Cathy Gaphty – Page 4 – Léargas Security

Léargas Security with AI – The Dawn Of A New Age In Cybersecurity

For a long time, “artificial intelligence” has been a popular buzzword in the cybersecurity sector, boasting solutions capable of detecting suspicious network activities, rapidly understanding the situation, and assisting in incident response upon an intrusion. However, the most effective and reliable services so far have been machine learning algorithms designed to identify malware traits and other questionable network behaviors. Now, with the increasing availability of generative AI tools, Léargas Security has finally developed a service for security professionals that lives up to the hype.

Léargas Security Sponsors NRECA 2023 Co-op Cyber Tech

Léargas Security, a leading cybersecurity firm, has made a name for itself by providing top-notch security solutions and services to clients worldwide. With a focus on innovation, Léargas Security stays ahead of emerging threats by continuously updating its strategies and techniques. The company’s dedication to excellence has made it the perfect partner for NRECA in the 2023 Co-Op Cyber Tech Conference.

Léargas Security with AI: The Cost Factor

With the increasing number of cyber-attacks and the ever-changing threat landscape, there is a growing demand for cybersecurity analysts who can effectively protect computer systems and networks. However, the shortage of skilled cybersecurity professionals is a major challenge that many organizations face. Artificial Intelligence (AI) has emerged as a potential solution to this problem, and its importance in the strategic shortening of skills gaps in cybersecurity analysts cannot be overstated. That is why Léargas Security has leveraged ChatGPT for strategically shortening the knowledge gap.

Leveraging ChatGPT To Close The Knowledge Gaps

Cybersecurity threats are increasingly becoming more frequent, sophisticated, and complex, and companies are struggling to keep up with the pace. With the rise of the internet, the number of attacks has grown exponentially, and attackers are continuously finding new ways to bypass traditional security measures. As a result, the demand for security analysts has increased drastically, however, there is a significant shortage of qualified professionals to fill these positions.

What Is SecOps and What Is The Value Of SecOps To Organizations?

As organizations continue to embrace digital transformation, software development has become a critical part of their operations. However, with the rise of cyber threats and data breaches, security has become a top concern for many organizations.

This is where SecOps comes in…

A methodology that integrates security practices into the DevOps process.

SecOps is a collaborative approach that brings together security professionals, developers, and operations teams to ensure that security is incorporated throughout the entire software development lifecycle. By integrating security into the development process, SecOps helps to identify and address security vulnerabilities earlier, reducing the likelihood of security breaches and data leaks.

So, what is the value of SecOps to organizations? Let’s take a look:

Enhanced Security

One of the primary benefits of SecOps is enhanced security. By incorporating security into the development process, organizations can identify and address security vulnerabilities earlier in the development lifecycle. This means that security issues can be remediated before they become major problems, reducing the likelihood of security breaches and data leaks.

Faster Time to Market

In today’s fast-paced business environment, time to market is critical. SecOps helps teams to identify and resolve security issues earlier in the development cycle, reducing delays and accelerating time to market. By reducing the time it takes to get products and services to market, organizations can stay ahead of the competition and meet the needs of their customers more effectively.

Improved Collaboration

SecOps fosters collaboration between security, development, and operations teams, breaking down silos and enabling teams to work together more effectively. This collaboration helps to ensure that security is integrated into the development process from the outset, rather than being an afterthought. By working together, teams can identify and address security issues more effectively, reducing the likelihood of security incidents and data breaches.

Cost Reduction

Early identification and remediation of security vulnerabilities can save organizations money in the long run. By identifying and addressing security issues earlier in the development process, organizations can avoid costly security incidents and data breaches. This can result in significant cost savings, as well as reducing the risk of reputational damage.

Regulatory Compliance

Finally, SecOps helps organizations to meet regulatory requirements and standards, such as GDPR, HIPAA, and PCI DSS, by incorporating security into the software development process from the outset. By ensuring that security is integrated into the development process, organizations can avoid costly fines and penalties for non-compliance.

SecOps is a powerful approach to security that can help organizations to build more secure software, reduce costs, and accelerate time to market. By integrating security into the development process, organizations can identify and address security vulnerabilities earlier, reduce the risk of security incidents and data breaches, and meet regulatory requirements and standards. As organizations continue to embrace digital transformation, SecOps will become an increasingly critical part of their operations.

At Léargas Security, we provide several integration points for SecOps and SDLC.

Reach out and ask how we can help you, today!

