Data-Driven Risk Management
How Peakhour's contextual security aligns with Visa's data-driven risk management approach in the 2025-2028 Security Roadmap.
Visa's Security Roadmap 2025-2028
An analysis of Visa's Security Roadmap 2025-2028 and how Peakhour's solutions help Australian businesses meet these security objectives.
Account Protection and User Experience in Web Applications
Explore strategies to enhance web application security without compromising user experience, focusing on contextual security and adaptive authentication measures.
A Risk Based Approach To Vulnerability Scoring
An in-depth exploration of EPSS, its data-driven approach to assessing cybersecurity threats, and how it complements CVSS.
ModSecurity’s End-of-Life
ModSecurity's end-of-life marks a pivotal moment in application security evolution. Discover how modern Application Security Platforms are advancing beyond traditional WAF approaches to provide comprehensive protection for web applications and APIs at the edge.
APRA Cybersecurity Guidelines
Comprehensive guide to APRA cybersecurity requirements for Australian financial institutions. Learn how application security platforms help meet CPS 234 compliance and Information Security Manual guidelines for protecting financial services infrastructure.
Web scraping another Business' website
Scraping competitor websites is a common practice, but is it legal? Read on to find out.
Headless Commerce Security
Comprehensive analysis of security challenges in headless commerce and Single Page Applications. Learn how to protect modern e-commerce APIs and microservices architectures from scraping, fraud, and automated attacks.
Double MAD?
This article explores the use of Double Median Absolute Deviation (Double MAD) for anomaly detection in time series data, particularly in skewed or non-symmetric distributions.
Double MAD vs the Rest
A look at the limitations of Double MAD for anomaly detection, and a comparison with the Z-score method, to help you choose the right approach for your data.
Scaling anomaly detection with RRCF
Discusses strategies for scaling the Robust Random Cut Forest (RRCF) algorithm for large-scale anomaly detection, including using summary statistics, buffering input, and parallelisation.
Applied RRCF - thresholding techniques.
Explores various thresholding techniques like Median Absolute Deviation (MAD), Min/Max, and Z-Score for interpreting Robust Random Cut Forest (RRCF) anomaly scores, crucial for classifying data points as normal or anomalous.
Application Performance Optimisation
Comprehensive guide to application performance optimisation with integrated security testing. Learn how modern Application Security Platforms deliver superior performance whilst maintaining robust threat protection for contemporary applications.