cybersecurity

Embracing AI in Cyberdefense: Practical Tips for Successful Adoption

artificial intelligence, cybersecurity

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.

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Tor vs. VPNs: A Comprehensive Comparison

cybersecurity

In the quest for online privacy and security, two tools often come up: Tor and VPNs. Both offer unique advantages and cater to different needs. This blog post will delve into the differences between Tor and VPNs, their pros and cons, and compare some of the top VPN providers available today.

What is Tor?

Tor, short for The Onion Router, is a free, open-source software that enables anonymous internet browsing. It routes your internet traffic through a network of volunteer-operated servers (nodes), making it difficult to trace your online activity back to you.

Pros of Tor:

1. Anonymity: Tor provides high levels of anonymity by routing traffic through multiple nodes, making it nearly impossible to trace.
2. Access to the Dark Web: Tor allows access to .onion sites, which are not indexed by traditional search engines.
3. Free to Use: Tor is completely free and open-source.

Cons of Tor:

1. Speed: Due to multiple layers of encryption and routing, Tor can be significantly slower than a VPN.
2. Limited Use: Tor is primarily designed for browsing and may not be suitable for other internet activities like streaming or gaming.
3. Blocked by Some Sites: Some websites block traffic from known Tor exit nodes.

What is a VPN?

A Virtual Private Network (VPN) encrypts your internet traffic and routes it through a server in a location of your choice. This masks your IP address and secures your online activities from prying eyes.

Pros of VPNs:

1. Speed: VPNs generally offer faster connection speeds compared to Tor, making them suitable for streaming, gaming, and other bandwidth-intensive activities.
2. Security: VPNs provide strong encryption, protecting your data from hackers and surveillance.
3. Geo-Spoofing: VPNs allow you to bypass geo-restrictions and access content from different regions.

Cons of VPNs:

1. Cost: High-quality VPN services usually require a subscription fee
2. Trust: You need to trust your VPN provider with your data, as they can potentially log your activities.
3. Not Completely Anonymous: While VPNs enhance privacy, they do not offer the same level of anonymity as Tor.

Comparing Top VPN Providers

ExpressVPN

Pros: High-speed servers, strong encryption, user-friendly interface, excellent customer support.
Cons: More expensive than other options

NordVPN

Pros: Advanced security features, including double VPN and Onion over VPN, fast speeds, large server network.
Cons: Slightly higher cost, complex pricing structurehttps://www.techradar.com/vpn/best-vpn.

Surfshark

Pros: Affordable pricing, unlimited devices, strong security features, fast speeds.
Cons: Newer provider, smaller server network compared to others.

CyberGhost

Pros: User-friendly, strong privacy policies, optimized servers for streaming.
Cons: Slower speeds on some servers, higher cost for monthly plans.

ProtonVPN

Pros: Strong focus on privacy, free plan available, secure core servers.
Cons: Limited server locations, slower speeds on the free plan.

Conclusion

Both Tor and VPNs have their place in the world of online privacy and security. Tor is ideal for those who need maximum anonymity and are willing to sacrifice speed, while VPNs are better suited for everyday use, offering a balance of speed, security, and convenience. When choosing a VPN provider, consider factors such as speed, security features, cost, and ease of use to find the best fit for your needs.

For more detailed comparisons and reviews, you can explore resources like PCMag and TechRadar.

Feel free to share your thoughts or ask any questions about these privacy tools in the comments below!

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The Impact of Unified Security Intelligence on Cybersecurity and Network Monitoring Companies

cloud monitoing, cybersecurity, monitoring

The recent collaboration between major cloud service providers (CSPs) and federal agencies to create a unified security intelligence initiative marks a significant milestone in the cybersecurity landscape. This initiative, spearheaded by the Cloud Safe Task Force, aims to establish a “National Cyber Feed” that provides continuous threat-monitoring data to federal cybersecurity authorities. This unprecedented move is set to have far-reaching implications for companies that develop cybersecurity and network monitoring solutions.

Enhanced Threat Intelligence

One of the primary benefits of this initiative is the enhancement of threat intelligence capabilities. By pooling resources and data from leading CSPs like Amazon, Google, IBM, Microsoft, and Oracle, the National Cyber Feed will offer a comprehensive and real-time view of the threat landscape. This unified approach will enable cybersecurity companies to access richer and more timely threat intelligence, allowing them to develop more effective and proactive security measures.

