Artificial intelligence (AI) algorithm security focuses on protecting systems based on machine learning and AI algorithms from unwanted manipulation, attacks, malicious use, and data falsification. Threats concern both the learning of models (attacks on training data) and their use in production environments (attacks on input data, such as so-called adversarial attacks). With the growing use of AI in critical areas, such as finance, health care, and public safety, protecting the integrity, credibility, and reliability of AI algorithms is essential to ensure their safe and ethical use.
AI Algorithm Security
Type of technology
Description of the technology
Basic elements
- Protection of training data: Ensuring the integrity and reliability of the data used to train AI models.
- AI model protection: Securing finished models from unauthorised access and manipulation.
- Detection of attacks on algorithms: Detection of attempts to take control of the operation of models.
- Input security: Protection against malicious influence on input data used by AI models.
- Control and audit of algorithms: Monitoring the performance of algorithms and analysing their results to detect anomalies.
Industry usage
- Financial systems: Securing the algorithms used to analyse credit risk and transactions.
- Health care: Protecting diagnostic systems from falsifying results.
- Industry 4.0: Protecting production optimisation algorithms from external manipulation.
- Image recognition systems: Securing face and object recognition models against manipulation attacks.
- Cybersecurity: Using AI models to analyse threats and detect unauthorised access.
Importance for the economy
The security of AI algorithms is fundamental to sectors that use machine learning to make operational decisions, such as finance, medicine, and manufacturing. Manipulation of AI models can lead to wrong decisions, financial losses, and health and safety risks. Securing AI algorithms is also key to building customer trust and ensuring the ethical use of Artificial intelligence.
Related technologies
Mechanism of action
- AI algorithm security encompasses a set of practices related to protecting the machine learning process, including selecting appropriate training data, validating input data, monitoring model performance, and testing for resistance to attacks. These mechanisms help identify and block attempts to manipulate results and ensure that AI models work as intended by design. Protection includes both pre-deployment testing and real-time monitoring.
Advantages
- Maintaining integrity: Protecting AI algorithms from result falsification and manipulation.
- Data protection: Securing training and operational data from malicious changes.
- Reliability of models: Guaranteeing correct operation of models in a production environment.
- Operational security: Minimisation of the risk of poor decisions resulting from attacks on AI models.
- Regulatory compliance: Meeting ethics and security requirements for algorithms (e.g. AI Act).
Disadvantages
- Adversarial attacks: Attacks that involve making small changes to the input data to manipulate the results of AI models.
- Malicious teaching: Manipulating training data to influence the performance of models in a production environment.
- Lack of transparency: Difficulties in explaining how AI models make decisions, which can lead to misinterpretations of results.
- Dependence on data: AI models are prone to errors due to unreliable or incomplete input data.
- Breach of privacy: Attacks can lead to the leakage of personal data used in machine learning.
Implementation of the technology
Required resources
- Resistance testing tools: Systems to test models’ resistance to various types of attacks (e.g. FGSM and PGD).
- Monitoring systems: Software for monitoring the performance of algorithms and their results.
- Data analytics platforms: Tools to help assess the reliability of training data.
- Model management systems: Tools for the protection and version control of AI models.
- Data encryption systems: Mechanisms to secure input and training data.
Required competences
- Threat analysis: Ability to identify threats typical of AI systems.
- Machine learning: Knowledge of algorithms and processes for creating AI models.
- Data security: Ability to secure data at various stages of processing.
- Penetration tests: Knowledge of attack techniques and methods to defend against attacks in the context of AI.
- Model management: Ability to monitor and audit AI algorithms in a production environment.
Environmental aspects
- Energy consumption: High demand for computing resources to train and test AI models.
- Emissions of pollutants: Emissions from the operation of data centres that support machine learning.
- Raw material consumption: High demand for rare materials and electronic components in AI servers.
- Recycling: Problems with recovering materials from obsolete computing systems.
- Waste generated: Problems with disposal of equipment used for AI calculations.
Legal conditions
- AI ethics regulations: Standards for transparency and ethics in the use of AI (e.g. AI Act).
- AI security standards: Standards for protecting algorithms from attacks (e.g. ISO/IEC TR 24028).
- Data protection regulations: Regulations for the protection of data used in AI processes (e.g. GDPR).
- Standards for critical systems: AI security standards in sectors such as medicine and finance.
- IT security: Regulations for IT risk management in the context of AI applications.