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mariacallous · 3 months ago

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This month, Andrew Bernier, a US Army Corps of Engineers researcher and a union leader, says that he has received a barrage of menacing messages from the same anonymous email account. Unfolding like short chapters in a dystopian novel, they have spoken of the genius of Elon Musk, referenced the power of the billionaire’s so-called Department of Government Efficiency (DOGE), and foretold the downfall of “corrupt” union bosses.

But the most eerie thing about the emails, which Bernier says began arriving after he filed an official charge accusing the Trump administration of violating his union’s collective bargaining agreement, is that they included personal details about his life—some of which he believes might have come from surveillance of his work laptop. The author referenced Bernier’s union activities, nickname, job, travel details, and even the green notebook he regularly uses. The most recent email implied that his computer was loaded with spyware. “Andy's crusade, like so many before it, had been doomed from the start,” one email stated. “The real tragedy wasn't his failure—it was his belief that the fight had ever been real.”

The unsettling messages, which were reviewed by WIRED, are an extreme example of the kinds of encounters that workers across the US government say they have had with technology since President Donald Trump took office. WIRED spoke to current employees at 13 federal agencies for this story who expressed fears about potentially being monitored by software programs, some of which they described as unfamiliar. Others said that routine software updates and notifications, perhaps once readily glossed over, have taken on ominous new meanings. Several reported feeling anxious and hyperaware of the devices and technology around them.

At the General Services Administration (GSA), one worker cited a Chrome browser extension called Dynatrace, an existing program for monitoring app performance. Inside the Social Security Administration (SSA), another employee pointed to Splunk, a longstanding tool that’s used to alert IT staff to security anomalies like when an unauthorized USB drive is plugged into a laptop. At the US Agency for International Development (USAID), one worker was caught off guard by Google’s Gemini AI chatbot, installations of which kicked off days before Trump took office.

“Everyone has been talking about whether our laptops are now able to listen to our conversations and track what we do,” says a current GSA employee, who like other workers in this story, was granted anonymity because they didn’t have authorization to speak and feared retaliation.

Dynatrace and Splunk did not respond to requests for comment from WIRED.

The workers’ accounts come as Musk’s DOGE organization is rapidly burrowing into various government agencies and departments, often gaining access to personnel records, logs of financial transactions, and other sensitive information in the process. The efforts are part of the Trump administration’s broader plan to terminate thousands of government employees and remake the face of federal agencies.

Like many private companies, US federal agencies disclose to staff that they have tools to monitor what workers do on their computers and networks. The US government’s capabilities in this area have also expanded over the past decade.

It couldn’t be learned whether the Trump administration has begun using existing tools to monitor employees in new ways; multiple agencies, including the Social Security Administration and the General Services Administration, denied that they have. The White House did not respond to requests for comment. Public evidence has not emerged of new government purchases of user-monitoring software, which is generally needed for detailed surveillance such as tracking which files a worker has copied onto a thumb drive. Some of the updates and changes that have been noticed by federal workers date back to software purchases and plans enacted long before Trump was in power, WIRED reporting shows.

“I will say my concerns are primarily based in general fear as opposed to specific knowledge,” says a worker at the Department of Homeland Security, who adds: “I’d love to be told I’m wrong.”

But activity that some workers perceive as signs of increased surveillance has prompted them to take precautions. Bernier, who works as a civil engineer for the Army Corps based in Hanover, New Hampshire, says the messages he received spooked him enough that he asked local police to keep an eye on his home, removed the battery from his work-issued laptop, and kept his work phone on airplane mode while traveling to a non-work conference last week. “There are things I don’t control but actions I can take to protect myself and my family,” he says.

Bernier’s anonymous emailer and the Army Corps did not respond to requests for comment.

A person inside the Environmental Protection Agency told WIRED last week that they’ve witnessed coworkers back out of Microsoft Teams meetings, which can be easily recorded and automatically transcribed, when they are related to topics they believe could get them fired. “Definite chilling effect,” the person says. The EPA did not respond to a request for comment.

An employee at the National Oceanic and Atmospheric Administration (NOAA), whose work with international partners is being audited by DOGE operatives, says they and their colleagues began avoiding messaging one another and have “really cut down on putting things in writing” in recent weeks. They report that correspondence from their supervisors has also significantly dropped off. NOAA declined to comment.

At the Federal Bureau of Investigation, anxiety around officials possibly targeting officers and activities perceived as being disloyal to the president has cratered morale, a federal law enforcement source with knowledge of the agents' concerns tells WIRED. The FBI declined to comment.

Aryani Ong, a civil rights activist and cofounder of Asian American Federal Employees for Nondiscrimination, a group that advocates for government workers, says those she’s been in contact with are in a heightened state of alert. In response, some federal employees have turned to encrypted communications apps to connect with colleagues and taken steps to anonymize their social media accounts, Ong says. (Federal workers are granted an allowance to use non-official communication tools only “in exceptional circumstances.”)

Insider Threat

Long before Trump’s inauguration, user activity monitoring was already mandated for federal agencies and networks that handle classified information—the result of an executive order signed by then-president Barack Obama in the wake of a massive breach of classified diplomatic cables and information about the wars in Iraq and Afghanistan in 2010. The capability is part of government-wide insider threat (InTh) programs that greatly expanded after Edward Snowden’s leak of classified surveillance documents in 2013, and again after an Army specialist murdered four colleagues and injured 16 others at Fort Hood in 2014.

The US government’s current approach to digitally monitoring federal workers has largely been guided by a directive issued by the Committee on National Security Systems in 2014, which orders relevant agencies to tie user activity to “specific users.” The public portions of the document call for “every executive branch department and agency” handling classified information to have capabilities to take screenshots, capture keystrokes, and intercept chats and email on employee devices. They are also instructed to deploy “file shadowing,” meaning secretly producing facsimiles of every file a user edits or opens.

The insider threat programs at departments such as Health and Human Services, Transportation, and Veterans Affairs, also have policies that protect unclassified government information, which enable them to monitor employees’ clicks and communications, according to notices in the Federal Register, an official source of rulemaking documents. Policies for the Department of the Interior, the Internal Revenue Service, and the Federal Deposit Insurance Corporate (FDIC), also allow collecting and assessing employees’ social media content.

These internal agency programs, overseen by a national task force led by the attorney general and director of national intelligence, aim to identify behaviors that may indicate the heightened risk of not only leaks and workplace violence, but also the “loss” or "degradation" of a federal agency’s “resources or capabilities.” Over 60 percent of insider threat incidents in the federal sector involve fraud, such as stealing money or taking someone's personal information, and are non-espionage related, according to analysis by Carnegie Mellon researchers.

“Fraud,” “disgruntlement,” “ideological challenges,” “moral outrage,” or discussion of moral concerns deemed “unrelated to work duties” are some of the possible signs that a worker poses a threat, according to US government training literature.

Of the 15 Cabinet-level departments such as energy, labor, and veterans affairs, at least nine had contracts as of late last year with suppliers such as Everfox and Dtex Systems that allowed for digitally monitoring of a portion of employees, according to public spending data. Everfox declined to comment.