For companies specializing in network monitoring solutions, this initiative provides an opportunity to integrate advanced threat intelligence into their platforms. Enhanced visibility into potential threats will enable these companies to offer more robust and accurate monitoring services, ultimately improving their clients’ security postures.

 

Increased Collaboration and Standardization

The collaboration between cloud giants and federal agencies sets a precedent for increased cooperation and standardization within the cybersecurity industry. This initiative encourages the sharing of threat data and best practices, fostering a more collaborative environment among cybersecurity companies. As a result, companies will be better equipped to address emerging threats and develop standardized protocols for threat detection and response.

For network monitoring solution providers, this increased collaboration can lead to the development of more interoperable and cohesive monitoring tools. Standardized threat intelligence feeds and protocols will enable these companies to create solutions that seamlessly integrate with other security tools, providing a more comprehensive security ecosystem for their clients.

Competitive Advantage and Innovation

The unified security intelligence initiative also presents a competitive advantage for companies that can effectively leverage the enhanced threat intelligence and collaborative environment. Cybersecurity companies that quickly adapt to this new landscape and incorporate the latest threat data into their solutions will be better positioned to offer cutting-edge security services. This can lead to increased market share and a stronger reputation in the industry.

Moreover, the initiative is likely to spur innovation within the cybersecurity sector. Companies will be motivated to develop new technologies and methodologies to harness the power of unified threat intelligence. This could result in the creation of more advanced and sophisticated security solutions, further strengthening the overall cybersecurity infrastructure.

Challenges and Considerations

While the unified security intelligence initiative offers numerous benefits, it also presents certain challenges and considerations for cybersecurity and network monitoring companies. One of the primary challenges is ensuring data privacy and compliance. Companies must navigate the complexities of sharing threat data while adhering to strict privacy regulations and maintaining the confidentiality of sensitive information.

Additionally, the integration of unified threat intelligence into existing security solutions may require significant investment in technology and resources. Companies will need to invest in advanced analytics, machine learning, and artificial intelligence to effectively process and utilize the vast amounts of threat data generated by the National Cyber Feed.

Conclusion

The collaboration between cloud giants and federal agencies to create a unified security intelligence initiative is poised to transform the cybersecurity landscape. For companies that develop cybersecurity and network monitoring solutions, this initiative offers enhanced threat intelligence, increased collaboration, and opportunities for innovation. However, it also presents challenges related to data privacy and integration. By navigating these challenges and leveraging the benefits of unified threat intelligence, cybersecurity companies can strengthen their offerings and contribute to a more secure digital environment.

What are your thoughts on this initiative? How do you think it will shape the future of cybersecurity?

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How to Avoid Common Cloud Security Mistakes and Manage Cloud Security Risk

cloud monitoing, cybersecurity, shared fate

Cloud computing has become a dominant trend in the IT industry, offering many benefits such as scalability, flexibility, cost-efficiency, and innovation. However, cloud computing also introduces new challenges and risks for security and compliance. According to a recent report by LogicMonitor, 87% of global IT decision-makers agree that cloud security is a top priority for their organization, but only 29% have complete confidence in their cloud security posture.

Moreover, the report reveals that 66% of respondents have experienced a cloud-related security breach in the past year, and 95% expect more cloud-related security incidents in the future.

Therefore, enterprises need to adopt best practices and strategies to avoid common cloud security mistakes and manage cloud risk effectively.

We are going to review now some of the most common cloud security mistakes made by enterprises, and how to prevent or mitigate them. We will also discuss how to adopt a shared fate approach to manage cloud risk, which is a concept proposed by Google Cloud Security.

Common Cloud Security Mistakes

Some of the most common cloud security mistakes made by enterprises are:

• Lack of visibility and control: Many enterprises do not have a clear understanding of their cloud assets, configurations, dependencies, and vulnerabilities. They also do not have adequate tools and processes to monitor, audit, and enforce their cloud security policies and standards. This can lead to misconfigurations, unauthorized access, data leakage, compliance violations, and other security issues.

• Lack of shared responsibility: Many enterprises do not fully comprehend the shared responsibility model of cloud security, which defines the roles and responsibilities of the cloud provider and the cloud customer. They either assume that the cloud provider is responsible for all aspects of cloud security, or that they are responsible for none. This can result in gaps or overlaps in cloud security coverage, as well as confusion and conflicts in case of a security incident.

