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Total Size:
10.5 MB
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11607339DE5C8DAF76FFF3A4CEDAA42D902029C3
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Added:
July 14, 2025, 12:31 p.m.
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(Last updated: July 14, 2025, 12:31 p.m.)
| File | Size |
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| ['Mattei M. Data-Driven Cybersecurity. Reducing risk...(MEAP v6) 2025.pdf'] | 0 bytes |
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10.5 MB
[13
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18]
2025-07-14
| Uploaded by andryold1 | Size 10.5 MB | Health [ 13 /18 ] | Added 2025-07-14 |
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47.7 MB
[44
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15]
2025-07-25
| Uploaded by andryold1 | Size 47.7 MB | Health [ 44 /15 ] | Added 2025-07-25 |
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SOURCE: Mattei M. Data-Driven Cybersecurity. Reducing risk...(MEAP v6) 2025
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COVER
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MEDIAINFO
Textbook in PDF format Measure, improve, and communicate the value of your security program. Every business decision should be driven by data—and cyber security is no exception. In Data-Driven Cybersecurity, you'll master the art and science of quantifiable cybersecurity, learning to harness data for enhanced threat detection, response, and mitigation. You’ll turn raw data into meaningful intelligence, better evaluate the performance of your security teams, and proactively address the vulnerabilities revealed by the numbers. Data-Driven Cybersecurity will teach you how to Align a metrics program with organizational goals Design real-time threat detection dashboards Predictive cybersecurity using AI and machine learning Data-driven incident response Apply the ATLAS methodology to reduce alert fatigue Create compelling metric visualizations Data-Driven Cybersecurity teaches you to implement effective, data-driven cybersecurity practices—including utilizing AI and machine learning for detection and prediction. Throughout, the book presents security as a core part of organizational strategy, helping you align cyber security with broader business objectives. If you’re a CISO or security manager, you’ll find the methods for communicating metrics to non-technical stakeholders invaluable. AI is a broad term that encompasses various technologies, including Machine Learning (ML), natural language processing (NLP), and Generative AI with Large Language Models (LLMs). In cybersecurity, AI is leveraged to identify complex patterns and correlations that human analysts might overlook. When trained on high-quality historical and real-time data, Machine Learning models can recognize subtle indicators of compromise, enabling predictive security measures. However, AI models are only as effective as the data they are trained on and the algorithms that power them. Poor quality, biased, or incomplete data can lead to false positives, missed threats, or misleading insights. Likewise, AI decision-making algorithms must be carefully designed, tested, and validated to avoid errors that could undermine security efforts. Preface Introduction_to_cybersecurity_metrics Cybersecurity_analytics_toolkit Implementing_a_security_metrics_program Integrating_metrics_into_business_strategy Establishing_the_foundation Foundations_of_cyber_risk Protecting_your_assets Continuous_threat_detection Incident_management_and_recovery Advanced_cybersecurity_metrics Advanced_statistical_analysis Advanced_machine_learning_analysis Generative_AI_in_cybersecurity_metrics
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