Dtex’s Intercept software, which is used by multiple federal agencies, is one example of a newer class of programs that generate individual risk scores by analyzing anonymized metadata, such as which URLs workers are visiting and which files they’re opening and printing out on their work devices, according to the company. When an agency wants to identify and further investigate someone with a high score, two people have to sign off in some versions of its tool, according to the company. Dtex’s software doesn’t have to log keystrokes or scan the content of emails, calls, chats, or social media posts.

But that isn't how things work broadly across the government, where employees are warned explicitly in a recurring message when they boot up their devices that they have "no reasonable expectation of privacy" in their communications or in any data stored or transmitted through government networks. The question remains if and to what extent DOGE’s operatives are relying on existing monitoring programs to carry out Trump’s mission to rapidly eliminate federal workers that his administration views as unaligned with the president’s agenda or disloyal.

Rajan Koo, the chief technology officer of Dtex tells WIRED that he hopes the Trump administration will adjust the government’s approach to monitoring. Events such as widespread layoffs coupled with a reliance on what Koo described as intrusive surveillance tools can stir up an environment in which workers feel disgruntled, he says. “You can create a culture of reciprocal loyalty,” says Koo, or “the perfect breeding ground for insider threats.”

Already Overwhelmed

Sources with knowledge of the US government’s insider threat programs describe them as largely inefficient and labor intensive, requiring overstretched teams of analysts to manually pore through daily barrages of alerts that include many false positives. Multiple sources said that the systems are currently “overwhelmed.” Any effort by the Trump administration to extend the reach of such tools or widen their parameters—to more closely surveil for perceived signs of insubordination or disloyalty to partisan fealties, for instance—likely would result in a significant spike in false positives that would take considerable time to comb through, according to the people familiar with the work.

In an email last month seeking federal employees’ voluntary resignations, the Trump administration wrote that it wanted a “reliable, loyal, trustworthy” workforce. Attempts to use insider threat programs to enforce that vision could be met by a number of legal challenges.

US intelligence community analysts are required by law and directive to provide unbiased and objective work. That means avoiding cherry-picking information to deliberately alter judgements or falling prey to outside pressure, including from personal or political biases. These standards, even when not officially codified, are core to the professional ethics of any intelligence practitioner or law enforcement analyst conducting assessments of insider threats.

A 2018 national insider threat task force framework notes that federal programs should comply with “all applicable legal, privacy and civil liberties rights, and whistleblower protections.” Bradley Moss, an attorney representing US intelligence and law enforcement personnel, says that "disloyalty" to the Trump administration is “too vague” an excuse to terminate employees with civil service protections, adding that if "they're going to go through the statutory process, they need to demonstrate actual cause for termination."

A federal law enforcement source warns that monitoring could theoretically be used to gather political intelligence on federal employees, while the administration looks for more palatable reasons to terminate them later; similar to how law enforcement may obtain evidence that's inadmissible in the course of a criminal investigation, but then search for another evidentiary basis to file charges.

Joe Spielberger, senior legal counsel at the Project On Government Oversight, a nonpartisan group fighting alleged corruption, says that if Musk were serious about cutting government waste, he would be strengthening protections for people who report corruption and mismanagement. Any warrantless or mass surveillance of federal workers without transparent guidelines, he says, would represent a major concern.

“When you create this culture of fear and intimidation and have that chilling effect of making people even more fearful about calling out wrongdoing, it ensures that corruption goes unnoticed and unaddressed,” Spielberger says.

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fromdevcom · 6 days ago

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Splunk is a popular choice for log analytics. I am a java developer and really love to use splunk for production analytics. I have used splunk for more than 5 years and like its simplicity. This article is a list of best practices that I have learned from good splunk books and over my splunk usage in everyday software projects. Most of the learnings are common for any software architect however it becomes important to document them for new developers. This makes our life easier in maintaining the software after it goes live in production. Almost any software becomes difficult change after its live in production. There are some many things you may need to worry about. Using these best practices while implementing splunk in your software will help you in long run. First Thing First : Keep Splunk Logs Separate Keep splunk log separate from debug / error logs. Debug logs can be verbose. Define a separate splunk logging file in your application. This will also save you on licensing cost since you will not index unwanted logs. Use Standard Logging Framework Use existing logging framework to log to splunk log files. Do not invent your own logging framework. Just ensure to keep the log file separate for splunk. I recommend using Asynchronous logger to avoid any performance issues related to too much logging. Some popular choice of logging frameworks in Java are listed below Log4j SLF4J Apache commons logging Logback Log In KEY=VALUE Format Follow Key=Value format in splunk logging - Splunk understands Key=Value format, so your fields are automatically extracted by splunk. This format is also easier to read without splunk too. You may want to follow this for all other logs too. Use Shorter KEY Names Keep the key name short - preferable size should be less than 10 characters. Though you may have plenty of disc space. Its better to keep a tap on how much you log since it may create performance problems in long run. At the same time keep them understandable. Use Enums For Keys Define a Java Enum for SplunkKeys that has Description of each key and uses name field as the splunk key. public enum SplunkKey TXID("Transaction id"); /** * Describes the purpose of field to be splunked - not logged */ private String description; SplunkKey(String description) this.description = description; public String getDescription() return description; Create A Util Class To Log In Splunk Define a SplunkAudit class in project that can do all splunk logging using easy to call methods. public class SplunkAudit private Map values = new HashMap(); private static ThreadLocal auditLocal = new ThreadLocal(); public static SplunkAudit getInstance() SplunkAudit instance = auditLocal.get(); if (instance == null) instance = new SplunkAudit(); auditLocal.set(instance); return instance; private SplunkAudit() public void add(SplunkKey key, String message) values.put(key.name(), message); public void flush() StringBuilder fullMessage = new StringBuilder(); for (Map.Entry val : values.entrySet()) fullMessage.append(val.getKey()); fullMessage.append("="); fullMessage.append(val.getValue()); fullMessage.append(" "); //log the full message now //log.info(fullMessage); Collect the Splunk Parameters (a collection of key,value pairs ) in transaction and log them at the end of transaction to avoid multiple writes. Use Async Log Writer Its recommended to use async logger for splunk logs. Async logging will perform logging in a separate thread. Below are some options Async Logger Appender for Log4j Logback Async Appender Setup Alerts Setup Splunk queries as alerts - get automatic notifications. Index GC Logs in Splunk Index Java Garbage Collection Logs separately in splunk.