• Lack of skills and expertise: Many enterprises do not have enough skilled and experienced staff to handle the complexity and diversity of cloud security challenges. They also do not invest enough in training and education to keep up with the evolving cloud security landscape. This can result in human errors, poor decisions, delayed responses, and missed opportunities.

• Lack of automation and integration: Many enterprises rely on manual processes and siloed tools to manage their cloud security operations. They also do not leverage the automation and integration capabilities offered by the cloud platform and third-party solutions. This can result in inefficiency, inconsistency, redundancy, and scalability issues.

• Lack of governance and compliance: Many enterprises do not have a clear and consistent framework for governing their cloud security strategy, objectives, policies, procedures, roles, and metrics. They also do not have a systematic approach to ensuring compliance with internal and external regulations and standards. This can result in misalignment, confusion, duplication, and non-compliance.

How to Prevent or Mitigate Common Cloud Security Mistakes

To prevent or mitigate these common cloud security mistakes, enterprises should adopt the following best practices and strategies:

• Gain visibility and control: Enterprises should use tools and techniques such as asset inventory, configuration management, dependency mapping, vulnerability scanning, threat detection, incident response, and forensics to gain visibility and control over their cloud environment. They should also implement policies and standards for securing their cloud resources, such as encryption, authentication, authorization, logging, backup, recovery, etc.

• Understand shared responsibility: Enterprises should understand the shared responsibility model of cloud security for each cloud service model (IaaS, PaaS, SaaS) and each cloud provider they use. They should also communicate and collaborate with their cloud providers to clarify their respective roles and responsibilities, as well as their expectations and obligations. They should also review their contracts and service level agreements (SLAs) with their cloud providers to ensure they cover their security requirements.

• Build skills and expertise: Enterprises should hire or train staff who have the necessary skills and expertise to manage their cloud security challenges. They should also provide continuous learning opportunities for their staff to update their knowledge and skills on the latest cloud security trends and technologies. They should also seek external help from experts or consultants when needed.

• Leverage automation and integration: Enterprises should use automation tools such as scripts.

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Could Shared Fate be the Best Approach for Cloud Security?

cloud monitoing, cybersecurity, shared fate

Cloud security is a critical concern for any organization that uses cloud services to run their applications and store their data. Cloud security involves protecting the confidentiality, integrity, and availability of the cloud resources and data from various threats and risks. However, cloud security is not a simple or straightforward task, as it involves many challenges and complexities.

One of the challenges of cloud security is understanding and applying the shared responsibility model, which defines the roles and responsibilities of the cloud provider and the cloud customer. Depending on the type of cloud service they use (IaaS, PaaS, SaaS), the customer may have more or less control and responsibility over their cloud security. However, the shared responsibility model can sometimes create confusion or gaps in cloud security coverage, as different cloud services have different configuration options and security controls.

Another challenge of cloud security is managing the trust and collaboration between the cloud provider and the customer. The cloud provider and the customer may have different goals, expectations, and incentives when it comes to cloud security. The cloud provider may want to maximize their profit and reputation, while the customer may want to minimize their cost and risk. The cloud provider and the customer may also have different levels of expertise, visibility, and access to the cloud environment. This can result in miscommunication, misunderstanding, or conflict in case of a security incident.

To overcome these challenges and achieve better security outcomes in the cloud, a new approach is needed: shared fate. Shared fate is a concept proposed by Google Cloud Security, which aims to improve the security outcomes for cloud customers and providers. Shared fate is based on the idea that both parties have a common interest and stake in securing the cloud environment, and that they should work together as partners rather than adversaries.

Shared fate goes beyond the traditional shared responsibility model, which defines the roles and responsibilities of the cloud provider and the customer based on the type of cloud service they use. While shared responsibility is still important, it can sometimes create confusion or gaps in cloud security coverage, as different cloud services have different configuration options and security controls.

Shared fate sees the cloud provider accepting the reality of where shared responsibility breaks down and steps up to close the gaps. The cloud provider does this by offering secure-by-default infrastructure, security foundations, and secure blueprints that help customers deploy their workloads in a secure way. The cloud provider also provides guidance, transparency, guardrails, and innovative insurance options to help customers measure and mitigate their cloud risks.

Shared fate also involves the cloud provider and the customer interacting more closely and collaboratively to address cloud security challenges. The cloud provider listens to the customer’s feedback and needs, and provides solutions that meet their security requirements. The customer trusts the cloud provider’s expertise and follows their best practices and recommendations. The cloud provider and the customer share information and insights, and respond to security incidents together.

