Özge Aykaç
Özge is an industry analyst at AIMultiple focused on data loss prevention, device control and data classification.
She is a member of the AIMultiple DLP benchmark team and evaluates the effectiveness of the top DLP providers.
Latest Articles from Özge
Top 15 AI DLP Best Practices with Case Studies
We analyzed the leading DLP software and, based on our experience with DLP solutions, we identified best practices supported by case studies.
Top 7 Red Teaming Tools
Red teaming is a critical component of modern cybersecurity strategies, simulating real-world attacks to expose vulnerabilities in systems, networks, and human processes. Choosing the right tool can make the difference between a surface-level test and a deep, realistic security assessment.
Top 5 Endpoint Management Software with Pricing
According to Microsoft’s Digital Defense Report, the most common case of cybercrime is unauthorized access to unmanaged devices. With the rise of bring-your-own-device (BYOD) implementations and the trend toward remote/hybrid work, businesses are strengthening their network security measures to prevent security compromises.
Top 9 IT Documentation Software to Streamline Your Workflow
Effective IT documentation is crucial for organizations to maintain operational efficiency, ensure compliance, and facilitate knowledge transfer. We analyzed over 18,000 recent user reviews and tested the key features of the nine leading platforms, from dedicated MSP vaults to integrated RMM solutions.
Top 12 LLM DLP Best Practices to Prevent AI Data Leaks
Enterprises are investing in large language models (LLMs) and generative AI, making the protection of sensitive data essential. As GenAI adoption grows, the risk of sensitive data exposure or GenAI data risk becomes a critical AI compliance concern for organizations across industries.
Top 10 Data Crowdsourcing Platforms
With the spread of AI tools like generative AI and chatbots, the demand for AI data services has also increased. One such service is data crowdsourcing platforms, which leverage large groups to gather data, enhancing collection efforts with fast, detailed insights.
10+ Image Data Collection Services
As artificial intelligence (AI) and machine learning-powered solutions grow, the demand for comprehensive image datasets has never increased. The foundation of a successful AI model, especially in computer vision (CV) projects, is reliant upon high-quality data. Image data collection services play an instrumental role in gathering this crucial data.
Top 20 Analytics Case Studies
Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making, and enables the launching of more personalized products.
Image Data Collection with Best Practices
Computer vision (CV) is revolutionizing industries, from autonomous vehicles to healthcare, but success depends critically on the collection of high-quality image data. Organizations that implement strategic data collection services can achieve higher accuracy in specialized applications, while poor data strategies lead to biased models and compliance violations.
Ethical & Legal AI Data Collection
Disruptive technologies, such as AI, ML, the Internet of Things (IoT), and computer vision, require various types of data to operate. This data often includes biometric data, such as facial images and voice recordings. Collecting and managing such data requires multiple ethical and legal considerations, which, if disregarded, can lead to expensive lawsuits and significant reputational damage.
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