The format of GC log is different and it may get mixed with your regular application logs. Therefore its better to keep it separate. Here are some tips to do GC log analytics using splunk. Log These Fields Production logs are key to debug problems in your software. Having following fields may always be useful. This list is just the minimum fields, you may add more based on your application domain. ThreadName Most important field for Java application to debug and identify multithreading related problems. Ensure every thread has a logical name in your application. This way you can differentiate threads. For example transaction threads and background threads may have different prefix in thread name. Ensure to give a unique id for each thread. Its super easy to set thread names in java. One line statement will do it. Thread.currentThread().setName(“NameOfThread-UniqueId”) Thread Count Print count of threads at any point in time in JVM. Below one liner should provide you java active thread count at any point in JVM. java.lang.Thread.activeCount() Server IP Address Logging the server IP address become essential when we are running the application on multiple servers. Most enterprise application run cluster of servers. Its important to be able to differentiate errors specific to a special server. Its easy to get IP address of current server. Below line of code should work for most places (unless the server has multiple ip addresses) InetAddress.getLocalHost().getHostAddress() Version Version of software source from version control is important field. The software keeps changing for various reasons. You need to be able to identify exact version that is currently live on production. You can include your version control details in manifest file of deployable war / ear file. This can be easily done by maven. Once the information is available in your war/ear file, it can be read in application at runtime and logged in splunk log file. API Name Every application performs some tasks. It may be called API or something else. These are the key identifier of actions. Log unique API names for each action in your application. For example API=CREATE_USER API=DELETE_USER API=RESET_PASS Transaction ID Transaction id is a unique identifier of the transaction. This need not be your database transaction id. However you need a unique identifier to be able to trace one full transaction. User ID - Unique Identifier User identification is important to debug many use cases. You may not want to log user emails or sensitive info, however you can alway log a unique identifier that represents a user in your database. Success / Failure of Transaction Ensure you log success or failure of a transaction in the splunk. This will provide you a easy trend of failures in your system. Sample field would look like TXS=S (Success transaction) TXS=F (Failed transaction) Error Code Log error codes whenever there is a failure. Error codes can uniquely identify exact scenario therefore spend time defining them in your application. Best way is to define enum of ErrorCodes like below public enum ErrorCodes INVALID_EMAIL(1); private int id; ErrorCodes(int id) this.id = id; public int getId() return id; Elapsed Time - Time Taken to Finish Transaction Log the total time take by a transaction. It will help you easily identify the transactions that are slow. Elapsed Time of Each Major Component in Transaction If you transaction is made of multiple steps, you must also include time take for each step. This can narrow down your problem to the component that is performing slow. I hope you find these tip useful. Please share with us anything missed in this page.

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cybersecurityict · 6 days ago

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Cloud Performance Management Market Size, Share, Analysis, Forecast, and Growth Trends to 2032 Identify Emerging Technology Leaders

The Cloud Performance Management Market size was valued at USD 2.00 billion in 2023 and is expected to reach USD 8.25 billion by 2032, with a growing at a CAGR of 17.06% over the forecast period 2024-2032.

The Cloud Performance Management Market is experiencing significant momentum as enterprises across various sectors rapidly adopt cloud technologies to streamline operations and boost agility. As businesses transition from traditional infrastructure to cloud environments, managing and optimizing performance in real-time has become essential to ensure service reliability, end-user satisfaction, and operational efficiency. This has led to a surge in demand for advanced cloud performance tools that provide visibility, automation, and intelligent analytics.

Cloud Performance Management Market is witnessing a shift towards proactive monitoring and AI-driven insights, empowering organizations to predict and resolve performance bottlenecks before they impact operations. With the growing complexity of hybrid and multi-cloud ecosystems, vendors are focusing on innovative solutions that can seamlessly integrate across platforms and deliver unified performance metrics. This market is not just growing in size but also evolving in depth and capability.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/3773

Market Keyplayers:

VMware (vRealize Operations, CloudHealth)

HPE (Hewlett Packard Enterprise) (Cloud Optimizer, InfoSight)

Oracle (Oracle Management Cloud, Oracle Cloud Observability and Management Platform)

CA Technologies (Broadcom Inc.) (DX AIOps, App Synthetic Monitor)

Microsoft (Azure Monitor, System Center Operations Manager)

IBM (Instana, Turbonomic)

AppDynamics (Cisco Systems) (AppDynamics Business iQ, Application Performance Monitoring)

Riverbed Technology (SteelCentral, AppResponse)

BMC Software (Helix AIOps, TrueSight)

HR Cloud Inc. (Onboard, Workmates)

Dynatrace (Dynatrace Software Intelligence Platform, Synthetic Monitoring)

NamLabs Technologies Pvt Ltd (Site24x7, ManageEngine Applications Manager)

Citrix Systems Inc. (Citrix ADM, Citrix SD-WAN)

Commvault (Metallic SaaS, HyperScale X)

Lanteria LLC (Performance, HR Portal)

New Relic (New Relic One, APM)

Splunk (Splunk Observability Cloud, IT Service Intelligence)

Datadog (Cloud Monitoring, Log Management)

SolarWinds (Server & Application Monitor, Network Performance Monitor)

PagerDuty (Incident Response, Digital Operations Management)

Market Analysis

The market is shaped by the increasing digital transformation initiatives and the need for robust, scalable IT infrastructure. Enterprises are leveraging cloud performance management tools to ensure optimal resource utilization, enhance application delivery, and support uninterrupted business continuity. The market is highly competitive, with key players focusing on automation, predictive analytics, and real-time monitoring to differentiate their offerings. Moreover, regulatory compliance and data security remain critical factors driving product development and adoption.

Market Trends

Surge in demand for AI and machine learning-powered performance analytics

Growing adoption of multi-cloud and hybrid cloud strategies

Integration of observability platforms with performance management solutions

Emphasis on DevOps and continuous delivery environments

Rise of edge computing and its impact on cloud performance tools

Expansion of SaaS-based performance monitoring solutions

Increasing focus on cost optimization and ROI measurement

Market Scope

The scope of the Cloud Performance Management Market encompasses various industry verticals including IT and telecom, healthcare, BFSI, retail, manufacturing, and government. It covers a wide range of deployment models such as public, private, and hybrid clouds. Solutions include network performance monitoring, application performance management (APM), infrastructure monitoring, and workload automation. Enterprises of all sizes are integrating these solutions into their operations to enhance productivity, improve uptime, and gain actionable business intelligence.

Market Forecast

Over the forecast period, the market is expected to show sustained momentum driven by technological innovation, strategic partnerships, and the rising importance of seamless user experience. Advanced analytics, AI integration, and end-to-end observability will remain key differentiators among market leaders. As businesses scale their digital operations, the demand for agile, reliable, and intelligent cloud performance solutions will continue to expand across global markets.

Access Complete Report: https://www.snsinsider.com/reports/cloud-performance-management-market-3773

Conclusion

The Cloud Performance Management Market is not just a reflection of the growing reliance on cloud computing but a testament to the need for smarter, faster, and more adaptive IT environments. As organizations push toward innovation and resilience, cloud performance management stands as a critical pillar enabling sustainable growth. Forward-looking companies that prioritize performance, visibility, and agility in their cloud journey will be best positioned to lead in the digital age.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

#Cloud Performance Management Market #Cloud Performance Management Market Scope #Cloud Performance Management Market Share #Cloud Performance Management Market Trends

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generativeinai · 21 days ago

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The Ultimate Roadmap to AIOps Platform Development: Tools, Frameworks, and Best Practices for 2025

In the ever-evolving world of IT operations, AIOps (Artificial Intelligence for IT Operations) has moved from buzzword to business-critical necessity. As companies face increasing complexity, hybrid cloud environments, and demand for real-time decision-making, AIOps platform development has become the cornerstone of modern enterprise IT strategy.

If you're planning to build, upgrade, or optimize an AIOps platform in 2025, this comprehensive guide will walk you through the tools, frameworks, and best practices you must know to succeed.

What Is an AIOps Platform?

An AIOps platform leverages artificial intelligence, machine learning (ML), and big data analytics to automate IT operations—from anomaly detection and event correlation to root cause analysis, predictive maintenance, and incident resolution. The goal? Proactively manage, optimize, and automate IT operations to minimize downtime, enhance performance, and improve the overall user experience.

