cloud monitoing

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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Containers and Kubernetes Observability Tools and Best Practices

cloud monitoing, containers, deep observability, kubernetes, monitoring, observability

Containers and Kubernetes are popular technologies for developing and deploying cloud-native applications. Containers are lightweight and portable units of software that can run on any platform. Kubernetes is an open-source platform that orchestrates and manages containerized workloads and services.

Containers and Kubernetes offer many benefits, such as scalability, performance, portability, and agility. However, they also introduce new challenges for observability. Observability is the ability to measure and understand the internal state of a system based on the external outputs. Observability helps developers and operators troubleshoot issues, optimize performance, ensure reliability, and improve user experience.

Observability in containers and Kubernetes involves collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the containerized applications and the Kubernetes clusters. These data and events include metrics, logs, traces, events, alerts, dashboards, and reports.

In this article, we will explore some of the tools and best practices for observability in containers and Kubernetes.

Tools for Observability in Containers and Kubernetes

There are many tools available for observability in containers and Kubernetes. Some of them are native to Kubernetes or specific container platforms, while others are third-party or open-source solutions. Some of them are specialized for certain aspects or layers of observability, while others are comprehensive or integrated solutions. Some of them are:

  • Kubernetes Dashboard: Kubernetes Dashboard is a web-based user interface that allows users to manage and monitor Kubernetes clusters and resources. It provides information such as cluster status, node health, pod logs, resource usage, network policies, and service discovery. It also allows users to create, update, delete, or scale Kubernetes resources using graphical or YAML editors.
  • Prometheus: Prometheus is an open-source monitoring system that collects and stores metrics from various sources using a pull model. It supports multi-dimensional data model, flexible query language, alerting rules, and visualization tools. Prometheus is widely used for monitoring Kubernetes clusters and applications, as it can scrape metrics from Kubernetes endpoints, pods, services, and nodes. It can also integrate with other tools such as Grafana, Alertmanager, Thanos, and others.
  • Grafana: Grafana is an open-source visualization and analytics platform that allows users to create dashboards and panels using data from various sources. Grafana can connect to Prometheus and other data sources to display metrics in various formats such as graphs, charts, tables, maps, and more. Grafana can also support alerting, annotations, variables, templates, and other advanced features. Grafana is commonly used for visualizing Kubernetes metrics and performance
  • EFK Stack: EFK Stack is a combination of three open-source tools: Elasticsearch, Fluentd, and Kibana. Elasticsearch is a distributed search and analytics engine that stores and indexes logs and other data. Fluentd is a data collector that collects
    and transforms logs and other data from various sources and sends them to Elasticsearch or other destinations. Kibana is a web-based user interface that allows users to explore and visualize data stored in Elasticsearch. EFK Stack is widely used for logging and observability in containers and Kubernetes as it can collect and analyze logs from containers pods, nodes, services, and other software.
  • Loki: Loki is an open-source logging system that is designed to be cost-effective and easy to operate. Loki is inspired by Prometheus and uses a similar data model and query language. Loki collects logs from various sources using Prometheus service discovery and labels. Loki stores logs in a compressed and indexed format that enables fast and efficient querying. Loki can integrate with Grafana to display logs alongside metrics

Best Practices for Observability in Containers and Kubernetes

Observability in containers and Kubernetes requires following some best practices to ensure effective, efficient, and secure observability Here are some of them:

