AI Risk Database Tackles AI Supply Chain Risks
The open source tool — a collaboration between Robust Intelligence, MITRE, and Indiana University — assesses heavily shared, public machine learning models for risk.
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Background for this topic.
Databases store and organize data electronically, enabling efficient retrieval and management. They often hold sensitive information such as user credentials, financial records, or personal details, making them prime targets for attackers. Common database types include relational databases using structured query language (SQL) and NoSQL databases designed for unstructured data.
Security risks include SQL injection attacks that exploit improper input handling to manipulate or extract data, and misconfigured access controls that allow unauthorized users to view or alter information. Protecting databases involves strict authentication, role-based access controls, encryption of data at rest and in transit, and timely application of security patches to database software. Monitoring query logs and access patterns helps detect suspicious activity that could indicate compromise or insider threats.
The open source tool — a collaboration between Robust Intelligence, MITRE, and Indiana University — assesses heavily shared, public machine learning models for risk.
If you can't trust a spyware developer with your info, who can you trust? Stalkerware slinger LetMeSpy will shut down for good this month after a miscreant breached its servers and stole a heap of data in June.…