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March 21, 2025

The AI Revolution Needs Encryption—Here’s Why It Matters

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In the age of artificial intelligence (AI), data serves as the lifeblood of innovation. However, the sheer scale and sensitivity of data used in AI systems make it a prime target for breaches, misuse, and regulatory scrutiny. Data encryption has emerged as a cornerstone of safeguarding AI processes, offering robust solutions to challenges like data security in case of leaks and traceability for compliance.

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Understanding Data Encryption

At its core, data encryption transforms readable data into an encoded format that can only be accessed with the appropriate decryption key. By making information unreadable to unauthorized users, encryption acts as a critical defense layer against unauthorized access, ensuring that sensitive data remains secure even in the event of a breach. In the context of AI training and algorithm usage, encryption plays a pivotal role at multiple stages—from data collection to storage and processing.

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Limiting Data Access to Prevent Leaks

One of the most significant concerns in AI is the potential for data leaks. The datasets used to train AI models often contain sensitive information, including personally identifiable information (PII), proprietary business data, or confidential medical records. A breach could have catastrophic consequences, ranging from legal repercussions to reputational damage.

Data encryption mitigates these risks by ensuring that only precisely identified information is shared with the AI training algorithm. In an encrypted data lake, data is inaccessible by default and must be explicitly shared with the AI engine to be utilized. This means that the data that is made available to the AI engine can be set to access only anonymous data.

Enhancing Data Traceability for Compliance

AI systems are subject to increasing scrutiny from regulators and watchdogs concerned about data privacy and ethical usage. Laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) mandate stringent controls over how data is collected, stored, and processed. Data encryption supports compliance in several ways:

  • Audit Trails and Accountability: Encryption tools can embed metadata and unique identifiers into datasets, enabling organizations to track who accessed the data, when, and for what purpose. This traceability fosters accountability and simplifies compliance reporting.

  • Controlled Access: Encryption allows for granular control over who can access specific datasets. By integrating encryption with access management systems, organizations can ensure that only authorized individuals or algorithms can use the data.

  • Anonymization and Pseudonymization: Encrypting data enables organizations to anonymize or pseudo-anonymize sensitive information, reducing the risk of exposure while still allowing AI models to extract meaningful insights.

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Enabling Collaboration Without Compromising Privacy

Collaboration is a key driver of innovation in AI. Researchers and organizations often need to share data to build more robust models or tackle complex problems. However, sharing sensitive data can expose it to security risks. Encryption bridges this gap by enabling secure collaboration:

  • Homomorphic Encryption: This advanced encryption method allows AI models to process encrypted data without decrypting it. As a result, organizations can collaborate on data-intensive projects without exposing raw datasets.

  • Secure Multi-Party Computation: Encryption facilitates scenarios where multiple parties can jointly compute AI models on shared datasets without revealing their individual data inputs.

Building Trust with Stakeholders

Trust is essential for the widespread adoption of AI. Users, regulators, and business partners need assurance that their data will be handled responsibly. Encryption plays a crucial role in building this trust by:

Demonstrating a Commitment to Security: Organizations that prioritize encryption signal their dedication to safeguarding stakeholder data.

  • Mitigating Reputational Risks: In the event of a breach, encrypted data is significantly less damaging than unprotected information, helping to preserve trust and reputation.

  • Supporting Ethical AI Development: Encryption aligns with the ethical principles of transparency, accountability, and fairness by ensuring that data privacy is a top priority.

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Enabling Compliance with Data Localization Requirements

Many countries enforce data localization or data residency laws, requiring organizations to store and process data within their borders. Encryption simplifies compliance with these regulations by:

  • Securing Cross-Border Transfers: When data needs to move across jurisdictions, encryption ensures that it remains secure and compliant with local laws.

  • Protecting Localized Data: Encrypting data stored in-country adds an extra layer of protection against local threats.

Challenges and Best Practices for Encryption in AI

While the benefits of data encryption are clear, implementing it effectively in AI workflows comes with challenges:

  • Performance Overheads: Encrypting and decrypting data can increase computational requirements, potentially slowing down AI model training. Organizations can address this by optimizing encryption algorithms and leveraging specialized hardware.

  • Key Management: Protecting encryption keys is critical, as compromised keys can render encryption useless. Best practices include using hardware security modules (HSMs) and key rotation policies.

  • Balancing Accessibility and Security: Ensuring that encryption doesn’t hinder legitimate access to data requires careful planning and integration with access management systems.

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The Future of Encryption in AI

As AI continues to evolve, so too will the methods for protecting data. Emerging technologies like quantum-resistant encryption and advanced homomorphic encryption are set to revolutionize data security. These innovations promise to address current limitations and provide even greater assurances of privacy and security.

Moreover, the integration of encryption with AI-specific frameworks will likely become standard practice. Organizations that adopt encryption early and proactively will be better positioned to navigate the complex landscape of data security, compliance, and ethical AI development.

Conclusion

Data encryption is more than just a security measure

It is a fundamental enabler of responsible and effective AI. By protecting sensitive information, ensuring traceability, and fostering trust, encryption empowers organizations to harness the full potential of AI while safeguarding the interests of individuals and stakeholders. As the AI landscape and more generally data security regulations grow increasingly complex, investing in robust encryption practices will be essential for driving innovation, maintaining compliance, and upholding ethical standards in a data-driven world.

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Summary of the Pros & Cons of Data Encryption

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At iCure we’ve been working hard to make sure Cardinal Backend & SDK’s take care of the implementation and key management complexity, optimizes performances and enable you to seamlessly access all data encryption’s benefits.

Medical records of more than 4mio patients are already encrypted and securely accessed by authorized doctors, patients and scientists thanks to iCure technology.

