classification tool. For instance, a robust discovery and classification tool should offer integration with various infrastructures, systems, and applications. The tool must offer increased functionality, such as metadata management, data lineage, data flow management, data catalog, etc. It must also offer scalability and automation to deal with petabyte-scale data.

What are the challenges of implementing data discovery?

There are varying factors that could hinder an organization from carrying out effective data discovery, such as data sprawl, diverse data sources, shadow data assets, data velocity and volume, data silos, etc.

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Anas Baig is a Product Marketing Manager with a proven track record in the cybersecurity industry. He has been a prominent contributor to numerous esteemed publications, including Infosecurity Magazine, CSO Online, Tripwire, Security Affairs, Network Computing, Security Boulevard, and several other renowned cybersecurity blogs.

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