Machine learning is one of the tools that you should be using as part of a multifaceted fraud strategy at financial institutions. To get value out of ML, you need to have multiple inputs. You have to have an intelligence network and the location to gather all that network intelligence.
Download this eBook on the...
Application fraud losses are projected to top $1.3B in the US by 2020. Understanding the trends behind these staggering numbers is critical if institutions are to prevent crippling financial and reputational losses.
Join Aite's Senior Analyst, Shirley Inscoe, and DataVisor's Director of Solution Engineering, Alex...
Using artificial intelligence and machine learning in cybersecurity has pitfalls, says McAfee's Steve Grobman, who describes appropriate steps to take.
Multi-stage attacks use diverse and distributed methods to circumvent existing defenses and evade detection - spanning endpoints, networks, email and other vectors in an attempt to land and expand. Meanwhile, individual tools including DLP, EDR, CASBs, email security and advanced threat protection are only designed to...
Machine learning models and artificial intelligence are
transforming the way email is secured.
Download this eBook to learn more about:
The state of email security today;
The role AI and ML play in securing email infrastructures;
Why humans need not be a weak link.
Banks can drive real value to their fraud prevention strategies with machine learning and analytics if they cut through the hype. Machine learning can be made intuitive and available directly to fraud experts. A multi-faceted strategy can turn fraud prevention expertise into a revenue generator for the...
Organizations may have great cybersecurity intentions, but translating those desires into a robust security reality is often challenging, says Ratinder Ahuja, CEO of ShieldX Networks. That's why he advocates automation to ensure intention equals reality.
Machine learning systems adapt their behavior on the basis of a feedback loop, so they can overlearn and develop blind spots, which if not understood by practitioners can lead to dangerous situations, says Sam Curry of Cybereason.
"Security by design" is at an inflection point as a result of advances in automation, orchestration, artificial intelligence and machine learning, says Lee Waskevich of ePlus Technology.
Is 'Observability' just a disingenuous rebrand of 'monitoring'? Is it monitoring on steroids? Or, is it the new way to find out "why" (and not just "if") your system, IT or application isn't working as expected?
Today we see that traditional monitoring approaches can fall short if a system or an application doesn't...
Automation is the first step toward full-blown machine learning and artificial intelligence. But unfortunately, automation already is being weaponized for malicious purposes, says Fortinet's Derek Manky.
Machine learning can play an important role in fraud prevention at financial institutions, says Marc Trepanier of ACI Worldwide, who addresses the challenges involved.
When examining a data-driven future, embracing new developments around artificial intelligence (AI) and machine learning (ML), IT operations, security and IoT is key. The goal: to generate positive outcomes from your data.
Pulling together insights from thousands of customer and our dedicated research teams, our...
SIEM (security information and event management) software offers a lot of promise, but legacy SIEMs simply can't keep up with the rate and sophistication of today's cyberattacks. Organizations today require access to analytics-driven SIEMs that combine a big data platform that is optimized for machine data with...
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