Key Functions of AIOps Platforms:

Data Ingestion and Integration

Real-Time Monitoring and Analytics

Intelligent Event Correlation

Predictive Insights and Forecasting

Automated Remediation and Workflows

Root Cause Analysis (RCA)

Why AIOps Platform Development Is Critical in 2025

Here’s why 2025 is a tipping point for AIOps adoption:

Explosion of IT Data: Gartner predicts that IT operations data will grow 3x by 2025.

Hybrid and Multi-Cloud Dominance: Enterprises now manage assets across public clouds, private clouds, and on-premises.

Demand for Instant Resolution: User expectations for zero downtime and faster support have skyrocketed.

Skill Shortages: IT teams are overwhelmed, making automation non-negotiable.

Security and Compliance Pressures: Faster anomaly detection is crucial for risk management.

Step-by-Step Roadmap to AIOps Platform Development

1. Define Your Objectives

Problem areas to address: Slow incident response? Infrastructure monitoring? Resource optimization?

KPIs: MTTR (Mean Time to Resolution), uptime percentage, operational costs, user satisfaction rates.

2. Data Strategy: Collection, Integration, and Normalization

Sources: Application logs, server metrics, network traffic, cloud APIs, IoT sensors.

Data Pipeline: Use ETL (Extract, Transform, Load) tools to clean and unify data.

Real-Time Ingestion: Implement streaming technologies like Apache Kafka, AWS Kinesis, or Azure Event Hubs.

3. Select Core AIOps Tools and Frameworks

We'll explore these in detail below.

4. Build Modular, Scalable Architecture

Microservices-based design enables better updates and feature rollouts.

API-First development ensures seamless integration with other enterprise systems.

5. Integrate AI/ML Models

Anomaly Detection: Isolation Forest, LSTM models, autoencoders.

Predictive Analytics: Time-series forecasting, regression models.

Root Cause Analysis: Causal inference models, graph neural networks.

6. Implement Intelligent Automation

Use RPA (Robotic Process Automation) combined with AI to enable self-healing systems.

Playbooks and Runbooks: Define automated scripts for known issues.

7. Deploy Monitoring and Feedback Mechanisms

Track performance using dashboards.

Continuously retrain models to adapt to new patterns.

Top Tools and Technologies for AIOps Platform Development (2025)

Data Ingestion and Processing

Apache Kafka

Fluentd

Elastic Stack (ELK/EFK)

Snowflake (for big data warehousing)

Monitoring and Observability

Prometheus + Grafana

Datadog

Dynatrace

Splunk ITSI

Machine Learning and AI Frameworks

TensorFlow

PyTorch

scikit-learn

H2O.ai (automated ML)

Event Management and Correlation

Moogsoft

BigPanda

ServiceNow ITOM

Automation and Orchestration

Ansible

Puppet

Chef

SaltStack

Cloud and Infrastructure Platforms

AWS CloudWatch and DevOps Tools

Google Cloud Operations Suite (formerly Stackdriver)

Azure Monitor and Azure DevOps

Best Practices for AIOps Platform Development

1. Start Small, Then Scale

Begin with a few critical systems before scaling to full-stack observability.

2. Embrace a Unified Data Strategy

Ensure that your AIOps platform ingests structured and unstructured data across all environments.

3. Prioritize Explainability

Build AI models that offer clear reasoning for decisions, not black-box results.

4. Incorporate Feedback Loops

AIOps platforms must learn continuously. Implement mechanisms for humans to approve, reject, or improve suggestions.

5. Ensure Robust Security and Compliance

Encrypt data in transit and at rest.

Implement access controls and audit trails.

Stay compliant with standards like GDPR, HIPAA, and CCPA.

6. Choose Cloud-Native and Open-Source Where Possible

Future-proof your system by building on open standards and avoiding vendor lock-in.

Key Trends Shaping AIOps in 2025

Edge AIOps: Extending monitoring and analytics to edge devices and remote locations.

AI-Enhanced DevSecOps: Tight integration between AIOps and security operations (SecOps).

Hyperautomation: Combining AIOps with enterprise-wide RPA and low-code platforms.

Composable IT: Building modular AIOps capabilities that can be assembled dynamically.

Federated Learning: Training models across multiple environments without moving sensitive data.

Challenges to Watch Out For

Data Silos: Incomplete data pipelines can cripple AIOps effectiveness.

Over-Automation: Relying too much on automation without human validation can lead to errors.

Skill Gaps: Building an AIOps platform requires expertise in AI, data engineering, IT operations, and cloud architectures.

Invest in cross-functional teams and continuous training to overcome these hurdles.

Conclusion: Building the Future with AIOps

In 2025, the enterprises that invest in robust AIOps platform development will not just survive—they will thrive. By integrating the right tools, frameworks, and best practices, businesses can unlock proactive incident management, faster innovation cycles, and superior user experiences.

AIOps isn’t just about reducing tickets—it’s about creating a resilient, self-optimizing IT ecosystem that powers future growth.

#ai #artificial intelligence

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pallavinovel · 1 month ago

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Site Reliability Engineering: Tools, Techniques & Responsibilities

Introduction to Site Reliability Engineering (SRE)

Site Reliability Engineering (SRE) is a modern approach to managing large-scale systems by applying software engineering principles to IT operations. Originally developed by Google, SRE focuses on improving system reliability, scalability, and performance through automation and data-driven decision-making.

At its core, SRE bridges the gap between development and operations teams. Rather than relying solely on manual interventions, SRE encourages building robust systems with self-healing capabilities. SRE teams are responsible for maintaining uptime, monitoring system health, automating repetitive tasks, and handling incident response.

A key concept in SRETraining is the use of Service Level Objectives (SLOs) and Error Budgets. These help organizations balance the need for innovation and reliability by defining acceptable levels of failure. SRE also emphasizes observability—the ability to understand what's happening inside a system using metrics, logs, and traces.

By embracing automation, continuous improvement, and a blameless culture, SRE enables teams to reduce downtime, scale efficiently, and deliver high-quality digital services. As businesses increasingly depend on digital infrastructure, the demand for SRE practices and professionals continues to grow. Whether you're in development, operations, or IT leadership, understanding SRE can greatly enhance your approach to building resilient systems.

Tools Commonly Used in SRE

Monitoring & Observability

Prometheus – Open-source monitoring system with time-series data and alerting.

Grafana – Visualization and dashboard tool, often used with Prometheus.

Datadog – Cloud-based monitoring platform for infrastructure, applications, and logs.

New Relic – Full-stack observability with APM and performance monitoring.

ELK Stack (Elasticsearch, Logstash, Kibana) – Log analysis and visualization.

Incident Management & Alerting

PagerDuty – Real-time incident alerting, on-call scheduling, and response automation.

Opsgenie – Alerting and incident response tool integrated with monitoring systems.

VictorOps (now Splunk On-Call) – Streamlines incident resolution with automated workflows.

Automation & Configuration Management

Ansible – Simple automation tool for configuration and deployment.

Terraform – Infrastructure as Code (IaC) for provisioning cloud resources.

Chef / Puppet – Configuration management tools for system automation.

CI/CD Pipelines

Jenkins – Widely used automation server for building, testing, and deploying code.

GitLab CI/CD – Integrated CI/CD pipelines with source control.

