ID Dataweb™, a recognized leader in identity threat detection and risk mitigation, today announced the availability of a new industry white paper from technology research and advisory group Omdia titled "Controlling Identity Risk: Detecting and Mitigating Identity Threats." The paper examines how attackers exploit weak, stolen, or otherwise compromised credentials across customer, third-party and workforce workflows, and why enterprises need a holistic approach to managing identity risk.
Quick Intel
The report highlights that traditional identity and access management (IAM) approaches are no longer sufficient against threat actors armed with stolen data and increasingly sophisticated social engineering tactics. It presents an identity verification maturity model that progresses from credential-only authentication to step-up authentication, point-in-time identity verification (IDV), and ultimately continuous identity threat detection and risk mitigation.
The advent of identity threat detection and risk mitigation goes beyond IDV by analyzing the broader pattern of transactions associated with a credential, as well as activity across other credentials in the environment. It incorporates a feedback loop that enables continuous learning and refinement of the decision engine. This approach integrates adaptive identity verification, behavioral analytics, device and credential intelligence, and risk scoring to identify and mitigate threats in real time.
Identity use cases are diverse and often span multiple teams within an enterprise, each with distinct objectives and risk tolerances. To be effective, organizations must avoid siloed identity security strategies that optimize for individual teams but leave the enterprise exposed overall.
The report emphasizes the importance of solutions that provide, at minimum, the following capabilities: flexible risk detection and orchestration, broad access to authoritative identity data sources and risk signals, resilience across infrastructure, data, and identity verification services, and privacy preservation.
This holistic approach enables organizations to deploy a single solution that integrates across multiple enterprise use cases—including customers, third parties (such as contractors and suppliers), and workforce—while reducing complexity, cost, and risk.
"Identity risk is no longer confined to a single login, transaction, or device," said Dave Coxe, Co-founder and CEO at ID Dataweb. "Organizations need a unified approach that can continuously verify the person behind the credential across the full identity lifecycle without adding friction for legitimate users."
To learn more about controlling identity risk, watch the fireside chat featuring Todd Thiemann, Principal Analyst at Omdia, and Dr. Torsten George, Cybersecurity Evangelist at ID Dataweb.
About ID Dataweb
ID Dataweb™ helps enterprises stay ahead of identity fraud and account-related threats with real-time detection and mitigation while maintaining a seamless experience for their workforce, third parties, and customers. The ID Dataweb SaaS platform combines adaptive identity verification methods, behavioral analytics, device and credential intelligence, and risk scoring. Backed by AI and expert insights, these capabilities proactively stop identity-based attacks, protect revenue, and strengthen compliance. Unlike static legacy identity tools, ID Dataweb delivers dynamic, multi-layered risk orchestration that adapts to evolving threats. Its low-code, cloud-native services deploy quickly, integrate seamlessly with existing IAM systems, and align with each customer's policies. For more information, please visit www.iddataweb.com.
About Omdia
Omdia, part of TechTarget, Inc. d/b/a Informa TechTarget (Nasdaq: TTGT), is a technology research and advisory group. Our deep knowledge of tech markets grounded in real conversations with industry leaders and hundreds of thousands of data points, makes our market intelligence our clients' strategic advantage. From R&D to ROI, we identify the greatest opportunities and move the industry forward.
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