Extend your Attack Surface Coverage with No SIEM Required

If 2020 seemed like an anomaly, 2021 proved to us that it’s time to get comfortable with the transformed reality. Remote work, intended as a temporary response to COVID-19, is now an increasingly standardized way to operate. With face-to-face operations no longer the norm, numerous organizations have shifted to a technology-driven strategy. We’ve seen a rise in initiatives like self-service or online purchases, as well as an upturn in remote events, with many new or established conferences choosing to go virtual. Businesses are also investing in technology more than ever, growing their IT infrastructure with new software and IoT devices. However, as can be expected, new or enhanced cybersecurity threats went hand and hand with this unexpected digital renaissance.

Every company is concerned about data breaches, and for good reason—according to industry estimates, approximately half of the companies in the U.S. have suffered a data breach. As organizations seek to better secure their environments, they are facing a number of challenges including growing data volumes and expanded attack surfaces, as well as data silos and the inability to enable data-driven decisions about security issues in real time. It is not surprising that security and privacy rank in the top three data-related priorities in TDWI surveys.

Protect data confidentiality, personally identifiable information (PII), intellectual property, and industry information systems from the risk of data breach, damage and theft.

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 business. Register for this webinar and join Marc Trepanier, Principal Fraud Consultant at ACI Worldwide, and leading industry analyst Julie Conroy of Aite Group as they discuss real-life use cases that demonstrate how banks can: Improve data management control costs while reducing the compliance and regulatory risks.