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Cybersecurity Training Classes
81% of data breaches are the result of human-related security failures, we address this by providing training on the latest threats and how best to mitigate them
Cybersecurity Assessments
Envescent specializes in cybersecurity assessments, providing comprehensive audits that focus on comprehensively identifying security vulnerabilities
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Fast, friendly and professional information technology consulting services from our team of seasoned experts

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Penetration Testing

Simulate how a malicious hacker may compromise your critical business networks and systems with our cutting edge penetration testing services


network segmentation

The Importance of Network Segmentation

Network security has become a top priority for businesses and organizations of all sizes, and rightfully so. Improperly configured or secured networks can create a playground for malicious hackers and automated attacks.   One critical aspect of maintaining robust cybersecurity is segmenting networks to prevent unauthorized access and data breaches. Network segmentation involves dividing a single network into multiple smaller sub-networks or segments, each with its own set of rules and restrictions. This approach not only improves overall security but also enhances productivity, reduces downtime, and minimizes the attack surface. Why Segment Networks? Segmenting networks is essential for several reasons: Prevents Lateral Movement : By isolating sensitive devices on separate networks, an attacker who gains access to one segment cannot easily move laterally to other areas of the network. Limits Damage in Case of a Breach : If a device on a segmented network is compromised, the damage is contained within that specific segment, preventing it from spreading to other parts of the network. Reduces Attack Surface : Segmenting networks reduces the number of potential entry points for attackers, making it more difficult for them to gain access. Segmenting Untrusted Devices Untrusted devices, such as IoT (Internet of Things) devices, phones, cameras, and guests’ computers, should be kept on separate networks than business computers, servers, and other sensitive devices. This is because these devices are often unpatched, outdated, or vulnerable to exploitation. Larger organizations may also segment networks by groups or teams, such as separating the sales and financial team as they likely access differentiated resources. Segmenting Wireless and Wired Networks Segmenting wireless and wired networks is also crucial for maintaining robust cybersecurity: Wireless Networks : Wireless networks should be segmented from wired networks to prevent unauthorized access via Wi-Fi. Wired Networks : Sensitive devices, such as servers and databases, […]

The Envescent Cybersecurity Workshop for 2024

Learn about the latest threats that businesses face in 2024 through our free Cybersecurity Workshop. This comprehensive webinar covers the changes happening in attacks such as ransomware, phishing, how AI is being weaponized, hacking activities by nation states such as China and much more.

cybersecurity risk assessments

The Importance of Periodic Cybersecurity Assessments

Cybersecurity is a critical aspect of modern business operations, with cyber threats evolving rapidly and becoming more sophisticated. Regular cybersecurity assessments are essential to ensure that systems, networks, cloud presence, personnel, and IT policies remain secure and up-to-date. These assessments go beyond the scope of ongoing IT Managed Services Provider (MSP) support and conventional protection measures such as anti-virus software and spam filtering.

DNS blacklisting

The Value of DNS Blacklisting

In today’s increasingly interconnected world, cyber threats are becoming more sophisticated and widespread. As a result, it is more important than ever for individuals and organizations to take proactive measures to protect themselves from potential harm.

Quantization

What Does Quantization Mean and How Does It Shrink AI Models?

Quantization is a technique used to reduce the size of machine learning models while maintaining accuracy. This can lead to faster deployment times, lower memory usage, and reduced bandwidth requirements when deploying these models on edge devices or in other resource-constrained environments.