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Machine Learning

Ways to Build Cybersecurity Resilience: Defending Against New Threats

In today's digital age, where cyber threats loom larger and more complex than ever, building cybersecurity resilience isn't just advisable-it's imperative. Each day, new vulnerabilities are discovered and exploited by cybercriminals who are becoming increasingly sophisticated in their methods. This reality makes it crucial for both individuals and organizations to fortify their cyber defenses to protect sensitive data and maintain business continuity.

How, and why, we applied machine learning to Cove Continuity, part 1

Over the next three blogs, I want to explain how we used machine learning to increase Cove Continuity boot-check accuracy to 99%. Cove Continuity offers the ability to restore source (protected) servers/workstations to virtual machines (VMs) in Hyper-V, ESXi, or Azure. After a VM is restored, Cove performs a boot-check test to prove that the system was properly restored.

Harnessing Technology for Seamless Mortgage Lender Discovery

In an era where digital solutions are at the forefront of transforming industries, the mortgage sector stands as a prime example of this revolutionary change. The complexity of selecting the perfect mortgage lender can overwhelm potential homebuyers, yet, technology simplifies this process, offering streamlined, efficient methods for comparison and selection.

An overview of machine learning security risks

Data is at the heart of all machine learning (ML) initiatives – and bad actors know it. As AI continues to occupy the limelight of modern tech discourse, ML systems are becoming increasingly attractive targets for attack. With the Identity Theft Resource Center reporting a 72% spike in data breaches in 2023, it’s critical to take the proper precautions to ensure your ML projects don’t provide a back door to your data.

Best practices for monitoring managed ML platforms

Machine learning (ML) platforms such as Amazon Sagemaker, Azure Machine Learning, and Google Vertex AI are fully managed services that enable data scientists and engineers to easily build, train, and deploy ML models. Common use cases for ML platforms include natural language processing (NLP) models for text analysis and chatbots, personalized recommendation systems for e-commerce web applications and streaming services, and predictive business analytics.

Best practices for monitoring ML models in production

Regardless of how much effort teams put into developing, training, and evaluating ML models before they deploy, their functionality inevitably degrades over time due to several factors. Unlike with conventional applications, even subtle trends in the production environment a model operates in can radically alter its behavior. This is especially true of more advanced models that use deep learning and other non-deterministic techniques.

Role of AI in Predictive Security Camera Monitoring

As the modern secure world faces several advanced technologies and ever-evolving security threats, the roles of artificial intelligence in predictive security camera monitoring systems have all the opportunities to be a stimulating force to protect people, properties, and public places. Thanks to the ability of AI to quickly predict threats and rapidly respond with much more accuracy than ever before, security systems can now be more tailored to surveillance and risk management needs, turning the traditional notion of this vertical upside down.