Shared fate is a better way to manage cloud risk because it creates a win-win situation for both parties. The cloud provider benefits from having more satisfied and loyal customers, as well as a more secure and resilient cloud platform. The customer benefits from having more secure and reliable workloads, as well as a more trusted

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How Cloud Monitoring Can Boost Your DevOps Success

cloud monitoing, cybersecurity, observability

DevOps is a culture and practice that aims to deliver high-quality software products and services faster and more efficiently. DevOps involves the collaboration and integration of various roles and functions, such as development, testing, operations, security, and more. DevOps also relies on various tools and processes, such as code repositories, build pipelines, testing frameworks, deployment tools, and more.

However, DevOps also poses some challenges and risks, such as ensuring the reliability, availability, performance, security, and cost-efficiency of the software products and services. This is especially true when the software products and services are deployed on the cloud, which offers scalability, flexibility, and convenience, but also introduces complexity, variability, and uncertainty.

This is where cloud monitoring comes in. Cloud monitoring is the process of collecting and analyzing data and information from cloud resources, such as servers, containers, applications, services, etc. Cloud monitoring can help DevOps teams to achieve their goals and overcome their challenges by providing them with insights and feedback on various aspects of their cloud-based software products and services.

In this blog post, we will explore how cloud monitoring can boost your DevOps success in four ways:

• Cloud monitoring enables proactive problem detection and resolution: Cloud monitoring can help you to detect and resolve problems before they affect your end-users or your business outcomes. By using cloud monitoring tools, you can collect and analyze various metrics and logs from your cloud resources, such as CPU, memory, disk, network, latency, errors, etc. You can also set up alerts and notifications to inform you of any anomalies or issues that may indicate a potential problem. This way, you can quickly identify the root cause of the problem and take corrective actions to fix it.

• Cloud monitoring facilitates performance optimization and cost efficiency: Cloud monitoring can help you to optimize the performance and scalability of your cloud-based software products and services by providing you with insights into resource utilization, load balancing, auto-scaling, etc. You can use cloud monitoring tools to measure and benchmark the performance of your cloud resources against your expectations and requirements. You can also use cloud monitoring tools to adjust and optimize your resource allocation and configuration to meet the changing demands and conditions of your end-users and your environment. Additionally, cloud monitoring can help you to reduce the cost of your cloud operations by providing you with visibility into resource consumption, billing, and budgeting. You can use cloud monitoring tools to track and analyze your cloud spending and usage patterns. You can also use cloud monitoring tools to set up limits and alerts to prevent overspending or underutilization of your cloud resources.

• Cloud monitoring supports continuous delivery and integration: Cloud monitoring can help you to achieve continuous delivery and integration of your cloud-based software products and services by providing you with feedback and validation throughout the development and deployment lifecycle. You can integrate cloud monitoring tools with other DevOps tools and processes, such as code repositories, build pipelines, testing frameworks, deployment tools, etc. You can use cloud monitoring tools to monitor the quality and functionality of your code changes as they are integrated into the main branch. You can use cloud monitoring tools to measure and benchmark the performance of your cloud resources against your expectations and requirements. You can also use cloud monitoring tools to adjust and optimize your resource allocation and configuration to meet the changing demands and conditions of your end-users and your environment. Additionally, cloud monitoring can help you to reduce the cost of your cloud operations by providing you with visibility into resource consumption, billing, and budgeting. You can use cloud monitoring tools to track and analyze your cloud spending and usage patterns. You can also use cloud monitoring tools to set up limits and alerts to prevent overspending or underutilization of your cloud resources.

• Cloud monitoring supports continuous delivery and integration: Cloud monitoring can help you to achieve continuous delivery and integration of your cloud-based software products and services by providing you with feedback and validation throughout the development and deployment lifecycle. You can integrate cloud monitoring tools with other DevOps tools and processes, such as code repositories, build pipelines, testing frameworks, deployment tools, etc. You can use cloud monitoring tools to monitor the quality and functionality of your code changes as they are integrated into the main branch. You can also use cloud monitoring tools to monitor the status and health of your deployments as they are rolled out to different environments or regions. This way, you can ensure that your software products and services are always in a deployable state and meet the quality standards and expectations of your end-users and your stakeholders.

