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== What role for the cyber security advisor in Artificial Intelligence?== The cybersecurity advisor for data scientists : * Ensures that the data science team implements the security requirements of their organisation and regulators, applicable to their working environment and the AI models produced; * Maintains a culture and reflexes of cybersecurity in the data science team; * Relays the needs of the data science team to the cybersecurity correspondents in his/her organisation. == Day-to-day activities == As a member of (or seconded to) a data science team, the cybersecurity advisor for data scientists must : * Carrying out a daily security watch on the threats and vulnerabilities of AI models and their working environment (including languages, frameworks, libraries, infrastructure, etc.) so that the team can deal with them; * Provide the team with the applicable cybersecurity solutions: ::- Interact with the cybersecurity correspondents in his/her organisation to raise new needs and obtain appropriate solutions; ::- Capitalising on/developing protection mechanisms, security function libraries, scripts, etc. to secure the models developed and their data; ::- Monitor data science security solutions as they mature; * Train colleagues in the cyber risks to which their environment and models are exposed, as well as in good security practices for development and data handling; * Verify the application of security measures in their working environment; * Help colleagues to integrate "by design" security into the models they design, in particular by integrating mechanisms to detect critical data leakage or model manipulation/deflection; * Carry out code and model security reviews and validate before going into production. == Expected key competencies == === Organisational skills === * Understanding of business issues, providing use cases to be developed to better anticipate harmful deviations from the model and better target dataset protection. * Good knowledge of the organization's cybersecurity ecosystem/community * Capitalization and transmission of knowledge; === Data science skills === A data scientist or ML engineer with * Broad knowledge of the libraries used and available on the market * Extensive knowledge of MLOps development platforms * Proven experience of ML over the entire cycle: design, development, training/validation, integration === Cybersecurity skills === * Fundamentals of cybersecurity (threats, risk analysis, needs, mechanisms, architecture, cloudsec) * Ability to organize security intelligence (threats, vulnerabilities, solutions): source, processing... * Knowledge of AI attack types and mitigation principles * Working knowledge of data protection mechanisms, including data pipeline security (transfer, storage, calculation) or dataset extractions. * Practical knowledge of development security, including supply chain risks, opensource, git, etc. * Practical knowledge of code security review
