Adversarial Machine Learning is a core cybersecurity threat or attack technique that every cybersecurity and GRC professional should understand.
What Is Adversarial Machine Learning?
Adversarial Machine Learning refers to the practices, processes, or technologies related to adversarial machine learning within the context of information security and risk management. Understanding adversarial machine learning helps organizations protect their systems, data, and operations from relevant threats.
How It Works
Adversarial Machine Learning is applied through a combination of policies, technical controls, and organizational procedures. Security teams implement adversarial machine learning as part of a broader security program aligned to industry frameworks and regulatory requirements.
Why It Matters
Organizations that correctly implement adversarial machine learning reduce their exposure to cyber risk, improve regulatory compliance, and strengthen their overall security posture.
Key Takeaways
- Understand what adversarial machine learning is and how it applies to your organization’s security program.
- Align adversarial machine learning practices to a recognized framework such as NIST CSF, ISO 27001, or CIS Controls.
- Regularly review and test controls related to adversarial machine learning to ensure they remain effective as threats evolve.