  • Define observability goals and requirements: Before choosing or implementing any observability tools or solutions, it is important to define the observability goals and requirements for the containerized applications and the Kubernetes clusters These goals and requirements should align with the business objectives, the user expectations, the service level agreements (SLAs), and the compliance standards. They should also specify what data and events to collect, how to analyze them, how to alert on them, and how to visualize them.
  • Use standard formats and protocols: To ensure interoperability and compatibility among different observability tools and solutions, it is recommended to use standard formats and protocols for collecting, storing, and exchanging data and events. For example, use OpenMetrics for metrics, JSON for logs, OpenTelemetry for traces, CloudEvents for events. Containers and Kubernetes Observability Tools and Best Practices. These standards can help reduce complexity, overhead, and vendor lock-in in observability.
  • Leverage native Kubernetes features: Kubernetes provides some native features that can help with observability For example, use labels and annotations to add metadata to Kubernetes resources that can be used for filtering, grouping, or querying. Use readiness probes and liveness probes to check the health status of containers. Use resource requests and limits to specify the resource requirements of containers. Use horizontal pod autoscaler (HPA) or vertical pod autoscaler (VPA) to scale pods based on metrics. Use custom resource definitions (CRDs) or operators to extend the functionality of Kubernetes resources These features can help improve the visibility, control, and optimization of containers and Kubernetes clusters.

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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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Monitoring and Observability in the Oracle Cloud

cloud monitoing, monitoring, observability

Monitoring and observability are essential practices for ensuring the availability, performance, security, and cost-efficiency of cloud-based systems and applications. Monitoring and observability involve collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the cloud environment, such as metrics, logs, traces, and user experience.

Oracle Cloud provides a comprehensive set of tools and services for monitoring and observability of its cloud resources and services. Oracle Cloud also supports integration with third-party tools and standards for monitoring and observability of hybrid and multi-cloud environments.

(Image: Delphi, Greece)

In this article, we will discuss some of the benefits and challenges of monitoring and observability of Oracle Cloud.

Benefits of Monitoring and Observability of Oracle Cloud

Some of the benefits of monitoring and observability of Oracle Cloud are:

  • Visibility: Oracle Cloud provides visibility into the health, performance, usage, and cost of its cloud resources and services. Users can access metrics, logs, events, alerts, dashboards, reports, and analytics from the Oracle Cloud console or APIs. Users can also use Oracle Cloud Observability and Management Platform, which provides a unified view of the observability data across Oracle Cloud and other cloud or on-premises environments.
  • Control: Oracle Cloud provides control over the configuration, management, and optimization of its cloud resources and services. Users can use policies, rules, thresholds, actions, functions, notifications, and connectors to automate monitoring and observability tasks. Users can also use Oracle Cloud Resource Manager to deploy and manage cloud resources using Terraform-based automation.
  • Security: Oracle Cloud provides security for its cloud resources and services. Users can use encryption, access control, identity management, auditing, compliance, firewall, antivirus, vulnerability scanning, and incident response to protect their cloud data and assets. Users can also use Oracle Cloud Security Advisor to assess their security posture and receive recommendations for improvement.
  • Innovation: Oracle Cloud provides innovation for its cloud resources and services. Users can use artificial intelligence (AI), machine learning (ML), natural language processing (NLP), computer vision (CV), blockchain, chatbots, digital assistants, Internet of Things (IoT), edge computing, serverless computing, microservices, containers, and Kubernetes to enhance their cloud capabilities and outcomes. Users can also use Oracle Cloud Enterprise Manager to monitor, analyze, and administer Oracle Database and Engineered Systems

Challenges of Monitoring and Observability of Oracle Cloud

Some of the challenges of monitoring and observability of Oracle Cloud are:

  • Complexity: Oracle Cloud offers a wide range of services and features that can create complexity and confusion for users. Users need to understand and choose the appropriate tools and services for their monitoring and observability needs. Users also need to configure and manage the tools and services properly to avoid errors, misconfigurations, or inefficiencies
  • Integration: Oracle Cloud supports integration with third-party tools and standards for monitoring and observability. However, users need to ensure compatibility, interoperability, and security of the integration solutions. Users also need to deal with potential issues such as data duplication, inconsistency, or loss
  • Skills: Oracle Cloud requires users to have adequate skills and knowledge to use its tools and services for monitoring and observability. Users need to learn how to use the Oracle Cloud console, APIs, CLI, SDKs, and other interfaces. Users also need to learn how to use the Oracle Cloud Observability and Management Platform, Oracle Cloud Resource Manager, Oracle Cloud Security Advisor, Oracle Cloud Enterprise Manager, and other tools and services.

