artificial intelligence

A Holistic Approach to Network Observability: Beyond the “Five Steps”

In a recent article on BetaNews, Song Pang outlines five steps to achieve network observability: Network Discovery and Data Accuracy, Network Visualizations, Network Design and Assurance, Automation, and Observability. While these steps provide a solid foundation, we believe there are alternative approaches that can be more effective, especially in today’s rapidly evolving network environments. Here, we propose a different set of steps and actions to achieve network observability, explaining why this approach might be superior with clear examples and historical facts.

BetaNews approach focuses on accurate data from logs, traces, traffic paths, and SNMP. We suggest getting a wider system’ view: instead of just focusing on traditional data sources, integrate data from a wider array of sources including cloud services, IoT devices, and user behavior analytics. This holistic view ensures that no part of the network is overlooked.

(C) Image copyright PacketAI & DALL-E
Advanced Automated Network Monitoring Image copyright (C) 2024 PacketAI and DALL-E

For example, back in 2016, a major retail company faced a significant data breach because their network monitoring only covered traditional data sources. By integrating data from IoT devices and user behavior analytics, they could have detected the anomaly earlier.

Real-Time Anomaly Detection with AI

BetaNews approach emphasizes network visualizations and manual baselines. This is great as a start, but you should consider implementing an AI-driven real-time anomaly detection. AI can learn normal network behavior and detect deviations instantly, reducing the time to identify and resolve issues.
In 2020, a financial institution implemented AI-driven anomaly detection, which reduced their mean time to resolution (MTTR) by 40% compared to their previous manual baseline approach.

Proactive Incident Response

BetaNews did not suggest that, but you should be ahead of any network issue. Develop a proactive incident response strategy that includes automated responses to common issues. This reduces downtime and ensures quicker recovery from incidents. A tech company in 2018 implemented automated incident response for their network. This proactive approach reduced their downtime by 30% during network outages.

Continuous Improvement and Feedback Loops

Establish continuous improvement and feedback loops. Regularly review and update network policies and configurations based on the latest data and trends.
In 2019, a healthcare provider adopted continuous improvement practices for their network observability. This led to a 25% improvement in network performance over a year.

User-Centric Observability

While BetaNews approach ends with achieving observability, you can focus on user-centric observability. Ensure that the network observability strategy aligns with user experience and business goals. This ensures that the network not only functions well but also supports the overall objectives of the organization.
A global e-commerce company in 2021 shifted their focus to user-centric observability. This alignment with business goals led to a 20% increase in customer satisfaction and a 15% boost in sales.

Common Mistakes in Network Monitoring

While striving for network observability, it’s crucial to be aware of common mistakes that can undermine your efforts:
Many teams adopt a reactive stance, addressing threats only after they occur. This can leave networks vulnerable to evolving threats. A proactive approach, constantly updating antivirus and cybersecurity practices, is essential.

  • Focusing solely on devices and neglecting applications can lead to incomplete visibility.
  • Monitoring both devices and applications ensures a comprehensive view of network performance and potential vulnerabilities.
  • Failing to monitor network logs can result in missed signs of breaches or performance issues. Regular log analysis is crucial for early detection of anomalies.
  • Not anticipating network expansion can lead to scalability issues. Planning for growth ensures that the network can handle increased traffic and new devices.
  • Using outdated tools can leave networks exposed to new types of threats. Regularly updating and upgrading monitoring tools is vital to maintain robust security.

Conclusion

While the five steps outlined by BetaNews provide a structured approach to network observability, the alternative steps proposed here offer a more comprehensive, proactive, and user-centric strategy. By integrating diverse data sources, leveraging AI, implementing proactive incident response, establishing continuous improvement practices, and focusing on user experience, organizations can achieve a higher level of network observability that not only ensures network performance but also supports business objectives.

A Holistic Approach to Network Observability: Beyond the “Five Steps” Read More »

The Growing DevSecOps Market: Current Trends and Future Prospects

The DevSecOps market is experiencing significant growth, driven by the increasing demand for secure software development practices. According to recent research, the market is projected to reach a staggering US$ 45.93 billion by 2032, growing at a CAGR of 24.7%. This rapid expansion underscores the critical role of integrating security into the DevOps process, ensuring that applications are secure from the outset.

