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A COMPREHENSIVE TRUSTWORTHY FRAMEWORK FOR CONNECTED MACHINE LEARNING AND SECURE INTERCONNECTED AI SOLUTIONS

Project description

Trusted framework for AI risk assessment

The AI landscape requires a comprehensive framework to address threats and vulnerabilities but lacks a system for managing weaknesses in the AI supply chain. The EU-funded CoEvolution project is developing an integrated security, trust, and robustness (STR) assessment solution that combines AI models with context awareness and an AI model bill of materials (AIMBOM). The project aims to establish a universal STR risk assessment and security assurance hub aligned with EU regulatory frameworks. This initiative introduces new AI model descriptions and security monitoring to ensure compliance with EU directives on trust, fairness, data governance, and GDPR guidelines. Open-source trusted datasets and AI models developed by CoEvolution will enhance the hub’s ability to address evolving AI cybersecurity threats.

Objective

The contemporary AI landscape demands a holistic framework ensuring security across the supply chain and entire AI lifecycle. Despite existing adversarial attack techniques, a comprehensive end-to-end flow for identifying threats and vulnerabilities with associated risks is lacking. The EU, through initiatives like the AI Act, emphasizes safety and trustworthiness in AI applications but lacks a system managing weaknesses in a networked AI-supply chain. The CoEvolution project integrates its architecture components to create an end-to-end Security, Trust, and Robustness (STR) assessment solution, generating context-aware AI models characterized by their AI Model Bill of Materials (AIMBOM). The goal is a universal hub providing a coherent STR risk assessment and security assurance flow, aligning with MLDevOps and EU AI regulatory frameworks. The paradigm includes novel AI model descriptions, AIMBOM management, security monitoring, and context awareness. CoEvolution introduces a new STR paradigm based on Bills-of-Materials, offering a unified approach to describing AI models in supply chains, ensuring STR compliance with EU directives on trust, fairness, data governance, and GDPR guidelines. Open source trusted datasets and CoEvolution-developed AI models enhance the hub's capabilities, aiming for a robust, adaptable risk analysis and security assessment framework aligned with evolving AI cybersecurity threats.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-CL3-2023-CS-01

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Coordinator

EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLOGISTON
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 573 582,50
Address
PATISION 42
106 82 ATHINA
Greece

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Αττική Aττική Κεντρικός Τομέας Αθηνών
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 573 582,50

Participants (14)

Partners (1)

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