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A good example of this threat landscape is the Sleepy Pickle technique.
Senior Solutions Architect at HackerOne.
At its core, MLSecOps integrates security practices and considerations into the ML development and deployment process.
This can include issues with things like software/hardware components, communications networks, data storage and management.
This helps organizations ensure compliance requirements are met, and the integrity of sensitive data is maintained.
Model provenance
Model provenance means tracking the handling of data and ML models in the pipeline.
Record keeping should be secure, integrity-protected, and traceable.
Adversarial ML
Defending against malicious attacks on ML models is crucial.
To achieve this, researchers have developed techniques that can detect and mitigate attacks in real time.
We’ve featured the best online cybersecurity course.
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