Current Popular DevSecOps Solutions

Several DevSecOps solutions are currently leading the market, each offering unique features to enhance security throughout the software development lifecycle:

1. Jenkins: Widely adopted for continuous integration and continuous delivery (CI/CD), Jenkins automates various aspects of software development, ensuring security checks are integrated seamlessly.

2. Aqua Security: This platform focuses on cloud-native applications, providing comprehensive CI/CD integration and thorough vulnerability scanning.

3. Checkmarx: Known for its robust static code analysis capabilities, Checkmarx helps identify vulnerabilities early in the development process.

4. SonarQube: An open-source tool that offers static code analysis, SonarQube is popular for its ability to detect code quality issues and security vulnerabilities.

 

Emerging Trends and Future Solutions

Looking ahead, several trends and emerging solutions are poised to shape the DevSecOps landscape over the next 24 months:

  1. Automation and AI Integration: Automation will continue to drive efficiency in DevSecOps, with AI playing a crucial role in threat detection and response. This trend will enable faster identification and remediation of security issues.
  2. Tool Consolidation: Organizations are moving towards consolidating their security tools to streamline processes and reduce costs. This approach will enhance the overall security posture by providing a unified view of the security landscape.
  3. Infrastructure as Code (IaC): The adoption of IaC is expected to grow, allowing for more consistent and secure infrastructure management. This practice ensures that security is embedded in the infrastructure from the beginning.
  4. Shift-Left Security: Emphasizing security earlier in the development process, known as “shift-left” security, will become more prevalent. This approach helps in identifying and addressing vulnerabilities before they become critical issues.

Conclusion

The DevSecOps market is on a robust growth trajectory, driven by the need for secure software development practices. Current solutions like Jenkins, Aqua Security, Checkmarx, and SonarQube are leading the way, while emerging trends such as automation, tool consolidation, IaC, and shift-left security are set to shape the future. As organizations continue to prioritize security, the DevSecOps market will undoubtedly see further innovation and expansion.

References:

1. DevSecOps Market Size Worth US$ 45.93 Billion by 2032
2.25 Top DevSecOps Tools (Ultimate Guide for 2024)
3.13 Best DevSecOps Tools for 2024 (Paid & Free)
4.DevSecOps Trends for 2024
5.The Future of DevSecOps: Emerging Trends in 2024 and Beyond

The Growing DevSecOps Market: Current Trends and Future Prospects Read More »

Comparing No-Code Mobile Platforms: GoodBarber and Beyond

In the ever-evolving world of mobile app development, no-code platforms have emerged as game-changers, enabling individuals and businesses to create fully functional mobile apps without writing a single line of code. This blog post will compare some of the leading no-code mobile platforms, with a special focus on GoodBarber, to help you choose the best tool for your needs.

GoodBarber: A Closer Look

GoodBarber is a popular no-code platform that allows users to build professional-grade mobile apps quickly and efficiently. According to Geeky Gadgets, you can create a fully functional app in under 30 minutes using GoodBarber. Here are some of its standout features:

• User-Friendly Interface: GoodBarber offers an intuitive drag-and-drop interface, making it accessible even for those with no technical background.

• Customization Options: The platform provides a wide range of design templates and customization options, allowing users to create unique and visually appealing apps.

• Advanced Features: GoodBarber supports features like push notifications, geofencing, and in-app purchases, which are essential for modern mobile apps.

• Customer Support: Users have praised GoodBarber’s responsive customer support, which is crucial for resolving issues quickly.

Other Leading No-Code Platforms

While GoodBarber is a strong contender, several other no-code platforms offer unique features and capabilities. Here’s a comparison of some of the top alternatives:

Bubble.io

• Strengths: Highly flexible and powerful, suitable for complex applications.

• Weaknesses: Steeper learning curve compared to other no-code platforms.

• Ideal For: Users who need extensive customization and are willing to invest time in learning the platform.

FlutterFlow

• Strengths: Integrates seamlessly with Google’s Firebase, supports both web and mobile apps.

• Weaknesses: Limited design customization compared to GoodBarber.

• Ideal For: Developers looking for a platform that supports both web and mobile app development.

Adalo

• Strengths: Very user-friendly, great for beginners.

