AI cybersecurity: mastering the risks of artificial intelligence models

Ensuring the cybersecurity of your AI models is essential to guarantee the availability, integrity and confidentiality of your services. (CIA: confidentiality, integrity & availability)

Artificial intelligence opens up new opportunities, but also new attack surfaces. Protecting models, data and users is not an option: it's an essential prerequisite for guaranteeing a successful Reliable, responsible and compliant AI regulations (RGPD, GDPR). Here's what you can do to ensure the cybersecurity of your AI models.

Guardrails : to strictly control model input and output, reduce the risk of hallucinations and align behavior with business, ethical and regulatory rules.
Data sanitization and prompts to prevent malicious injections, clean inputs and prevent leakage of sensitive information.
Proxy and model isolation : to filter, log and control flows, while partitioning environments to limit the impact of potential attacks.
IA endpoint security : to reinforce authentication, control access and audit API calls, guaranteeing system resilience.
Proactive prevention : encryption, key rotation, penetration testing and regular audits to identify vulnerabilities.
Alignment with standards : integration of OWASP best practices (application security, API, LLM Top 10) and threat mapping according to MITRE ATT&CK, to cover known and emerging attack vectors.

Objective: ensure that AI remains a strategic asset and not a vulnerability, by building robust, explainable systems capable of withstanding current and future threats.

Think of your AI's cybersecurity as an investment, not an expense.