Understanding the key differences between approaches in the EU and the US can help unlock maximum value with the right security strategies. Traditional methods often fall short, but integrating Machine Learning (ML) into your security framework can transform your defence against modern threats.
Embrace a dynamic approach to security that adapts to evolving risk profiles. ML optimises your security investments and ensures that measures are tailored to specific threats, enhancing protection and efficiency.
In this episode of the Security Strategist, Chris Steffen, EMA's VP of research, speaks to Brady Harrison, Kount's Director of Customer Analytics Solution Delivery, to discuss maximising value through optimal security strategies.
Key Takeaways:
- Finding a balance between fraud prevention and sales generation is crucial for optimising security strategies.
- Machine learning can help businesses make informed, risk-based decisions by analysing large volumes of data in real-time.
- Optimising security investments involves evaluating the cost-benefit trade-offs and setting appropriate risk thresholds.
Chapters:
00:00 - Introduction to the Security Strategist podcast
00:25 - Introduction to Kount and its focus on customer analytics and fraud prevention
01:49 - Differences between EU and US security strategies
05:12 - Balancing fraud prevention and sales conversion
08:59 - Optimizing security investments with machine learning
14:43 - Advantages of machine learning in security
18:31 - Setting security strategy based on machine learning
23:47 - Treating customers as good until proven otherwise
25:11 - Conclusion and call to action