Spinnaker – Multi-cloud continuous delivery platform.

Cloud & Container Orchestration

Kubernetes – Container orchestration for scaling and managing applications.

Docker – Containerization tool for packaging applications.

AWS CloudWatch / GCP Stackdriver / Azure Monitor – Native cloud monitoring tools.

Best Practices in Site Reliability Engineering (SRE)

Site Reliability Engineering (SRE) promotes a disciplined approach to building and operating reliable systems. Adopting best practices in SRE helps organizations reduce downtime, manage complexity, and scale efficiently.

A foundational practice is defining Service Level Indicators (SLIs) and Service Level Objectives (SLOs) to measure and set targets for performance and availability. These metrics ensure teams understand what reliability means for users and how to prioritize improvements.

Error budgets are another critical concept, allowing controlled failure to balance innovation with stability. If a system exceeds its error budget, development slows to focus on reliability enhancements.

SRE also emphasizes automation. Automating repetitive tasks like deployments, monitoring setups, and incident responses reduces human error and improves speed. Minimizing toil—manual, repetitive work that doesn’t add long-term value—is essential for team efficiency.

Observability is key. Systems should be designed with visibility in mind using logs, metrics, and traces to quickly detect and resolve issues.

Finally, a blameless post mortem culture fosters continuous learning. After incidents, teams analyze what went wrong without pointing fingers, focusing instead on preventing future issues.

Together, these practices create a culture of reliability, efficiency, and resilience—core goals of any successful SRE team.

Top 5 Responsibilities of a Site Reliability Engineer (SRE)

Maintain System Reliability and Uptime

Ensure services are available, performant, and meet defined availability targets.

Automate Operational Tasks

Build tools and scripts to automate deployments, monitoring, and incident response.

Monitor and Improve System Health

Set up observability tools (metrics, logs, traces) to detect and fix issues proactively.

Incident Management and Root Cause Analysis

Respond to incidents, minimize downtime, and conduct postmortems to learn from failures.

Define and Track SLOs/SLIs

Establish reliability goals and measure system performance against them.

Know More: Site Reliability Engineering (SRE) Foundation Training and Certification.

#sre certification #sre courses #sre training

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differenttimemachinecrusade · 2 months ago

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Cloud Security Market Developments: Industry Insights and Growth Forecast 2032

Cloud Security Market was valued at USD 36.9 billion in 2023 and is expected to reach USD 112.4 Billion by 2032, growing at a CAGR of 13.20% from 2024-2032.

Cloud Security Market is experiencing unprecedented growth as businesses worldwide move their operations to the cloud. With the rise in cyber threats and data breaches, organizations are prioritizing security solutions to protect sensitive information. The demand for advanced security frameworks and compliance-driven solutions is accelerating market expansion.

Cloud Security Market continues to evolve as enterprises adopt multi-cloud and hybrid cloud environments. Companies are investing in AI-driven threat detection, zero-trust security models, and encryption technologies to safeguard data. As cyber risks grow, cloud security remains a top priority for businesses, governments, and cloud service providers alike.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/3796

Market Keyplayers:

Amazon Web Services (AWS) - AWS Shield

Microsoft - Microsoft Defender for Cloud

Google Cloud Platform - Google Cloud Armor

IBM - IBM Cloud Security

Palo Alto Networks - Prisma Cloud

Cisco - Cisco Cloudlock

Check Point Software Technologies - CloudGuard

Fortinet - FortiGate Cloud

McAfee - McAfee MVISION Cloud

NortonLifeLock - Norton Cloud Backup

Zscaler - Zscaler Internet Access

CrowdStrike - CrowdStrike Falcon

Cloudflare - Cloudflare Security Solutions

Splunk - Splunk Cloud

Proofpoint - Proofpoint Email Protection

Trend Micro - Trend Micro Cloud One

SonicWall - SonicWall Cloud App Security

CyberArk - CyberArk Cloud Entitlement Manager

Barracuda Networks - Barracuda Cloud Security Guardian

Qualys - Qualys Cloud Platform

Market Trends Driving Growth

1. Rise of Zero-Trust Security Models

Organizations are implementing zero-trust frameworks to ensure strict authentication and access control, reducing the risk of unauthorized access.

2. AI and Machine Learning in Threat Detection

Cloud security providers are integrating AI-driven analytics to detect, predict, and prevent cyber threats in real-time.

3. Compliance and Regulatory Requirements

Stringent data protection laws, such as GDPR and CCPA, are pushing businesses to adopt cloud security solutions for compliance.

4. Increasing Multi-Cloud Adoption

Companies are using multiple cloud providers, necessitating advanced security solutions to manage risks across different cloud environments.

Enquiry of This Report: https://www.snsinsider.com/enquiry/3796

Market Segmentation:

By Component

Solution

Cloud Access Security Broker (CASB)

Cloud Detection and Response (CDR)

Cloud Security Posture Management (CSPM)

Cloud Infrastructure Entitlement Management (CIEM)

Cloud Workload Protection Platform (CWPP)

Services

Professional Services

Managed Services

By Deployment

Private

Hybrid

Public

By Organization Size

Large Enterprises

Small & Medium Enterprises

By End - Use

BFSI

Retail & E-commerce

IT & Telecom

Healthcare

Manufacturing

Government

Aerospace & Defense

Energy & Utilities

Transportation & Logistics

Market Analysis and Current Landscape

Rising Cybersecurity Threats: Growing cyberattacks, ransomware incidents, and data breaches are driving the need for robust cloud security solutions.

Adoption of Cloud-Based Applications: As enterprises migrate to cloud platforms like AWS, Azure, and Google Cloud, demand for security services is increasing.

Expansion of IoT and Edge Computing: The rise in connected devices is creating new vulnerabilities, requiring enhanced cloud security measures.

Government Investments in Cybersecurity: Public sector organizations are strengthening their cloud security frameworks to protect critical infrastructure and citizen data.

Despite this rapid growth, challenges such as complex security architectures, lack of skilled professionals, and evolving attack strategies persist. However, innovations in AI-driven security solutions and automated threat management are helping businesses address these concerns.

Future Prospects: What Lies Ahead?

1. Growth in Security-as-a-Service (SECaaS)

Cloud-based security services will become the norm, offering scalable and cost-effective protection for businesses of all sizes.

2. Advanced Threat Intelligence Solutions

Organizations will increasingly rely on AI-powered threat intelligence to stay ahead of cybercriminals and mitigate risks proactively.

3. Expansion of Quantum-Safe Security

With the advancement of quantum computing, encryption technologies will evolve to ensure data remains secure against future cyber threats.

4. Integration of Cloud Security with DevSecOps

Security will be embedded into cloud application development, ensuring vulnerabilities are addressed at every stage of the software lifecycle.

Access Complete Report: https://www.snsinsider.com/reports/cloud-security-market-3796

Conclusion

The Cloud Security Market is poised for exponential growth as digital transformation accelerates. Companies must invest in cutting-edge security frameworks to protect their data, applications, and infrastructure. As cyber threats become more sophisticated, cloud security will continue to be a critical pillar of business resilience, driving innovation and shaping the future of cybersecurity.

About Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

#Cloud Security Market #Cloud Security Market Scope #Cloud Security Market Growth #Cloud Security Market Trends

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spermarket · 2 months ago

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Behavior Analytics Market Analysis, Demand, Trends, Business Scope and Future Competition 2034: SPER Market Research

Behavior analytics involves using technology like machine learning, artificial intelligence, and big data to study human behavior. It helps find patterns and trends to improve decision-making, optimize processes, and enhance security. The industry focuses on collecting and analyzing consumer data to understand what drives customers. Digital product users, like those of apps or websites, provide data on their behaviors, allowing companies to assess customer engagement and enhance their digital services in the future.

According to SPER market research, ‘Global Behavior Analytics Market Size- By Deployment Model, By Component, By End-User - Regional Outlook, Competitive Strategies and Segment Forecast to 2034’ state that the Global Behavior Analytics Market is predicted to reach 33.04 billion by 2034 with a CAGR of 19.78%.

Drivers:

The industry is driven by the rise of digital platforms and the rapid increase of data from online interactions. Artificial intelligence (AI) and machine learning (ML) play key roles in developing behavioral analytics solutions.

Major sectors pushing for these solutions include e-commerce, finance, healthcare, cybersecurity, and telecommunications. Analytics will help e-commerce and finance improve marketing, pricing, and customer retention.

The growth of connected devices and data from IoT sensors is creating new chances for behavioral analytics. Integrating these solutions with big data platforms allows organizations to analyze various data sources for insights.

Request a Free Sample Report: https://www.sperresearch.com/report-store/behavior-analytics-market?sample=1

Restraints:

Data privacy concerns are a major limitation for the behavior analytics market. These solutions involve collecting and analyzing large amounts of often sensitive user data. Continuous tracking of user patterns can reveal detailed insights into individual habits and interactions, raising the risk of misuse or unauthorized access to private information. Strict data confidentiality regulations, like GDPR in Europe and CCPA in the US, complicate the gathering and use of personal data. Organizations need to ensure compliance, which can be costly, leading to reluctance in adopting behavior analytics fully. These concerns hinder market growth as organizations weigh the benefits against privacy risks.

North America is expected to have the largest market share during the forecast period due to many advanced technology and cybersecurity firms that adopt innovative solutions early. There is a strong focus on cybersecurity in both public and private sectors, increasing the demand for behavior analytics to protect against complex threats. The established IT infrastructure and a large number of enterprises recognize the need for behavior analytics for security, compliance, and efficiency. Strict data protection regulations encourage organizations to use behavior analytics to meet compliance. Companies also use behavior analytics in workforce management to understand employee behavior, enhance productivity, and improve workplace satisfaction. Some of the key market players are IBM Corporation, Splunk Inc, HP Enterprises, Dtex Systems, LogRhythm, Rapid7 Balabit Corp, Bay Dynamics, and others.

For More Information, refer to below link: –

Behavior Analytics Market Growth

Related Reports:

IoT Middleware Market Growth, Size, Trends Analysis- By Platform, By Enterprise Size, By Industry Vertical - Regional Outlook, Competitive Strategies and Segment Forecast to 2034

Global Data Center Networking Market Growth, Size, Trends Analysis- By Component, By End Use - Regional Outlook, Competitive Strategies and Segment Forecast to 2034

LinkedIn | Instagram | Facebook | Twitter

Sara Lopes, Business Consultant — USA

SPER Market Research

[email protected]

+1–347–460–2899

#Behavior Analytics Market #Behavior Analytics Market Share #Behavior Analytics Market Size #Behavior Analytics Market Revenue #Behavior Analytics Market Demand #Behavior Analytics Market Analysis #Behavior Analytics Market Segmentation #Behavior Analytics Market Future Outlook #Behavior Analytics Market Scope #Behavior Analytics Market Challenges #Behavior Analytics Market Competition #Behavior Analytics Market forecast

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kodspirit · 2 months ago

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How DevOps Improves Your Development Process | Key Benefits

H1 How Can DevOps Improve Your Development Process? 🚀

In today's fast-paced software development world, delivering high-quality applications quickly and efficiently is a priority. However, traditional development processes often face bottlenecks, delays, and inefficiencies due to poor collaboration between development and operations teams. This is where DevOps comes into play. DevOps is a culture, methodology, and set of practices that bridge the gap between software development (Dev) and IT operations (Ops) to improve collaboration, efficiency, and deployment speed.

H2 1. Faster Development and Deployment

One of the biggest advantages of DevOps is continuous integration (CI) and continuous deployment (CD). These practices allow developers to merge code changes frequently and deploy them automatically in a controlled manner.

🔹 Continuous Integration (CI): Ensures that new code is regularly integrated, reducing integration issues and allowing early detection of bugs. 🔹 Continuous Deployment (CD): Automates the release process, ensuring that updates reach production faster and more efficiently.

By implementing CI/CD pipelines, companies can release new features, bug fixes, and updates multiple times a day instead of waiting weeks or months.

H2 2. Improved Collaboration Between Teams

Traditional software development often leads to siloed teams, where developers focus only on writing code while operations teams handle deployment and infrastructure. DevOps breaks these silos by promoting collaboration and communication between development, operations, and QA teams.

🔹 Shared responsibility ensures that all teams work together from planning to deployment. 🔹 Improved transparency reduces misunderstandings and accelerates decision-making. 🔹 Faster feedback loops help identify issues early and improve software quality.

This cultural shift fosters a more productive and innovative environment.

H2 3. Enhanced Software Quality and Stability

With DevOps, software quality improves significantly through automated testing, monitoring, and continuous feedback loops.

🔹 Automated Testing: Ensures that code changes don’t introduce new bugs or break existing functionality. 🔹 Infrastructure as Code (IaC): Helps create consistent and error-free infrastructure configurations. 🔹 Real-time Monitoring: Tools like Prometheus, Grafana, and ELK Stack provide insights into application performance and potential issues.

By catching issues early, DevOps reduces downtime, system failures, and post-release bugs, resulting in more stable and reliable software.

H2 4. Increased Efficiency with Automation

Manual processes slow down development and increase the risk of human errors. DevOps leverages automation at various stages of the software development lifecycle to improve efficiency.

🔹 Code Deployment Automation: Tools like Jenkins, GitHub Actions, and GitLab CI/CD help automate build, test, and deployment processes. 🔹 Configuration Management: Tools like Ansible, Chef, and Puppet ensure consistent infrastructure configurations. 🔹 Cloud Automation: Platforms like AWS, Azure, and Google Cloud provide automated scaling and infrastructure management.

By automating repetitive tasks, developers can focus more on writing code and innovating, rather than managing infrastructure manually.

H2 5. Faster Issue Resolution & Better Incident Management

Traditional development processes often lead to long debugging sessions when issues arise in production. DevOps integrates proactive monitoring, logging, and alerting to help teams detect, diagnose, and resolve issues quickly.

🔹 Monitoring & Logging: Tools like Splunk, Datadog, and ELK Stack help track application performance in real time. 🔹 Incident Management: Platforms like PagerDuty and OpsGenie ensure rapid response to system failures. 🔹 Rollback & Recovery: DevOps enables quick rollbacks in case of critical failures, minimizing downtime.

This leads to improved customer experience, reduced downtime, and better overall application performance.

H2 6. Cost Optimization and Resource Efficiency

By automating deployment, monitoring, and infrastructure management, DevOps helps reduce operational costs and resource wastage.