The Importance of Normalization and Scoring of Threat Intelligence Artifacts

In the present-day, interconnected world, businesses confront an expanding threat landscape. To safeguard themselves from cyber threats, organizations rely on threat intelligence, which is one of the most valuable tools available. However, the effectiveness of threat intelligence hinges on the quality of its data. That’s why normalization and scoring of threat intelligence artifacts are two indispensable procedures that guarantee high-quality data.

Visibility and Log Fidelity – Recommendations

In the world of cybersecurity, adequate visibility and log fidelity are critical components in ensuring the necessary security of your organization’s assets. As cyber threats continue to evolve and become more sophisticated, it’s essential to have a comprehensive view of your many networks, cloud assets, and endpoints, provides and the ability to identify potential security incidents quickly.

Breaking these components down, “Visibility” refers to the level of insight you have into your organization’s activity. This insight includes understanding how your network operates, what devices are connected to it, and the types of traffic flowing through it. In essence, visibility provides a complete picture of your organization’s landscape, allowing you to identify and address potential security issues proactively. Adequate amounts of log fidelity will be required to raise the confidence in the assertions made by the analyst.

“Log fidelity”, on the other hand, refers to the accuracy and completeness of the data collected. It’s essential to collect logs from various devices in your organization to ensure that you have a complete picture of the activity. Log fidelity allows you to trace activity and identify potential security incidents with precision and speed.

Adequate amounts of log fidelity will be required to raise the confidence in the assertions made by the analyst, and more will always be better.

One might desire to collect the highest-fidelity of logs, but there are significant pros and cons to be considered. Some of the most important ones are outlined below.

Pros

Improved troubleshooting
Increasing log verbosity can provide more detailed information about system operations, making it easier to identify and diagnose issues.
Better understanding of system behavior
With more detailed logs, it’s easier to understand how a system is behaving, providing valuable insights into its operation.
Improved security
Detailed logs can provide security teams with more information about potential security incidents, making it easier to identify and respond to them.
Improved performance
In some cases, increasing log verbosity can help identify performance issues that might have gone unnoticed with less detailed logs. This is effectively implementing a SNR (Signal-To-Noise Ratio).

Cons

Increased storage requirements
More detailed logs require more storage space, which can be a concern for systems with limited disk space.
Licensing costs
Many SIEMs are built on a pricing model that could significantly increase the cost of platform, as the total volume of logs will increase.
Increased processing overhead
Generating more detailed logs can require additional processing overhead, which can impact system performance.
Reduced performance
In some cases, increasing log verbosity can cause a system to slow down, especially if there is a high volume of log data.
Privacy concerns
Detailed logs can contain sensitive information, which can pose privacy concerns if not handled properly.

Together, visibility and log fidelity provide a powerful tool for cybersecurity professionals to protect their organization from potential threats, but they must be properly tuned. Without adequate visibility, it’s challenging to know what’s happening within your organization, making it difficult to identify potential security incidents and manage the security posture. Similarly, without log fidelity, it’s challenging to trace activity and identify the root cause of a security incident.

Here are some recommendations for log levels in cybersecurity:

Use a consistent log level system
It’s essential to use a consistent log level system across all devices and applications in your network. This ensures that all logs are categorized and prioritized in a consistent manner, making it easier to identify potential security incidents. Normalizing the log data in the earlier stages of collection will likely reduce the TCO (Total Cost of Ownership) of the platform.
Use a minimum of three log levels
It’s recommended to use a minimum of three log levels: information, warning, and error. This provides a basic framework for identifying potential issues while keeping log files manageable. Where possible, consider formatting the logs in JSON (JavaScript Object Notation) as it can lower the cost of normalization between other logs.
Define log levels based on severity
Define log levels based on the severity of an event or activity being logged. This ensures that the most critical events are identified and addressed promptly.
Define thresholds for log levels
Define thresholds for each log level based on the severity of the event or activity being logged. For example, a warning log may be generated when a device is running low on storage space, and an error log may be generated when a device has encountered an error.
Define retention
Define the period of time that log data is kept and available for analysis. Retention policies define the length of time that log data is stored and are typically based on compliance requirements or organizational needs.
Monitor logs in real-time
It’s recommended to monitor logs in real-time to detect potential security incidents promptly. This can be done using Léargas Security, which can alert security teams when critical events occur.
Regularly review and analyze logs
Regularly reviewing and analyzing logs can help identify potential security incidents that may have gone unnoticed. This can help security teams identify and address vulnerabilities and threats before they cause significant damage.

