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AI and cybersecurity: A double-edged sword

CIO Business Intelligence

Perhaps one of the most anticipated applications of AI in cybersecurity is in the realm of behavioral analytics and predictive analysis. By leveraging machine learning algorithms, AI can analyze user behavior and network traffic patterns, identifying anomalies that might indicate insider threats or other malicious activities.

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Beyond Surveys: Rethinking Customer Engagement Through Behavioral Insight

Teradata

Behavioral analytics offer deeper, more actionable customer insights. 2 Understanding the drivers of satisfaction To truly measure and monitor behavioral loyalty, banks must develop robust predictive models of customer attrition. Tools like ClearScape Analytics ® support this by enabling the use of econometric methods (e.g.,

ROI
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Beyond Surveys: Rethinking Customer Engagement Through Behavioral Insight

Teradata

Behavioral analytics offer deeper, more actionable customer insights. 2 Understanding the drivers of satisfaction To truly measure and monitor behavioral loyalty, banks must develop robust predictive models of customer attrition. Tools like ClearScape Analytics ® support this by enabling the use of econometric methods (e.g.,

ROI
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How to Implement DevSecOps Without Slowing Down Delivery

DataFloq

Behavioral analytics and anomaly detection. Continuous monitoring and threat detection in production are essential to maintain security and avoid delays. You should implement: Runtime Application Self-Protection (RASP) to detect and block real-time attacks. SIEM integrations for centralized alerting and response.

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The hidden alchemy of data: Masking as the catalyst for AI and real-time decision-making

CIO Business Intelligence

Every dataset within an enterprise is a double-edged sword, fueling AI-driven insights and real-time analytics while simultaneously increasing security risks and regulatory exposure. This ensures transaction-heavy applications, AI models and real-time analytics remain performant and compliant.

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AI-driven CDR: The shield against modern cloud threats

CIO Business Intelligence

Real-time threat prevention detection: AI engines analyze vast volumes of telemetry in real time—logs, flow data, behavior analytics. Organizations need more than static posture security. They need real-time prevention. The full context this provides enables the detection and prevention of threats as they unfold.

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Securing data in the AI era

CIO Business Intelligence

End User Behavioral Analytics (EUBA): Using AI-driven behavioral analytics, Zscaler identifies anomalies not only from Copilot users but also from any connected third-party SaaS integrations. Prevent excess permissions and proactively block sensitive files from exposure.