🔹 Cloud Optimization: DevOps leverages cloud-native solutions to scale applications based on demand, reducing unnecessary expenses. 🔹 Infrastructure as Code (IaC): Eliminates the need for manual server provisioning, reducing IT overhead. 🔹 Efficient Resource Utilization: CI/CD pipelines reduce the time spent on debugging and manual deployments.

Companies that adopt DevOps experience lower costs related to infrastructure, maintenance, and operational inefficiencies.

H2 7. Security Integration with DevSecOps

Security is often an afterthought in traditional development, but DevOps promotes DevSecOps, where security is integrated into every stage of development.

🔹 Automated Security Testing: Tools like SonarQube and Snyk help identify vulnerabilities early. 🔹 Container Security: Kubernetes and Docker provide built-in security features for containerized applications. 🔹 Compliance & Access Controls: DevOps enforces strict access control policies to protect sensitive data.

By embedding security into the DevOps pipeline, organizations can detect and mitigate security threats before deployment. We provide industry-driven master’s programs designed to help you excel in high-demand fields. By adopting DevOps, you can reduce deployment failures, enhance productivity, and deliver high-quality applications faster.

Ready to improve your software development process? Start your DevOps journey today! 🚀

devops

#dev

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digitalmore · 2 months ago

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#IFTTT #DigitalMore.co

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learning-code-ficusoft · 3 months ago

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Automation in DevOps (DevSecOps): Integrating Security into the Pipeline

In modern DevOps practices, security can no longer be an afterthought — it needs to be embedded throughout the software development lifecycle (SDLC). This approach, known as DevSecOps, integrates security automation into DevOps workflows to ensure applications remain secure without slowing down development.

Why Security Automation?

Traditional security models relied on manual code reviews and vulnerability assessments at the end of the development cycle, often leading to bottlenecks and delayed releases. Security automation addresses these issues by: ✔️ Detecting vulnerabilities early in the CI/CD pipeline ✔️ Reducing manual intervention and human error ✔️ Ensuring continuous compliance with industry regulations ✔️ Improving incident response time

Key Areas of Security Automation in DevOps

1. Automated Code Security (Static & Dynamic Analysis)

Static Application Security Testing (SAST): Scans source code for vulnerabilities before deployment (e.g., SonarQube, Checkmarx).

Dynamic Application Security Testing (DAST): Identifies security flaws in running applications (e.g., OWASP ZAP, Burp Suite).

Software Composition Analysis (SCA): Detects vulnerabilities in third-party dependencies (e.g., Snyk, WhiteSource).

🔹 Example: Running SAST scans automatically in a Jenkins pipeline to detect insecure coding practices before merging code.

2. Secrets Management & Access Control

Automating the detection and handling of hardcoded secrets, API keys, and credentials using tools like HashiCorp Vault, AWS Secrets Manager, and CyberArk.

Implementing least privilege access via automated IAM policies to ensure only authorized users and services can access sensitive data.

🔹 Example: Using HashiCorp Vault to generate and revoke temporary credentials dynamically instead of hardcoding them.

3. Automated Compliance & Policy Enforcement

Infrastructure as Code (IaC) security scans using Checkov, OPA (Open Policy Agent), or Terraform Sentinel ensure that cloud configurations follow security best practices.

Automated audits and reporting help maintain compliance with GDPR, HIPAA, SOC 2, and ISO 27001 standards.

🔹 Example: Using Checkov to scan Terraform code for misconfigurations before provisioning cloud resources.

4. Container & Kubernetes Security

Scanning container images for vulnerabilities using Trivy, Aqua Security, or Anchore before pushing them to a registry.

Implementing Kubernetes security policies (e.g., Pod Security Policies, Kyverno, or Gatekeeper) to enforce security rules.

🔹 Example: Using Trivy in a CI/CD pipeline to scan Docker images before deployment to Kubernetes.

5. Continuous Security Monitoring & Threat Detection

Implementing SIEM (Security Information and Event Management) tools like Splunk, ELK Stack, or AWS Security Hub for real-time security event detection.

Using Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) (e.g., Snort, Suricata) to detect and respond to security threats.

AI-driven anomaly detection via Amazon GuardDuty, Microsoft Defender for Cloud, or Google Chronicle.

🔹 Example: Configuring AWS Security Hub to automatically detect and alert on misconfigurations in an AWS environment.

6. Automated Incident Response & Remediation

Using SOAR (Security Orchestration, Automation, and Response) platforms like Splunk SOAR or Palo Alto Cortex XSOAR to automate security incident triage and response.

Creating automated playbooks for threat mitigation, such as isolating compromised containers or blocking suspicious IPs.

🔹 Example: Automating AWS Lambda functions to quarantine an EC2 instance when an anomaly is detected.

Bringing It All Together: A DevSecOps Pipeline Example

1️⃣ Code Commit: Developers push code to a Git repository. 2️⃣ Static Code Analysis: SAST tools scan for vulnerabilities. 3️⃣ Dependency Scanning: SCA tools check third-party libraries. 4️⃣ Secrets Detection: Git hooks or automated scanners look for hardcoded secrets. 5️⃣ Container Security: Images are scanned before being pushed to a container registry. 6️⃣ Infrastructure as Code Scanning: Terraform or Kubernetes configurations are checked. 7️⃣ Automated Security Testing: DAST and penetration tests run in staging. 8️⃣ Compliance Checks: Policies are enforced before deployment. 9️⃣ Real-time Monitoring: Logs and security events are analyzed for threats. 🔟 Incident Response: Automated workflows handle detected threats.

Final Thoughts

Security automation in DevOps is critical for ensuring that security does not slow down development. By integrating automated security testing, policy enforcement, and monitoring, teams can build resilient, compliant, and secure applications without sacrificing speed.

WEBSITE: https://www.ficusoft.in/devops-training-in-chennai/

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weepingbarbariansweets · 3 months ago

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North America AIOps Platform Market Statistics, Trends, Size, Share, Regional Analysis by Key Players

The North America AIOps platform market is expected to grow from US$ 1,238.17 million in 2021 to US$ 8,810.61 million by 2028; it is estimated to grow at a CAGR of 32.4% from 2021 to 2028.

In dynamic, elastic contexts, traditional ways of controlling IT complexity—offline, manual activities requiring human intervention—do not even operate. It is no longer possible to track and manage this complexity by manual, human monitoring. For years, ITOps has exceeded human scale, and the situation is only getting worse. Organizations want their critical applications to be available and operate well.

📚 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅 𝐂𝐨𝐩𝐲@ https://www.businessmarketinsights.com/sample/BMIRE00025397

They are also seeking a highly automated setup, that makes it easier to make clear decisions about new product development by leveraging classified data. Hence, the introduction of the AIOps platform has catered to these demands. AIOps platforms consolidate all applications and infrastructure operations into a single management portal with a dashboard view. Studies claim that AIOps can automatically perform 90% of the operative tasks, and human interaction is required only for 10% of tasks. Hence, the growing digital data, coupled with premium support offered by the AIOps platform, is driving the AIOps platform market. AIOps are beneficial for any company wishing to modernize to a digital platform that incorporates cutting-edge automation, analytics, artificial intelligence, and machine learning technologies. AIOps systems decrease the flood of warnings and can perform everyday tasks such as backups, server restarts, and low-risk maintenance. AIOps are expected to become widely used and mainstream soon, which will drive the market in the coming years.

