@Caute_cautim wrote: DogLikesBest
Hi All
Details of eight vulnerabilities found in the open source supply chain used to develop in-house AI and ML models have been disclosed. All have CVE numbers, one has critical severity, and seven have high severity.
We are now seeing evermore evidence that platforms enabling MLOps are themselves vulnerable (the “Sec” part is lacking).
The risks are real! You’ve got to secure the data, the models, and keep your tooling patched!
https://www.securityweek.com/eight-vulnerabilities-disclosed-in-the-ai-development-supply-chain/
Regards
Caute_Cautim
Hello,
In the article you shared, the eight vulnerabilities affect the AI development supply chain, which includes tools and frameworks used to create, train, and deploy AI and ML models. These vulnerabilities could allow attackers to compromise the integrity, availability, and confidentiality of the AI and ML models and data.
To help secure your MLOps solutions, you may want to consider the following best practices:
- Secure your data: Data is the foundation of any AI and ML project, and it should be protected from unauthorized access, modification, or leakage. You can use encryption, access control, data masking, and data quality checks to ensure the security and integrity of your data.
- Secure your models: Models are the core of any AI and ML project, and they should be protected from tampering, theft, or misuse. You can use digital signatures, model encryption, model watermarking, and model testing to ensure the security and authenticity of your models.
- Secure your tooling: Tooling is the infrastructure and software that supports your AI and ML project, and it should be protected from exploitation, disruption, or compromise. You can use network security, patch management, vulnerability scanning, and configuration management to ensure the security and reliability of your tooling.
I hope this solution is helpful for you. If you need more assistance, please let me know.