• Cloud monitoring fosters collaboration and communication: Cloud monitoring can help you to improve collaboration

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Cloud Security Monitoring Trends

cloud monitoing, cybersecurity

Cloud security monitoring helps organizations detect and respond to threats, vulnerabilities, misconfigurations, compliance violations, and incidents in their cloud environments.

Cloud security monitoring is becoming more important and challenging as organizations adopt cloud services at an increasing rate and face new and evolving risks in the cloud. According to Gartner, 92% of organizations currently host their IT environment in the cloud, but with major advantages follow some critical security threats.

In this article, we will explore some of the key trends and developments that will shape the cloud security monitoring landscape in 2023 and beyond.

Trend 1: Cloud Security Posture Management (CSPM)

One of the main challenges of cloud security monitoring is the lack of visibility and control over the configuration and status of cloud resources and services. Misconfiguration, lack of visibility, identity, and unauthorized access are among the highest-ranked cloud threats, according to a survey by Oracle and KPMG.

Cloud Security Posture Management (CSPM) is a solution that helps organizations address this challenge by continuously assessing and improving their cloud security posture. CSPM tools automate the discovery and remediation of cloud misconfigurations, enforce security policies and best practices, provide compliance assurance, and generate reports and dashboards for visibility and accountability.

CSPM is expected to grow in demand and adoption this year, as organizations realize the benefits of proactive and preventive cloud security monitoring. According to Gartner, by 2024, 80% of cloud breaches will be due to customer misconfiguration, mismanaged credentials or insider theft, which CSPM tools can help prevent or mitigate.

Trend 2: Data Protection Before It Reaches the Cloud

Another challenge of cloud security monitoring is ensuring the protection of sensitive data that is stored or processed in the cloud. Data loss and leakage are among the top cloud security concerns in 2021, according to a report by Netwrix.

Data protection in the cloud involves encrypting data at rest and in transit, applying access controls and permissions, implementing data loss prevention (DLP) policies, and monitoring data activity and anomalies. However, these measures may not be enough to prevent data breaches or comply with data privacy regulations.

Therefore, some organizations are adopting a more proactive approach to data protection by encrypting or anonymizing data before it reaches the cloud. This way, they can reduce the risk of exposing sensitive data to unauthorized parties or compromising their data sovereignty.

One example of this approach is Bring Your Own Key (BYOK) encryption, which allows organizations to use their own encryption keys to encrypt data before sending it to the cloud. This gives them more control over their data security and access. However, BYOK encryption also requires careful management of the encryption keys and compatibility with the cloud service providers.

Trend 3: Digital Supply Chain Risk Management

The digital supply chain refers to the network of vendors, partners, suppliers, and customers that provide or consume digital products or services. The digital supply chain can introduce new risks for cloud security monitoring, as attackers can exploit vulnerabilities or compromise third-party components or services to gain access to target systems or data.

The SolarWinds breach in 2020 was a prominent example of a digital supply chain attack that affected thousands of organizations worldwide. The attackers inserted malicious code into a software update from SolarWinds, a network management software provider, which then infected its customers’ systems.

To prevent or mitigate such attacks, organizations need to adopt a holistic approach to digital supply chain risk management. This involves identifying and assessing the risks associated with their digital supply chain partners, implementing security standards and controls for third-party access and integration, monitoring their digital supply chain activity and performance, and responding to incidents or alerts promptly.

Trend 4: Vendor Consolidation

The cloud security monitoring market is fragmented and complex, with many vendors offering different products and services for various aspects and layers of cloud security. This can create challenges for organizations such as interoperability issues, redundant features, inconsistent policies or vendor lock-in. Therefore, some organizations are looking for more integrated and comprehensive solutions for cloud security monitoring that can reduce complexity, cut costs, and improve efficiency. This leads to a trend of vendor consolidation where vendors merge, acquire, or partner with other vendors
to offer more complete
and unified platforms for cloud security monitoring

Some examples of vendor consolidation in the cloud security monitoring space are:

Vendor consolidation can offer benefits for organizations such as:

  • Simplified procurement and management of cloud security monitoring tools
  • Enhanced visibility and correlation across multiple sources and types of data
  • Improved scalability and performance of cloud security monitoring solutions

However, vendor consolidation can also introduce some challenges such as:

  • Reduced negotiating power and flexibility with vendors
  • Potential single points of failure or compromise in case of vendor breaches or outages
  • Increased dependency on vendor support or updates

Summary

Cloud security monitoring is a vital function for organizations that use cloud services for their IT operations and business processes. Cloud security monitoring helps organizations detect and respond to threats, vulnerabilities, misconfigurations compliance violations, and incidents in their cloud environments.