Monitoring and observability are essential practices for ensuring the availability, performance, security, and cost-efficiency of cloud-based systems and applications. Oracle Cloud provides a comprehensive set of tools and services for monitoring and observability of its cloud resources and services. Oracle Cloud also supports integration with third-party tools and standards for monitoring and observability of hybrid and multi-cloud environments.
However, monitoring and observability of Oracle Cloud also pose some challenges such as complexity, integration, and skills Users need to be aware of these challenges and address them accordingly to ensure effective, efficient, and secure monitoring and observability of Oracle Cloud.

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Review of AI Tools for Cloud Monitoring and Observability

cloud monitoing, observability

Cloud monitoring and observability are essential practices for ensuring the availability, performance, and security of cloud-based systems and applications. Cloud monitoring and observability involve collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the cloud environment, such as metrics, logs, traces, and user experience.

However, cloud monitoring and observability can also be challenging and complex, as cloud environments are dynamic, distributed, heterogeneous, and scalable. Traditional monitoring and observability tools may not be able to cope with the volume, velocity, variety, and veracity of cloud data and events. Moreover, human operators may not be able to process and act on the data and events in a timely and effective manner.

This is where artificial intelligence (AI) tools can help. AI tools can leverage machine learning (ML), natural language processing (NLP), computer vision (CV), and other techniques to enhance cloud monitoring and observability capabilities. AI tools can provide benefits such as:

  • Automated data collection and ingestion from various sources and formats
  • Intelligent data processing and analysis to identify patterns, anomalies, correlations, and causations
  • Actionable insights and recommendations to optimize performance, reliability, security, and cost
  • Automated remediation and resolution of issues using predefined or self-learning actions
  • Enhanced user interface and user experience using natural language or visual interactions

In this article, we will explore some of the AI tools that are used or can be used for cloud monitoring and observability. We will also review some of the features, benefits, and challenges of these tools.

Dynatrace

Dynatrace is a software intelligence platform that provides comprehensive observability for hybrid and multi-cloud ecosystems. Dynatrace uses AI to automate data collection and analysis, provide actionable answers to performance problems, optimize resource allocation, and deliver superior customer experience.

Some of the features of Dynatrace are:

  • Automatic discovery and instrumentation of all applications, containers, services, processes, and infrastructure
  • Real-time topology mapping that captures and unifies the dependencies between all observability data
  • Causation-based AI engine that automates root-cause analysis and provides precise answers
  • OpenTelemetry integration that extends the breadth of cloud observability
  • Scalability and efficiency that ensure complete observability even in highly dynamic environments

Some of the benefits of Dynatrace are:

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

Some of the challenges of Dynatrace are:

  • 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

IBM Observability by Instana APM

IBM Observability by Instana APM is a solution that provides end-to-end visibility into serverless applications on AWS Lambda. IBM Observability by Instana APM uses AI to collect metrics, logs, and traces from AWS Lambda functions, provide real-time dashboards, alerts, and insights into the performance, errors, costs, and dependencies of serverless applications.

Some of the features of IBM Observability by Instana APM are:

  • Agentless data ingestion that does not require any code changes or configuration
  • Domain-specific AI engine that enables data organization and analysis
  • High-cardinality view that allows filtering and slicing by any attribute or dimension
  • Distributed tracing that supports OpenTelemetry standards
  • Cost optimization that monitors usage and cost of serverless functions

Some of the benefits of IBM Observability by Instana APM are:

  • Easy deployment and integration with AWS Lambda
  • Comprehensive coverage and granularity of serverless data
  • Fast detection and resolution of serverless issues

Some of the challenges of IBM Observability by Instana APM are:

  • Limited support for other serverless platforms or providers
  • Dependency on AWS services for data storage or streaming
  • Potential data privacy or sovereignty issues

Elastic Observability

Elastic Observability is a solution that provides unified observability for hybrid and multi-cloud ecosystems,
including AWS, Azure, Google Cloud Platform, and more. Elastic Observability allows users to ingest telemetry data from various sources such as logs, metrics, traces, and uptime using Elastic Agents or Beats shippers It also provides powerful search, analysis, and visualization capabilities using Elasticsearch engine, Kibana dashboard, and Elastic APM service.