• Weaknesses: Limited scalability for larger projects.

• Ideal For: Small businesses and startups looking to create simple apps quickly.

Glide

• Strengths: Excellent for creating data-driven apps using Google Sheets.

• Weaknesses: Limited to the functionalities provided by Google Sheets.

• Ideal For: Users who need to create apps that are heavily reliant on data.

Webflow

• Strengths: Powerful design capabilities, great for web apps.

• Weaknesses: Not specifically tailored for mobile app development.

• Ideal For: Designers and developers focused on web applications.

Key Considerations When Choosing a No-Code Platform

When selecting a no-code platform, consider the following factors:

• Ease of Use: How intuitive is the platform? Can you start building immediately, or is there a steep learning curve?

• Customization: Does the platform offer enough design and functionality customization to meet your needs?

• Scalability: Can the platform handle the growth of your app, or will you need to switch to a more robust solution as your user base expands?

• Support and Community: Is there a strong support system and active community to help you troubleshoot and improve your app?

Conclusion

No-code platforms like GoodBarber have democratized mobile app development, making it accessible to a broader audience. Whether you’re a small business owner, a startup founder, or an individual with a great app idea, there’s likely a no-code platform that fits your needs.

Comparing No-Code Mobile Platforms: GoodBarber and Beyond Read More »

Embracing AI in Cyberdefense: Practical Tips for Successful Adoption

Artificial Intelligence (AI) is often seen as a double-edged sword in the realm of cybersecurity. While it can be a formidable ally in defending against cyber threats, it also presents new challenges and risks. A recent report by GetApp highlights the growing recognition among IT professionals of AI’s potential in cyberdefense and provides practical tips for its successful adoption. Let’s delve into the key insights from this report and explore how organizations can effectively integrate AI into their cybersecurity strategies.

The Growing Role of AI in Cyberdefense

According to the report, a significant majority of IT and data security professionals view AI as more of an ally than a threat. Specifically, 64% of U.S. respondents see AI as a beneficial tool in their cybersecurity arsenal. This positive sentiment is driven by AI’s capabilities in areas such as network traffic monitoring, threat detection, and automated response.

Key Benefits of AI in Cybersecurity

1. Enhanced Threat Detection: AI can analyze vast amounts of data in real-time, identifying anomalies and potential threats that might go unnoticed by human analysts. This capability is crucial for early detection and mitigation of cyber attacks.
2. Automated Response: AI can automate routine tasks and responses to common threats, freeing up human resources to focus on more complex issues. This not only improves efficiency but also reduces the time taken to respond to incidents.
3. Predictive Analytics: By leveraging machine learning and deep learning algorithms, AI can predict potential vulnerabilities and threats, allowing organizations to proactively strengthen their defenses.

Practical Tips for AI Adoption in Cyberdefense

1. Plan Around AI’s Strengths: Organizations should set clear goals for AI deployment, focusing on areas where AI can provide the most value, such as threat detection and prevention. This involves understanding the specific cyber threats faced by the organization and how AI can address them.

2. Prioritize Human-in-the-Loop (HITL) Approaches: While AI can automate many tasks, human oversight remains crucial. HITL approaches ensure that AI systems are guided and monitored by human experts, enhancing their effectiveness and reliability.

3. Get Data AI-Ready: The effectiveness of AI in cybersecurity depends heavily on the quality of data it is trained on. Organizations should invest in data preparation, ensuring that their datasets are comprehensive, accurate, and relevant to the threats they aim to mitigate.

Challenges and Considerations

Despite its potential, the adoption of AI in cybersecurity is not without challenges. Key obstacles include:

Skill Gaps: There is a shortage of professionals skilled in both AI and cybersecurity, which can hinder effective implementationh.

Data Privacy: Ensuring that AI systems comply with data privacy regulations is critical, as mishandling sensitive information can lead to significant legal and reputational risks.

Trust and Transparency: Building trust in AI systems requires transparency in how they operate and make decisions. Organizations must ensure that their AI tools are explainable and accountable.

Conclusion

AI holds immense promise for enhancing cybersecurity, offering advanced capabilities in threat detection, automated response, and predictive analytics. However, successful adoption requires careful planning, human oversight, and robust data management. By following the practical tips outlined in the GetApp report, organizations can harness the power of AI to build more resilient and proactive cyber defenses.