However, cloud security monitoring is also evolving rapidly as organizations face new and emerging risks in the cloud. Some of the key trends that will shape the cloud security monitoring landscape in this year are:

  • Cloud Security Posture Management (CSPM)
  • Data Protection Before It Reaches the Cloud
  • Digital Supply Chain Risk Management
  • Vendor Consolidation

Organizations need to be aware of these trends and adapt their strategies, tools, processes and skills accordingly to ensure effective efficient and secure cloud security monitoring in this year and beyond.

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Cloud Native Security: Cloud Native Application Protection Platforms

cloud monitoing, cybersecurity, monitoring, network observability, observability

Back in 2022, 77% of interviewed CIOs stated that their IT environment is constantly changing. We can only guess that this number, would the respondents be asked today, will be as high as 90%+. Detecting flaws and security vulnerabilities becomes more and more challenging in 2023 since the complexity of typical software deployment is exponentially increasing year to year. The relatively new trend of Cloud Native Application Protection Platforms (CNAPP) is now supported by the majority of cybersecurity companies, offering their CNAPP solutions for cloud and on-prem deployments.

CNAPP rapid growth is driven by cybersecurity threats, while misconfiguration is one of the most reported reasons for security breaches and data loss. While workloads and data move to the cloud, the required skill sets of IT and DevOps teams must also become much more specialized. The likelihood of an unintentional misconfiguration is increased because the majority of seasoned IT workers still have more expertise and got more training on-prem than in the cloud. In contrast, a young “cloud-native” DevOps professional has very little knowledge of “traditional” security like network segmentation or firewall configuration, which will typically result in configuration errors.

Some CNAPP are proud to be “Agentless” eliminating the need to install and manage agents that can cause various issues, from machine’ overload to agent vulnerabilities due to security flows and, guess what, due to the agent’s misconfiguration. Agentless monitoring has its benefits but it is not free of risks. Any monitored device should be “open” for such monitoring, typically coming from a remote server. If an adversary was able to fake a monitoring attempt, he can easily get access to all the monitored devices and compromise the entire network. So “agentless CNAPP” does not automatically mean a better solution than a competing security platform. Easier for maintenance by IT staff? Yes, it is. Is it more secure? Probably not.

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Machine Learning for Network Security, Detection and Response

artificial intelligence, cloud monitoing, cybersecurity, deep observability

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.

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Gartner: “it is the user, not the cloud provider” who causes data breaches

cybersecurity

Gartner’s recommendations on cloud computing strategy open the rightful discussion on the roles and responsibilities of different actors involved in cloud security. How many security and data breaches happen due to Cloud Service Providers (CSP) flaws, and how many of them are caused by CSP’s customers and human beings dealing with the cloud on a daily base? Gartner predicts that through 2025 99% of cloud security failures will be the customer’s fault. Such a prediction can only be based on the current numbers that obviously should demonstrate that the vast majority of breaches come due to CSP clients’ issues.

Among other reason, the first place is taken by data breaches coming from misconfiguration of the cloud environment and security flaws in software that were missed by DevOps and IT teams working in the cloud.

While the workloads and data keep moving to the cloud, DevOps and IT teams often lack the required skill sets to properly configure and maintain cloud-based software. The likelihood of an unintentional misconfiguration is increased because the majority of seasoned IT workers have significantly more expertise and training with on-premises security than they do with the cloud. While younger, less experienced workers may be more acclimated to publishing data to the cloud, they may not be as familiar with dealing with security, which might result in configuration errors.

Some of the team members have near heard of the Roles Based Access Control (RBAC) principle and will have real trouble working in the cloud like AWS being required to properly set up IAM users and IAM roles for each software component and service. These DevOps and IT engineers need to take intensive training to close the cloud security gap. Until it is done the enterprise will keep struggling from improper configuration, production failures and periodic security breaches.

Simple solutions like a firewall can add an additional degree of security for data and workloads, either for on-prem, hybrid, or pure cloud deployments. And yet, even simple things like that add another dimension of IT complexity and risk due to possible misconfiguration because of a human mistake or a vulnerable historical software package.

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