Building Secure ML Starts with Understanding the Landscape: Oracle’s Perspective

What’s happening in the global ML space and how regulations are changing

ML is now one of the most popular subsets of artificial intelligence, so organizations can no longer afford to skim over security concerns.Companies must understand the threats they face when undertaking these machine learning initiatives. Where network security was the primary cybersecurity focus in the 90s — when we began living more of our lives with machines online– AI security is the going to be the most significant focus for this coming decade.

  • Resiliency: Safe, secure, private
  • Responsibility: Fair, ethical, sustainable

What trends are shaping the next phase of AI security development?

By far the most common type of AI attack involves some kind of manipulation. Threat actors can change the actions of AI through stealth methods or change the expected behavior of the machine. According to Adversa, this makes up over 80% of all attacks on AI.

Practitioners worry about the safety of AI deployment in real-world applications

The unknown unknowns are a pressing concern for industries involved in deploying AI. This keynote’s speakers note that security is still not a widespread concern in the field because organizations are still working on the actual act of deployment in production.

  • Build protection layers
  • Detect and Defend
  • Respond, analyze, and mitigate

Even in this budding field, organizations cannot afford to take a reactive approach

No matter what stage organizations are in their digital transformation, regulations are coming down the pipeline that could make machine learning safer for the everyday citizen but more difficult to deploy for businesses. Organizations can shift to a proactive approach by acknowledging that the possibility of threat is high.

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