📚𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨��𝐭 𝐋𝐢𝐧𝐤 @ https://www.businessmarketinsights.com/reports/north-america-aiops-platform-market

𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬

AppDynamics

BMC Software, Inc.

Broadcom Inc.

Dynatrace LLC

HCL Technologies

IBM Corporation

Micro Focus

Moogsoft Inc.

Resolve Systems, LLC

Splunk, Inc.

The Ascendance of AIOps: Navigating IT Complexity in the Era of Elasticity

The digital landscape has undergone a seismic shift, characterized by dynamic, elastic IT environments that defy traditional management paradigms. Gone are the days of static infrastructure and predictable workloads. Today's organizations operate in a realm of constant change, where applications scale instantaneously, and infrastructure resources fluctuate based on real-time demand. This dynamism, while offering immense agility and scalability, has introduced unprecedented levels of complexity, rendering traditional IT Operations (ITOps) methodologies obsolete.

The core challenge lies in the sheer volume and velocity of data generated within these elastic environments. Manual monitoring, offline analysis, and human intervention, once the cornerstones of ITOps, are simply inadequate to keep pace with the relentless flow of information. The scale of modern IT infrastructure has long surpassed human capacity, and the situation continues to escalate. Organizations are grappling with the imperative to ensure the availability and performance of critical applications while simultaneously striving for a highly automated setup that empowers data-driven decision-making for new product development.

This confluence of demands has catalyzed the emergence of Artificial Intelligence for IT Operations (AIOps) platforms. These platforms represent a paradigm shift in ITOps, leveraging the power of artificial intelligence (AI) and machine learning (ML) to automate and optimize IT operations in real-time. By consolidating data from diverse applications and infrastructure components into a unified management portal with a comprehensive dashboard view, AIOps provides a holistic understanding of the IT environment.

The transformative potential of AIOps is underscored by studies suggesting that these platforms can automate up to 90% of operational tasks, reducing the reliance on human intervention to a mere 10%. This dramatic reduction in manual effort frees up valuable IT resources to focus on strategic initiatives, such as innovation and business growth. The exponential growth of digital data, coupled with the premium support offered by AIOps platforms, is propelling the AIOps market forward, signaling a fundamental shift in how organizations manage their IT infrastructure.

Market Dynamics and Future Outlook:

The North America AIOps platform market is expected to continue its strong growth trajectory, driven by the increasing adoption of cloud computing, the growing complexity of IT infrastructure, and the need for automated, intelligent operations. The market is characterized by intense competition and continuous innovation, with vendors constantly enhancing their product offerings and expanding their capabilities. The future of AIOps is bright, with the potential to transform IT operations and enable organizations to achieve greater efficiency, agility, and resilience.

𝐀𝐛𝐨𝐮𝐭 𝐔𝐬: Business Market Insights is a market research platform that provides subscription service for industry and company reports. Our research team has extensive professional expertise in domains such as Electronics & Semiconductor; Aerospace & Defense; Automotive & Transportation; Energy & Power; Healthcare; Manufacturing & Construction; Food & Beverages; Chemicals & Materials; and Technology, Media, & Telecommunications

𝐀𝐮𝐭𝐡𝐨𝐫’𝐬 𝐁𝐢𝐨: 𝐬𝐭𝐞𝐩𝐡𝐞𝐧 𝐣𝐨𝐡𝐧𝐬𝐨𝐧 𝐒𝐞𝐧𝐢𝐨𝐫 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐄𝐱𝐩𝐞𝐫𝐭

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valiantwitchfestival · 3 months ago

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North America AIOps Platform Market Size, Key Players, Investment Opportunities, Top Regions, Growth and Forecast by 2028

The North America AIOps platform market is expected to grow from US$ 1,238.17 million in 2021 to US$ 8,810.61 million by 2028; it is estimated to grow at a CAGR of 32.4% from 2021 to 2028.

📚 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅 𝐂𝐨𝐩𝐲@ https://www.businessmarketinsights.com/sample/BMIRE00025397

📚𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭 𝐋𝐢𝐧𝐤 @ https://www.businessmarketinsights.com/reports/north-america-aiops-platform-market

𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬

AppDynamics

BMC Software, Inc.

Broadcom Inc.

Dynatrace LLC

HCL Technologies

IBM Corporation

Micro Focus

Moogsoft Inc.

Resolve Systems, LLC

Splunk, Inc.

Organization Size:

Large Enterprises: Large enterprises, with their complex IT environments and substantial budgets, were the primary adopters of AIOps platforms in 2020.

SMEs (Small and Medium-sized Enterprises): The SMEs segment is projected to experience the fastest growth during the forecast period. The increasing affordability and accessibility of cloud-based AIOps solutions are enabling SMEs to leverage the benefits of these platforms.

𝐀𝐮𝐭𝐡𝐨𝐫’𝐬 𝐁𝐢𝐨: 𝐕𝐚𝐢𝐛𝐡𝐚𝐯 𝐆𝐡𝐚𝐫𝐠𝐞 𝐒𝐞𝐧𝐢𝐨𝐫 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐄𝐱𝐩𝐞𝐫𝐭

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devotedlywingedtheorist · 3 months ago

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North America AIOps Platform Market Historical Analysis, Opportunities, Latest Innovations, Top Players Forecast 2028

The North America AIOps platform market is expected to grow from US$ 1,238.17 million in 2021 to US$ 8,810.61 million by 2028; it is estimated to grow at a CAGR of 32.4% from 2021 to 2028.

📚 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅 𝐂𝐨𝐩𝐲@ https://www.businessmarketinsights.com/sample/BMIRE00025397

📚𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭 𝐋𝐢𝐧𝐤 @ https://www.businessmarketinsights.com/reports/north-america-aiops-platform-market

𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐨𝐟 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬

AppDynamics

BMC Software, Inc.

Broadcom Inc.

Dynatrace LLC

HCL Technologies

IBM Corporation

Micro Focus

Moogsoft Inc.

Resolve Systems, LLC

Splunk, Inc.

Regional Distinctions and Considerations:

North America exhibits significant regional variations that impact the adoption and implementation of AIOps platforms. Here's a breakdown of key considerations:

Silicon Valley and the West Coast:

Characteristics: A hub of technological innovation, with a high concentration of tech companies and early adopters of AIOps.

Opportunities: Strong demand for cutting-edge AIOps solutions, including AI-driven automation and predictive analytics.

Considerations: Highly competitive market, requiring continuous innovation and differentiation.

The Northeast (New York, Boston, etc.):

Characteristics: A major financial and healthcare hub, with stringent regulatory requirements.

Opportunities: Strong demand for AIOps solutions that enhance security, compliance, and risk management.

Considerations: High emphasis on data privacy and security, requiring robust compliance measures.

𝐀𝐮𝐭𝐡𝐨𝐫’𝐬 𝐁𝐢𝐨: 𝐒𝐡𝐫𝐞𝐲𝐚 𝐏𝐚𝐰𝐚𝐫 𝐒𝐞𝐧𝐢𝐨𝐫 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐄𝐱𝐩𝐞𝐫𝐭

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