没有安全就没有人工智能
本文探讨网络安全在医疗技术人工智能应用中的关键作用，强调安全是AI应用的前提。
原文深度解读
本文由TQ-Group技术作家Manne Kreuzer撰写，主题为医疗技术中AI应用的信息安全。文章强调，AI模型依赖高质量数据，而医疗数据敏感，需通过完整性、保密性、可用性三大目标保障安全。TQ公司提供从嵌入式到云端的全栈安全开发服务，采用DevSecOps框架和“安全左移”原则，并提及MDR、IEC 81001-5-1、GDPR、CRA、RED等法规。文章与电子元器件供应链相关，因为TQ作为电子工程和制造服务提供商，其安全措施影响医疗设备的设计、制造和生命周期管理，进而影响供应链中的合规性和成本。关键数字包括TQ拥有300多名开发人员、30多年经验，以及Evertiq Expo哥德堡展会日期9月17日。
- 原文指出，全球医疗系统中关键IT中断和停机报告近年显著增加，勒索软件攻击持续威胁，每天发生多起重大事件。
- 原文引用TQ网络安全专家Sebastian Peralta Friedburg的观点：用于训练AI模型的数据必须具有代表性、结构良好且可追溯。
- 原文将信息安全目标分为完整性、保密性和可用性三类，并分别解释：完整性防止数据操纵，保密性控制患者数据访问，可用性确保数据、服务和设备随时可访问并防攻击。
- 原文提出安全措施分为技术、组织和法规三类，技术措施包括端到端加密、假名化/匿名化、基于角色的访问模型、零信任架构和标准化API；组织措施包括审计、日志、监控、培训和事件响应计划；法规措施包括MDR、IEC 81001-5-1、GDPR、CRA和RED。
- 原文强调“安全左移”原则，即安全要求、测试和验证提前到开发早期阶段，以降低成本、减少返工并缩短上市时间。
- 原文提到TQ拥有300多名开发人员、30多年经验，并提供从需求工程到售后服务的全生命周期支持，包括认证制造和测试实验室。
供应链影响
- 医疗设备制造商可能面临更严格的网络安全合规要求，取决于MDR、IEC 81001-5-1等法规的执行，这可能影响供应链中电子元器件的选型和设计。
- TQ等电子制造服务商提供的安全开发服务可能成为供应链中的关键环节，其能力可能影响医疗设备上市时间和成本。
- 安全左移原则可能促使供应链上下游更早介入安全测试，可能导致开发周期延长但后期返工减少，具体取决于项目执行。
- 对数据完整性和可用性的要求可能推动供应链中采用更可靠的存储和通信元器件，但具体影响取决于设备类型。
- 法规如CRA和RED可能增加供应链合规负担，可能影响元器件供应商的文档和测试要求，但具体取决于产品分类。
系统解读，仅作为判断线索，不构成备货、出货、涨价或投资建议。
作者：Manne Kreuzer，TQ-Group技术作家
一台AI驱动的手术机器人、一个智能监控系统或互联成像解决方案，如今可以显著改善临床工作流程——只要这些系统可靠运行。然而，这正是最大威胁所在：在全球医疗系统中，关键IT中断和停机的报告近年来显著增加。与此同时，网络安全专家警告称，勒索软件攻击仍然是一个持续威胁，每天都会发生多起重大事件。
AI模型的质量取决于其训练和运行所依据的数据。这在医疗技术中带来了一个根本性挑战：高性能应用（如自动患者监控或药物管理）需要高质量数据。同时，医疗数据尤为敏感，必须得到相应保护。
医疗技术中的AI需要安全数据
Sebastian Peralta Friedburg是技术公司TQ的网络安全、AI和软件开发专家，他深入参与了这一挑战。这家电子工程和制造服务的系统提供商拥有300多名经验丰富的开发人员，支持制造商从一开始就嵌入网络安全——保护整个技术栈的系统，从嵌入式解决方案、设备软件到云应用。
Peralta Friedburg解释说：“要使收集的数据适合训练AI模型，数据必须具有代表性、结构良好且可追溯。”
使用不合适或有缺陷的训练数据可能导致模型偏差和错误决策——对医疗诊断或治疗过程可能产生严重后果。同样关键的是谁有权访问这些数据以及如何控制访问。如果没有明确的访问规定，操纵、数据泄露甚至针对关键系统的定向网络攻击的风险将显著增加。
信息安全的三大核心目标
基于AI的医疗应用中的数据收集和处理因此必须满足严格要求，这些要求可以按照经典的信息安全目标来构建。
- 完整性：数据必须保持准确并防止操纵。错误数据可能危险地扭曲AI系统并导致错误预测。
- 保密性：对患者数据的访问必须严格控制并加以保护。敏感信息应仅对授权人员可访问，并尽可能进行匿名化或假名化处理。
- 可用性：数据、服务和设备必须始终可访问，同时防止中断、破坏和勒索软件攻击。
整体安全措施
从TQ的角度来看，确保安全数据收集需要技术、组织和法规措施的结合。
“我们区分三类：技术、组织和法规。只有它们有效互动才能确保真正的安全，”Peralta Friedburg解释说。
技术基础包括数据传输和存储的端到端加密，以及假名化或匿名化，使数据可用于AI应用而不暴露个人身份。基于角色的访问模型和明确界定的职责决定了谁可以查看或处理特定数据。零信任方法通过严格验证每个访问请求来补充这一点——无论请求来自哪个网络、系统或用户。
接口是另一个关键方面：当医疗数据在系统之间交换时，应仅使用具有明确认证和授权机制的标准安全API。这是防止不安全集成或因错误实现导致数据失控泄漏的唯一方法。
在组织方面，审计、日志和监控确保数据访问的可追溯性，并能及早发现异常。明确界定的访问权限和基于角色的权限概念对于确保敏感信息仅由授权人员访问或处理至关重要。
对临床和IT人员的培训促进安全处理数据和网络风险。事件响应计划同样重要：它们不仅应被记录，还应定期测试，以确保在事件发生时，系统能够被隔离、损害得到控制，并以结构化的方式恢复运营。
