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The Vulnerability of Zero Trust: Lessons from the Storm 0558 Hack
Aug 18, 2023
Network Detection and Response
While IT security managers in companies and public administrations rely on the concept of Zero Trust, APTS (Advanced Persistent Threats) are putting its practical effectiveness to the test. Analysts, on the other hand, understand that Zero Trust can only be achieved with comprehensive insight into one's own network. Just recently, an attack believed to be perpetrated by the Chinese hacker group Storm-0558 targeted several government agencies. They used fake digital authentication tokens to access webmail accounts running on Microsoft's Outlook service. In this incident, the attackers stole a signing key from Microsoft, enabling them to issue functional access tokens for Outlook Web Access (OWA) and Outlook.com and to download emails and attachments. Due to a plausibility check error, the digital signature, which was only intended for private customer accounts (MSA), also worked in the Azure Active Directory for business customers. Embracing the Zero Trust Revolution Acc
Unveiling the Unseen: Identifying Data Exfiltration with Machine Learning
Jun 22, 2023
Network Security / Machine Learning
Why Data Exfiltration Detection is Paramount? The world is witnessing an exponential rise in ransomware and data theft employed to extort companies. At the same time, the industry faces numerous critical vulnerabilities in database software and company websites. This evolution paints a dire picture of data exposure and exfiltration that every security leader and team is grappling with. This article highlights this challenge and expounds on the benefits that Machine Learning algorithms and Network Detection & Response (NDR) approaches bring to the table. Data exfiltration often serves as the final act of a cyberattack, making it the last window of opportunity to detect the breach before the data is made public or is used for other sinister activities, such as espionage. However, data leakage isn't only an aftermath of cyberattacks, it can also be a consequence of human error. While prevention of data exfiltration through security controls is ideal, the escalating complexity a
Guide: How to Minimize Third-Party Risk With Vendor Management
Vendor Risk Management
Manage third-party risk while dealing with challenges like limited resources and repetitive manual processes.
AI Solutions Are the New Shadow IT
Nov 22, 2023
AI Security / SaaS Security
Ambitious Employees Tout New AI Tools, Ignore Serious SaaS Security Risks Like the SaaS shadow IT of the past, AI is placing CISOs and cybersecurity teams in a tough but familiar spot. Employees are covertly using AI with little regard for established IT and cybersecurity review procedures. Considering ChatGPT's meteoric rise to 100 million users within 60 days of launch , especially with little sales and marketing fanfare, employee-driven demand for AI tools will only escalate. As new studies show some workers boost productivity by 40% using generative AI , the pressure for CISOs and their teams to fast-track AI adoption — and turn a blind eye to unsanctioned AI tool usage — is intensifying. But succumbing to these pressures can introduce serious SaaS data leakage and breach risks, particularly as employees flock to AI tools developed by small businesses, solopreneurs, and indie developers. AI Security Guide Download AppOmni's CISO Guide to AI Security - Part 1 AI evoke
The Future of Network Security: Predictive Analytics and ML-Driven Solutions
Feb 21, 2023
Network Security / Machine Learning
As the digital age evolves and continues to shape the business landscape, corporate networks have become increasingly complex and distributed. The amount of data a company collects to detect malicious behaviour constantly increases, making it challenging to detect deceptive and unknown attack patterns and the so-called "needle in the haystack". With a growing number of cybersecurity threats, such as data breaches, ransomware attacks, and malicious insiders, organizations are facing significant challenges in successfully monitoring and securing their networks. Furthermore, the talent shortage in the field of cybersecurity makes manual threat hunting and log correlation a cumbersome and difficult task. To address these challenges, organizations are turning to predictive analytics and Machine Learning (ML) driven network security solutions as essential tools for securing their networks against cyber threats and the unknown bad. The Role of ML-Driven Network Security Solutions
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