Some of the features of Elastic Observability are:

  • Agent-based or agentless data ingestion that supports various protocols, formats, and standards
  • Open source platform that allows customization, extension, and integration
  • Scalable architecture that can handle large volumes of data at high speed
  • Anomaly detection that uses ML to identify unusual patterns or behaviors
  • Alerting framework that supports multiple channels, actions, and integrations

Some of the benefits of Elastic Observability are:

  • Flexible deployment options on-premises, in the cloud, or as a service
  • Cost-effective pricing model based on resource consumption
  • Rich ecosystem of plugins, integrations, and community support

Some of the challenges of Elastic Observability are:

  • Complex installation and configuration process
  • High learning curve for users who are not familiar with Elasticsearch or Kibana
  • Potential security or compliance issues with open source software

Summary

AI tools can enhance cloud monitoring and observability capabilities by automating data collection and analysis, providing actionable insights and recommendations, and enabling automated remediation and resolution of issues. We have reviewed some of the AI tools that can be used for cloud monitoring and observability:

  • Dynatrace
  • IBM Observability by Instana APM
  • Elastic Observability

These tools have different features, benefits, and challenges that users should consider before choosing one.

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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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AWS vs Azure: Serverless Observability and Monitoring

cloud monitoing, monitoring, observability

Serverless computing is a cloud service model that allows developers to run code without provisioning or managing servers. Serverless applications are composed of functions that are triggered by events and run on demand. Serverless computing offers many benefits, such as scalability, performance, cost-efficiency, and agility.

However, serverless computing also introduces new challenges for observability and monitoring. Observability is the ability to measure and understand the internal state of a system based on the external outputs. Monitoring is the process of collecting, analyzing, and alerting on the metrics and logs that indicate the health and performance of a system.

Observability and monitoring are essential for serverless applications because they help developers troubleshoot issues, optimize performance, ensure reliability, and improve user experience. However, serverless applications are more complex and dynamic than traditional applications, making them harder to observe and monitor.

Some of the challenges of serverless observability and monitoring are:

  • Lack of visibility: Serverless functions are ephemeral and stateless, meaning they are created and destroyed on demand, and do not store any data or context. This makes it difficult to track the execution flow and dependencies of serverless functions across multiple services and platforms.
  • High cardinality: Serverless functions can have many variations based on input parameters, environment variables, configuration settings, and runtime versions. This creates a high cardinality of metrics and logs that need to be collected and analyzed.
  • Distributed tracing: Serverless functions can be triggered by various sources, such as HTTP requests, messages, events, timers, or other functions. This creates a distributed tracing problem, where developers need to correlate the traces of serverless functions across different sources and services.
  • Cold starts: Serverless functions can experience cold starts, which are delays in the execution time caused by the initialization of the function code and dependencies. Cold starts can affect the performance and availability of serverless applications, especially for latency-sensitive scenarios.
  • Cost optimization: Serverless functions are billed based on the number of invocations and the execution time. Therefore, developers need to monitor the usage and cost of serverless functions to optimize their resource allocation and avoid overspending.

AWS and Azure are two of the leading cloud providers that offer serverless computing services. AWS Lambda is the serverless platform of AWS, while Azure Functions is the serverless platform of Azure. Both platforms provide observability and monitoring features for serverless applications, but they also have some differences and limitations.

