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.

Cloud Security Monitoring Trends Read More »

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.

Cloud Native Security: Cloud Native Application Protection Platforms Read More »

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.

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

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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Predictive Networks: is it the Future?

artificial intelligence, network observability
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 »

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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Black Hats Matter

Uncategorized

It is 2023 and we are a few years now since “black lives matter” movement started.

black lives matter

Databases in a cluster are not called “master” and “slave” anymore, and the “blacklist” word for the list of issues is also vaporizing from the lexicons of all reasonable people. The biggest corporations in the US and worldwide are working on fixing the misuse of improper terms in their technology and documentation.

Yet, this Dark Reading article by Jonathan Care decided to bring “Black” word back to mark the evil in their article’s title on December 30, 2022. Yep, it is just a few weeks ago, and that’s why it is triple weird my dear DarkReading editors!

Do not be confused: the word “Black” is used in this article in a very negative context representing the vulnerability of various APIs due to security issues.

DarkReading should be ashamed

The above screenshot represents the original Dark Reading article by Jonathan Care as of December 2022.

Dark Reading should do a much better job while selecting their authors and proofreading texts. This title smells bad! And IMHO this particular article should be removed from the public domain. Shame on you, Dark Reading and Mr. Jonathan Clare, the writer.

Jonathan Care

Please meet Jonathan Care, a Contributing Writer at Dark Reading whose article voted against basic modern community principles like diversity and inclusion. He should have thought twice about his article’s title, but he did not. Probably, he is not exactly a “thinker” type, is he?

Black Hats Matter Read More »

Observability and Protection for Cloud Native Applications

cloud monitoing, network observability

Banks and other financial institutions are moving to the cloud. It is a slow process but the trend is here. Cloud computing business models give financial organizations flexibility to deploy pay-as-you-go cloud services. Furthermore, the cloud comes with built-in scalability so businesses react to market changes quickly. Pay-as-you-go infrastructure drastically reduces costs for banks and financial services institutions (BFSI), but then other questions raise. The first of these questions would be “is it secure to move my data and services to the cloud?”. Here network observability and AI-based network monitoring come to help, and particularly because financial institutions need to be compliant with regulations such as the PIPEDA.

MarketAndMarket report predicts that the market for cloud-native protection platforms will reach $19.3 billion by 2027. This is more than double from $7.8 billion in 2022 as estimated by the marketing firm. BFSI and other enterprises move to the cloud. This requires intelligent network observability and security solutions based on artificial intelligence and machine learning and thus such a rapid market growth at 19.9% CAGR in 2022-2027 seems to be a very reasonable assumption. Today AI-based observability and security solutions analyze hundreds of thousands of events a day. We should expect that the next generation of these software solutions will create and analyze a few orders of magnitudes of events daily, scaling up to tens to hundreds of millions of events a day for an average cloud-based BFSI organization. The report names a few market leaders, among them Check Point (Israel), Trend Micro (Japan), Palo Alto Networks (US), CrowdStrike (US), Fortinet (US), Forcepoint (US), Proofpoint (US), Radware (Israel), Zscaler (US).

Observability and Protection for Cloud Native Applications Read More »