Embracing AI in Cyberdefense: Practical Tips for Successful Adoption Read More »

Machine Learning for Network Security, Detection and Response

Cybersecurity is the defense mechanism used to prevent malicious attacks on computers and electronic devices. As technology becomes more advanced, it will require more complex skills to detect malicious activities and computer networks’ flaws. This is where machine learning can help.

Machine learning is a subset of artificial intelligence that uses algorithms and statistical analysis to make assumptions about a computer’s behavior. It can help organizations address new security challenges, such as scaling up security solutions, detecting unknown and advanced attacks, and identifying trends and anomalies. Machine learning can also help defenders more accurately detect and triage potential attacks, but it may bring new attack surfaces of its own.

Machine learning can be used to detect malware in encrypted traffic, find insider threat, predict “bad neighborhoods” online, and protect data in the cloud by uncovering suspicious user behavior. However, machine learning is not a silver bullet for cybersecurity. It depends on the quality and quantity of the data used to train the models, as well as the robustness and adaptability of the algorithms.

A common challenge faced by machine learning in cybersecurity is dealing with false positives, which are benign events that are mistakenly flagged as malicious. False positives can overwhelm analysts and reduce their trust in the system. To overcome this challenge, machine learning models need to be constantly updated and validated with new data and feedback.

Another challenge is detecting unknown or zero-day attacks, which are exploits that take advantage of vulnerabilities that have not been discovered or patched yet. Traditional security solutions based on signatures or rules may not be able to detect these attacks, as they rely on prior knowledge of the threat. Machine learning can help to discover new attack patterns or adversary behaviors by using techniques such as anomaly detection, clustering, or reinforcement learning.

Anomaly detection is the process of identifying events or observations that deviate from the normal or expected behavior of the system. For example, machine learning can detect unusual network traffic, login attempts, or file modifications that may indicate a breach.

Clustering is the process of grouping data points based on their similarity or proximity. For example, machine learning can cluster malicious domains or IP addresses based on their features or activities, and flag them as “bad neighborhoods” online.

Reinforcement learning is the process of learning by trial and error, aiming to maximize a cumulative reward. For example, machine learning can learn to optimize the defense strategy of a system by observing the outcomes of different actions and adjusting accordingly.

Machine learning can also leverage statistics, time, and correlation-based detections to enhance its performance. These indicators can help to reduce false positives, identify causal relationships, and provide context for the events. For example, machine learning can use statistical methods to calculate the probability of an event being malicious based on its frequency or distribution. It can also use temporal methods to analyze the sequence or duration of events and detect anomalies or patterns. Furthermore, it can use correlation methods to link events across different sources or domains and reveal hidden connections or dependencies.

Machine learning is a powerful tool for cybersecurity, but it also requires careful design, implementation, and evaluation. It is not a one-size-fits-all solution, but rather a complementary approach that can augment human intelligence and expertise. Machine learning can help to properly navigate the digital ocean of incoming security events, particularly where 90% of them are false positives. The need for real-time security stream processing is now bigger than ever.

Machine Learning for Network Security, Detection and Response Read More »

Predictive Networks: is it the Future?

Post-chatGPT Update as of May 26th, 2023:
Cisco and their EVP Liz Centoni have probably never been so wrong before in their useless predictions!

“Predictive Network” is a cool term but it goes down to some things that Cisco EVP Liz Centoni does not consider cool or trending anymore: Artificial Intelligence (AI) and Machine Learning (ML), which collect and analyze millions of network events, delivering problem-solving solutions. AI-based Predictive Networks, that by the way, are one of Liz’s 2023 “trends” predictions are contradicting her statement that

The cloud and AI are no longer frontiers

Obviously, Cisco’s EVP and Chief Strategy Officer Centoni refers to Cisco’s own Predictive Network product which, quoting Cisco now

 rely on a predictive engine in charge of computing (statistical, machine learning) models of the network using several telemetry sources

So how exactly AI is “no longer the frontier” Liz, if machine learning powers Predictive Networks that you predict to become a 2023 trend?

Predictive Networks: is it the Future? Read More »