In this article, we will compare AWS Lambda and Azure Functions in terms of their observability and monitoring capabilities, including their native features and third-party software reviews and recommendations.

Native Features

Both AWS Lambda and Azure Functions provide native features for observability and monitoring serverless applications. These features include:

  • Metrics: Both platforms collect and display metrics such as invocations, errors, duration, memory usage, concurrency, and throughput for serverless functions. These metrics can be viewed on dashboards or queried using APIs or CLI tools. Metrics can also be used to create alarms or alerts based on predefined thresholds or anomalies.
  • Logs: Both platforms capture and store logs for serverless functions. These logs include information such as start and end time, request ID, status code, error messages, custom print statements, etc. Logs can be viewed on consoles or queried using APIs or CLI tools. Logs can also be streamed or exported to external services for further analysis or retention.
  • Tracing: Both platforms support distributed tracing for serverless functions. Distributed tracing allows developers to track the execution flow and latency
    of serverless functions across different sources and services. Tracing can help identify bottlenecks errors, failures or performance issues in serverless applications.

Both platforms use open standards such as OpenTelemetry or W3C Trace Context for tracing. However, there are also some differences between AWS Lambda and Azure Functions in terms of their native features for observability and monitoring.

Some of these differences are:

  • Metrics granularity: AWS Lambda provides metrics at a 1-minute granularity by default while Azure Functions provides metrics at a 5-minute granularity by default
    However, both platforms allow users to change the granularity to a lower or higher level depending on their needs
  • Metrics aggregation: AWS Lambda aggregates metrics by function name function version or alias (if specified), region (if specified) or globally (across all regions). Azure Functions aggregates metrics by the function name (or function app name), region (if specified) or globally (across all regions).
  • Logs format: AWS Lambda logs are formatted as plain text with a timestamp prefix. Azure Functions logs are formatted as JSON objects with various fields such as timestamp, level, message, category, functionName, invocationId, etc.
  • Logs retention: AWS Lambda logs are stored in Amazon CloudWatch Logs service for 90 days by default (or longer if specified by users). Azure Functions logs are stored in Azure Monitor service for 30 days by default (or longer if specified by users)
  • Tracing integration: AWS Lambda integrates with AWS X-Ray service for tracing. AWS X-Ray provides a web console and an API for viewing traces and analyzing the performance of serverless applications on AWS. Azure Functions integrates with Azure Application Insights service for tracing. Azure Application Insights provides a web console and an API for viewing traces and analyzing the performance of serverless applications on Azure.

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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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Full Stack IT Observability Will Drive Business Performance in 2023

cloud monitoing, cybersecurity, monitoring, network observability

Cisco predicts that 2023 will be shaped by a few exciting trends in technology, including network observability with business correlation. Cisco’s EVP & Chief Strategy Officer Liz Centoni is sure that

To survive and thrive, companies need to be able to tie data insights derived from normal IT operations directly to business outcomes or risk being overtaken by more innovative competitors

and we cannot agree more.

Proper intelligent monitoring of digital assets along with distributed tracing should be tightly connected to the business context of the enterprise. Thus, any organization can benefit from actionable business insights while improving online and digital user experience for customers, employees, and contractors. Additionally, fast IT response based on artificial intelligence data analysis of monitored and collected network and assets events can prevent or at least provide fast remediation for the most common security threat that exists in nearly any modern digital organization: misconfiguration. 79% of firms have already experienced a data breach in the past 2 years, while 67% of them pointed to security misconfiguration as the main reason.

Misconfiguration of most software products can be timely detected and fixed with data collection and machine learning of network events and configuration files analyzed by network observability and network monitoring tools. An enterprise should require its IT departments to reach full stack observability, and connect the results with the business context. It is particularly important since we know that 99% of cloud security failures are customers’ mistakes (source: Gartner). Business context should be widely adopted as a part of the results delivered by intelligent observability and cybersecurity solutions.

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