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 are the methods of data discovery?

Data discovery methods include data scanning, data mapping, data cataloging, and the use of data discovery tools and software. These methods help organizations identify where personal data is stored and how it is processed.

What are the strategies for data discovery?

Strategies for data discovery involve creating a systematic approach to locate, classify, and manage data. This includes conducting data audits, engaging stakeholders, implementing data discovery tools, and documenting data flows and processing activities.

What are the key factors of data discovery?

Key factors of data discovery include understanding data sources, data types, data processing activities, data ownership, and data access controls. Effective data discovery also considers regulatory requirements and privacy implications to ensure data protection and compliance.

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July 16, 2023

8 Data Discovery Best Practices

With data growing at an unprecedented rate, organizations need to know what data they hold for data security & privacy as well as global compliance....

July 14, 2023

Data Discovery for GDPR: Ensuring Personal Data Compliance

With an increase in data breach incidents coupled with an increasing rise of data privacy regulations all across the globe, organizations are now required...

July 6, 2022

CCPA Data Discovery: The First Step Towards Personal Data Compliance

The consistent increase in frequency and severity of data breach incidents, coupled with the introduction of data privacy regulations such as GDPR and CCPA...

November 27, 2021

CPRA Data Discovery: The Step Towards Personal Data Compliance

Listen to the content Table of contents 7 Key Areas of Concern CPRA Data Discovery Tools Must Address Discovering Sensitive Personal Information (SPI) Honoring...

November 25, 2021

LGPD Data Discovery: The step towards personal data compliance

Global privacy laws require organizations to map their data processing activities, tighten up their security measures, build customer trust, fulfill legal and moral obligations and fulfill data...

October 25, 2021

How Sensitive Data Discovery Helps Secure PII (Personally Identifiable Information)

Personally identifiable information (PII) is defined by the US Department of Homeland Security as information that can uniquely identify an individual, such as an...

October 5, 2021

Data Discovery with Snowflake: 5 Things You Need to Know

The digital landscape is experiencing exponential growth in data, with 2.5 quintillion bytes being produced every day. Organizations analyze data to discover behaviors, trends,...

October 4, 2021

Why Data Discovery is Essential for PCI DSS Compliance

Companies are producing and processing data in massive volumes. This data is then scattered across a multitude of environments, such as on-premise infrastructure, SaaS...

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September 28, 2021

Best Practices to Overcome Data Discovery Challenges

Companies that are not leveraging big data may face imminent extinction, suggests a survey by Accenture. Data is an invaluable asset that allows organizations...

September 24, 2021

Data Discovery for Securing Sensitive Data on the Cloud

Data is moving to the cloud, creating a new challenge for organizations to protect their consumers’ data stored. The first step to securing sensitive...

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Discover shadow and cloud-native assets and accurately classify data

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Monitor user access to data and enforce least privilege controls

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Extract info from complex Unstructured Files, convert it into AI-ready formats, and sync to vector databases

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Data Curation and Sanitization for AI
Transform raw, unstructured files into data ready for model training and tuning

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Context-aware LLM Firewalls
Protect AI interactions with intelligent retrieval, response, and prompt firewalls

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Unstructured Data Governance
Manage and govern unstructured data to enable its safe use with generative AI

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Govern data for safe innovation

Data Discovery & Classification
Discover shadow and cloud-native assets and accurately classify data

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Unstructured Data Governance
Manage unstructured data to enable safe use with generative AI

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Data Access Governance
Monitor sensitive data access and prevent unauthorized use

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AI Governance
Establish controls for safe adoption of AI technologies including GenAI

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Data Catalog
Enable users to easily find, understand, trust and access the data they need

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Data Lineage
Automatically track changes and transformations of data throughout its lifecycle

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Conduct data quality checks and validation across various data types

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