Visit us: https://icure.com

Start developing: https://cardinalsdk.com

Created with AI – reviewed & improved by humans. Copyright iCure 2025

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Information Security Policy

www.iCure.com

1. Introduction

The iCure universe is built on trust. Guaranteeing the confidentiality of the data that are entrusted to us is our highest priority.

The Information Security Policy of iCure abstracts the security concept that permeates every activity and abides by the ISO 27001:2013 requirements for Information Security, so that we ensure the security of the data that iCure and its clients manage.

Every employee, contractor, consultant, supplier and client of iCure is bound by our Information Security Policy.

2. Our Policy

iCure is committed to protecting the confidentiality, integrity and availability of the service it provides and the data it manages. iCure also considers protecting the privacy of its employees, partners, suppliers, clients and their customers as a fundamental security aspect.

iCure complies with all applicable laws and regulations regarding the protection of information assets and voluntarily commits itself to the provisions of the ISO 27001:2013.

3. Information Security Definitions

Confidentiality refers to iCure’s ability to protect information against disclosure. Attacks, such as network reconnaissance, database breaches or electronic eavesdropping or inadvertent information revealing through poor practices.

Integrity is about ensuring that information is not tampered with during or after submission. Data integrity can be compromised by accident or on purpose, by evading intrusion detection or changing file configurations to allow unwanted access.

Availability requires organizations to have up-and-running systems, networks, and applications to guarantee authorized users’ access to information without any interruption or waiting. The nature of data entrusted to us requires a higher-than-average availability.

Privacy is the right of individuals to control the collection, use, and disclosure of their personal information. Our privacy policies are based on the GDPR(https://gdpr-info.eu/) and can be augmented by added requirements of specific clients or law areas.

4. Risk Assessment

The main threats iCure is facing as a company are:

  1. Data Theft;
  2. Data Deletion;
  3. Denial of Service attacks;
  4. Malware;
  5. Blackmail and Extortion.

As providers of a solution used by developers active in Healthcare, we also have to anticipate the risks of:

  1. Attacks on our clients’ data, which could lead to major social damages and a loss of trust in our solution;
  2. Abuse of our solution by ill-intentioned clients, that could impact the quality of the service provided to the rest of our clients.

The motivation of the attackers in the latter cases can range from financial gain to political or ideological motivations.

A last risk is linked to the nature of the healthcare data we handle. We must ensure, that the data we handle are not used for purposes other than those for which they were collected:

A piece of data collected from a patient for the purpose of a medical consultation should not be available to third parties, not even a government agency.

5. Risk Management

The main principles we apply to manage the risks we face are:

  1. Confidentiality by design: All sensitive data is encrypted end-to-end before being stored in our databases. We do not have any access to the data we store. Our client’s customers are the only ones who can decrypt the data we store.
  2. Anonymization by design: Healthcare information that has to be stored unencrypted is always anonymized using end-to-end encryption scheme. This means that the link between the healthcare and administrative information must be encrypted.

Those two principles allow us to minimize the risks of data theft, blackmail, extortion, and coercion by government agency.

  1. Multiple real-time replicas, with automatic failover: We use a distributed database architecture to ensure that our data is available at all times. We use a master-master architecture, each data is replicated at least 3 times. Snapshots are taken every day to ensure that we can restore the data in case of a malevolent deletion event.
  2. Automatic password rotations: no single password can be used for more than 48 hours. Passwords are automatically rotated every 24 hours. In case of a password leak, we can limit the window of opportunity for an attack.

Those two principles allow us to minimise the risks of data deletion, denial of service attacks, and malware.

  1. Minimization of the attack surface: we deploy our systems in the most minimal way. We only expose the network services that are strictly necessary.
  2. Strict dependency management: we only use open-source software that is regularly updated and audited by the community. We favor dependency management software and providers that minimize the risk of supply chain poisoning.

Those two principles allow iCure to minimise the risks of intrusion by vulnerability exploit or supply chain attacks, two risks that could lead to data theft or data deletion.

6. Further Information

This policy is valid as of November 10th, 2022. For futher information please connect with us at privacy@icure.com

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Last update: September 13th, 2023

Quality Policy

www.iCure.com

At iCure SA, we are committed to excellence in all aspects of our work. Our quality policy is designed to provide a framework for measuring and improving our performance within the QMS.

1. Purpose of the Organization

The purpose of the QMS is to ensure consistent quality in the design, development, production, installation, and delivery of Data processing, security, archival, technical support and protection solutions for medical device software, while ensuring we meet customer and regulatory requirements. The document applies to all documentation and activities within the QMS. Users of this document are members of the iCure Management Team involved in the processes covered by the scope.

2. Compliance and Effectiveness

We are committed to complying with all applicable regulatory and statutory requirements, including ISO 13485: 2016 and ISO 27001:2013. We strive to maintain and continually improve the effectiveness of our quality management system.

3. Quality Objectives

Our quality objectives are set within the framework of this policy and as defined by our Software Development Lifecycle and are reviewed regularly to ensure they align with our business goals. These objectives serve as benchmarks for measuring our performance and guide our decision-making processes.

4. Communication

We ensure that our quality policy is communicated and understood at all levels of the organization. We encourage every member of our team to uphold these standards in their daily work whether they are employees, contractors, consultants, suppliers, clients or any other person involved in building our medical data management software.

5. Continuing Suitability

We regularly review our quality policy to ensure it remains suitable for our organization. This includes considering new regulatory requirements, feedback from customers, and changes in our business environment. By adhering to this policy, we aim to enhance customer satisfaction, improve our performance, and contribute to the advancement of medical technology

iCure SA

Contact: contact@icure.com

Last update: April 17th, 2024