还存在明确的法规框架，要求“隐私和安全设计”。在医疗器械法规中，IT安全日益成为互联医疗产品的强制性组成部分。MDR和IEC 81001-5-1标准定义了具体的网络安全要求，而GDPR确立了数据最小化等原则。其他法规包括《网络弹性法案》（CRA）以及——取决于设备类型——《无线电设备指令》（RED）。因此，制造商不仅必须实施安全措施，还必须全面记录并在整个产品生命周期内维护这些措施。
通过DevSecOps实现安全设计
随着AI的兴起，变化速度正在加快：“网络威胁不断演变，更强大的AI模型不断涌现，新的法规要求也在出台，”这位软件专家警告说。“这就是为什么我们在TQ遵循基于安全软件开发生命周期的整体方法。”
具体来说，公司使用DevSecOps（开发、安全、运维）框架。这是一种持续开发和运维模型，将网络安全视为一个持续过程，而非一次性周期。在实践中，这意味着安全要求嵌入到架构和设计的早期阶段，安全测试在构建和测试管道中自动化，运维通过漏洞管理、结构化更新流程和持续监控来支持，以确保系统持续改进。
为什么早期安全投入值得
除了患者安全和合规性之外，还有另一个支持“早期安全”的有力论据：成本效益。漏洞发现和解决得越晚，成本就越高——无论是技术上、组织上还是监管审批方面。
这就是“左移”原则发挥作用的地方。安全要求、测试和验证被提前到开发早期阶段——集成到架构、设计和初始开发阶段，而不是仅在审批前或运营期间才处理。这降低了风险，提高了质量，防止返工，并避免认证过程中的意外。因此，上市时间缩短，即使规划和开发最初需要更多努力。
TQ的端到端专业知识
AI在医疗应用中的潜力巨大，但也带来了安全、计算性能和法规合规方面的严格要求。为了管理这种复杂性，与经验丰富的专家合作至关重要。
TQ在整个产品生命周期中支持制造商——从需求工程和规格开发到咨询研讨会。客户受益于公司紧密的跨学科合作（网络安全、AI、软件等）及其360度方法。这不仅包括整体开发，还包括完全认证的制造、认可的测试实验室以及售后服务，如过时管理，以实现可持续和有弹性的电子产品。
“在TQ，我们不仅拥有必要的能力，还拥有30多年的经验和众多成功的医疗项目，使我们能够在医疗技术等要求苛刻的环境中开发安全系统，”Sebastian Peralta Friedburg强调。
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Author: Manne Kreuzer, Technical Writer at TQ-Group
An AI-powered surgical robot, an intelligent monitoring system, or connected imaging solutions can significantly improve clinical workflows today — as long as these systems function reliably. However, this is precisely where the greatest threat emerges: Across global healthcare systems, reports of critical IT disruptions and outages have increased significantly in recent years. At the same time, cybersecurity experts warn that ransomware attacks remain a persistent threat, with several major incidents occurring on a daily basis.
AI models are only as good as the data they are trained and operated on. This creates a fundamental challenge in medical technology: High-quality data is required for powerful applications such as automated patient monitoring or medication management. At the same time, healthcare data is particularly sensitive and must be protected accordingly.
AI in Medical Technology Requires Secure Data
Sebastian Peralta Friedburg, an expert in cybersecurity, AI, and software development at the technology company TQ, is deeply engaged with this challenge. The system provider for electronics engineering and manufacturing services employs more than 300 experienced developers who support manufacturers in embedding cybersecurity from the outset – securing systems across the entire stack, from embedded solutions and device software to cloud applications.
