TrendMicro Deep Security is a comprehensive security platform designed to protect virtual, cloud, and physical servers from a wide range of threats. It provides advanced protection features such as intrusion detection and prevention (IDS/IPS), anti-malware, web reputation, firewall, and application control. Deep Security is particularly useful for securing cloud environments, including hybrid and multi-cloud infrastructures, as well as virtualized environments. It enables organizations to achieve continuous security without sacrificing performance, thanks to its lightweight agent-based approach.

Use cases for TrendMicro Deep Security include cloud security, where it ensures the protection of workloads across public and private cloud environments; endpoint security, providing real-time protection for physical and virtual servers; and compliance, where it helps meet regulatory requirements by securing sensitive data and applications. It is widely used in industries such as finance, healthcare, and retail to enhance security posture, prevent breaches, and safeguard critical infrastructure from cyber threats.

What is Trend Micro Deep Security?

Trend Micro Deep Security is a cloud workload protection platform that offers multi-layered security to protect data centers, cloud environments, and containers. It is widely used to secure servers, virtual machines, and hybrid cloud infrastructures, providing a unified platform for monitoring, detecting, and preventing threats in real-time.

Key Characteristics of Trend Micro Deep Security:

• Comprehensive Protection: Offers multiple layers of security, including anti-malware, firewall, and intrusion prevention.
• Cloud-Native: Integrates seamlessly with public cloud providers like AWS, Azure, and Google Cloud.
• Scalability: Designed to protect workloads in both small-scale environments and large enterprises.
• Automation: Automates security processes to improve efficiency and reduce operational overhead.

Top 10 Use Cases of Trend Micro Deep Security

1. Cloud Workload Protection
   • Secures workloads running on AWS, Azure, and Google Cloud with built-in integrations.
2. Virtual Environment Security
   • Provides advanced protection for VMware, Microsoft Hyper-V, and other virtualized environments.
3. Container Security
   • Monitors and protects containerized applications across platforms like Kubernetes and Docker.
4. Intrusion Detection and Prevention
   • Identifies and blocks unauthorized access attempts and exploits.
5. