The data being used to drive effective anti-fraud efforts can be rich in context and useful for other activities. Jim Apger of Splunk describes emerging fraud schemes and solutions, highlighting the role of machine learning.
The easy availability of tools for designing face-swapping deep-fake videos drove Symantec security researchers Vijay Thaware and Niranjan Agnihotri to design a tool for spotting deep fakes, which they described in a briefing at the Black Hat Europe 2018 conference in London.
Using machine learning to classify known malicious and benign activity, while humans focus on the grey areas, is the most effective combination for tackling today's threat landscape of constantly evolving cyberattacks.
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Limitations of AI and machine-learning solutions as...
Security hygiene can be poor, and criminals know it. Fraudulent activity costs are in the billions worldwide across industries, and over 16 million consumers in the US were victims of identity theft or fraud in the past year. Learning to onboard new data at the speed of the business will ensure your fraud team can...
Artificial intelligence and machine learning get all the marketing buzz, but First Data's Jaclyn Blumenfeld cites examples of use cases where these emerging technologies are actually transforming security and fraud management.
Interest in artificial intelligence (AI) and machine learning (ML) is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive...
From protecting customer experience to preserving lines of revenue, IT operations teams are faced with increasingly complex responsibilities while being required to prevent outages that could harm the organization. Splunk IT Service Intelligence (ITSI) utilizes AI powered by machine learning to deliver a predictive IT...
AI and machine learning have made their way into the business and it's here to stay. Organizations that have already invested in the burgeoning technology are seeing benefits to both their top and bottom lines. In fact, enterprise use of AI and one of its offshoots - machine learning - is about to leap into the...
While unsupervised machine learning techniques get away from the data labeling and classification that most supervised systems require, they are dependent on the quality and variety of the data provided, says Gartner's Jonathan Care.
Identity and access management is not about compliance anymore - It's really about security, says Gartner's Felix Gaehtgens. With cloud, virtualization, DevOps and other IT trends, IAM has evolved from being a one-off project to an ongoing initiative.
Protecting "East-West" cloud traffic - the traffic between apps and virtual machines - is a significant challenge, but microsegmentation can help address it, says Raghu Raghuram of VMware.
As companies go through a digital transformation, they need to move toward real-time risk management - and artificial intelligence can play a critical role, says David Walter, vice president of RSA Archer.
Organizations can effectively rely on managed security services providers to take care of many tasks, but certain strategic security functions must be handled in-house, says Sid Deshpande, research director at Gartner.
With so much focus on endpoint security, it's important not to overlook the importance of network-level security controls, says Lawrence Orans, research vice president at Gartner.
Machine learning could be a breakthrough for data classification, addressing fundamental challenges and paving the way to create and enforce automated policies that can be scaled across the enterprise, says Titus CEO Jim Barkdoll.
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