Peralta Friedburg explains: “For collected data to be suitable for training AI models, it must be representative, well-structured, and traceable.”
The use of unsuitable or flawed training data can lead to biased models and incorrect decisions — with potentially serious consequences for medical diagnoses or treatment processes. Equally critical is the question of who has access to this data and how that access is controlled. Without clear access regulations, the risk of manipulation, data breaches, or even targeted cyberattacks that compromise critical systems increases significantly.
The Three Core Objectives of Information Security
Data collection and processing in AI-based medical applications must therefore meet stringent requirements, which can be structured along the classic objectives of information security.
- Integrity: Data must remain accurate and protected against manipulation. Incorrect data can dangerously distort an AI system and lead to false predictions.
- Confidentiality: Access to patient data must be strictly controlled and secured. Sensitive information should only be accessible to authorized individuals and, wherever possible, be anonymized or pseudonymized.
- Availability: Data, services, and devices must be accessible at all times while being protected against outages, sabotage, and ransomware attacks.
Holistic Security Measures
From TQ’s perspective, ensuring secure data collection requires a combination of technical, organizational, and regulatory measures.
“We distinguish between three categories: technical, organizational, and regulatory. Only their effective interaction ensures true security,” explains Peralta Friedburg.
Technical foundations include end-to-end encryption for data transmission and storage, as well as pseudonymization or anonymization to make data usable for AI applications without exposing personal identities. Role-based access models and clearly defined responsibilities determine who can view or process specific data. Zero-trust approaches complement this by rigorously verifying every access request — regardless of the originating network, system, or user.
Interfaces are another critical aspect: When medical data is exchanged between systems, only standardized and secured APIs with clearly defined authentication and authorization mechanisms should be used. This is the only way to prevent insecure integrations or uncontrolled data leakage due to faulty implementations.
On the organizational side, audits, logs, and monitoring ensure traceability of data access and enable early detection of anomalies. Clearly defined access rights and role-based permission concepts are essential to ensure that sensitive information is only accessed or processed by authorized individuals.
Training for clinical and IT staff promotes secure handling of data and cyber risks. Incident response plans are equally important: They should not only be documented but also regularly tested to ensure that, in the event of an incident, systems can be isolated, damage contained, and operations restored in a structured manner.
A clear regulatory framework also exists, requiring “privacy and security by design.” In medical device regulation, IT security is increasingly becoming a mandatory component of connected medical products. The MDR and the IEC 81001-5-1 standard define specific cybersecurity requirements, while the GDPR establishes principles such as data minimization. Additional regulations include the Cyber Resilience Act (CRA) and — depending on the device type — the Radio Equipment Directive (RED). Manufacturers must therefore not only implement security measures but also document them comprehensively and maintain them throughout the entire product lifecycle.
Security by Design with DevSecOps
The pace of change is accelerating with the rise of AI: “Cyber threats are constantly evolving, more powerful AI models are emerging, and new regulatory requirements are being introduced,” warns the software expert. “This is why we at TQ follow a holistic approach based on the Secure Software Development Lifecycle.”
Specifically, the company uses a DevSecOps (Development, Security, Operations) framework. This is a continuous development and operations model that treats cybersecurity as an ongoing process rather than a one-time cycle. In practice, this means that security requirements are embedded early in architecture and design, security testing is automated within build and test pipelines, and operations are supported by vulnerability management, structured update processes, and continuous monitoring to ensure ongoing system improvement.
Why Early Security Pays Off
Beyond patient safety and compliance, there is another strong argument for “security early”: cost efficiency. The later vulnerabilities are identified and resolved, the more expensive it becomes – technically, organizationally, and in terms of regulatory approval.
This is where the “shift-left” principle comes into play. Security requirements, testing, and validation are moved forward into the early stages of development – integrated into architecture, design, and initial development phases rather than being addressed only before approval or during operation. This reduces risks, improves quality, prevents rework, and avoids unpleasant surprises during certification. As a result, time-to-market is reduced, even if planning and development initially require greater effort.
End-to-End Expertise at TQ
AI in medical applications offers enormous potential but also comes with demanding requirements in terms of security, computing performance, and regulatory compliance. To manage this complexity, partnering with experienced experts is essential.
TQ supports manufacturers throughout the entire product lifecycle – from requirements engineering and specification development to consulting workshops. Customers benefit from the company’s close interdisciplinary collaboration (cybersecurity, AI, software, etc.) and its 360-degree approach. This includes not only holistic development but also fully certified manufacturing, an accredited testing laboratory, and after-sales services such as obsolescence management for sustainable and resilient electronics.
“At TQ, we not only have the necessary capacity but also more than 30 years of experience and numerous successful medical projects, enabling us to develop secure systems in a highly demanding environment such as medical technology,” emphasizes Sebastian Peralta Friedburg.
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