Log levels play a critical role in cybersecurity by providing information on the severity of an event or activity being logged. By using a consistent log level system, defining log levels based on severity, and regularly reviewing and analyzing logs, security teams can identify and address potential security incidents proactively. By monitoring logs in real-time and using automated tools, security teams can detect and respond to potential security incidents promptly, minimizing the impact of a security breach or data loss incident.

At Léargas Security, our goal is to work with our customers to determine their operational and regulatory needs, because it helps the organizations identify and manage security risks, comply with legal and regulatory requirements, establish effective security practices, and allocate resources effectively. By understanding their operational and regulatory needs, organizations can establish appropriate policies, procedures, and technical controls that mitigate risks and protect critical assets.

Need help? Contact us today at, sales@leargassecurity.com!

Inline Detections and Hunting: The Differences and Value Gained

The threat landscape is continually evolving and growing increasingly complex, therefore organizations must take a proactive approach to cybersecurity. Traditional security tools such as firewalls, intrusion detection systems (IDS), and antivirus software are no longer sufficient to protect against advanced threats. Inline security detections and threat hunting are two approaches that can help organizations to better detect and respond to security threats.

Attack Surface Reduction: Why It Matters.

In today’s digital age, cybersecurity is more critical than ever before. With the growing number of cyber threats, it is essential to reduce the attack surface to protect your organization’s assets. What is the “attack surface”? The attack surface refers to the total number of vulnerabilities, entry points, and possible attack vectors that hackers can exploit to gain unauthorized access to an organization’s systems and data. In this blog post, we will discuss the importance of reducing the cybersecurity attack surface and some effective ways to do it.

The Importance of Artificial Intelligence and Machine Learning in Cybersecurity

Léargas Security, like many other Extended Detection and Response (XDR) platforms, has become an essential part of modern cybersecurity. As the number and complexity of cyber threats continue to increase, more organizations are turning to Léargas to provide comprehensive and proactive threat detection and response capabilities. And one of the most significant advancements in the Léargas platform in recent years has been the integration of artificial intelligence (AI) and machine learning (ML) algorithms.

Let’s understand what Léargas is. Léargas is an advanced security platform that provides organizations with a comprehensive approach to threat detection and response. Unlike traditional security solutions that only focus on specific parts of an organization’s infrastructure, the Léargas platform leverages data from multiple security tools and data sources, both on-premises and in the cloud, to provide a more holistic and comprehensive view of the network, endpoints, and cloud environments. The Léargas platform combines security analytics, threat intelligence, and automated response capabilities to detect and respond to threats across the entire infrastructure.

With the integration of artificial intelligence and machine learning, Léargas can improve the detection capabilities and speed up response times for its subscribers. Artificial intelligence and machine learning algorithms can process copious amounts of data from a growing number of sources in real-time, identifying patterns and anomalies that may indicate an attack. This allows the Léargas platform to detect and respond to threats more quickly, reducing the risk of damage and data loss.

So, here are some specific ways that the Léargas platform utilizes AI and ML:

Enhanced detection capabilities: AI and ML algorithms can analyze large volumes of data from various sources, such as network traffic, logs, and endpoints. This enables Léargas to detect advanced and emerging threats that traditional security solutions may miss.
Faster response times: AI and ML algorithms can automate response actions, such as isolating infected endpoints, blocking malicious traffic, and containing the attack. This permits Léargas to respond quickly to threats, reducing the time-to-detection and time-to-response.
Reduced false positives: AI and ML algorithms can filter out false positives, reducing the number of alerts that security teams need to investigate. This saves time and resources, allowing security teams, both MSP (Managed Service Providers), MSSP, and independent organizations to focus on more critical threats.
Improved threat intelligence: AI and ML algorithms can analyze threat intelligence data, identifying new patterns and trends that may indicate emerging threats. This enables Léargas to stay ahead of the threat landscape, providing proactive threat detection and response capabilities. Additionally, Léargas partners with companies like Critical Path Security to gain more valuable intelligence each day.
Better risk management: AI and ML algorithms can provide risk scoring and prioritization, allowing security teams to focus on the most critical threats.
Malware analysis: Machine learning algorithms are used by the Léargas platform and the supported EDR (Endpoint Detection and Response) solutions to analyze malware behavior, identifying patterns that may indicate the presence of malware, ransomware, or an internal threat actor on a network.

Léargas strives to secure organizations by enhancing threat detection capabilities, speeding up response times, reducing false positives, improving threat intelligence, and providing better risk management.

As the threat landscape continues to evolve, organizations need advanced security solutions that can keep pace with the changing threat landscape. The AI-powered Léargas security platform provides a proactive and comprehensive approach to cybersecurity, helping organizations to stay one step ahead of cyber threats.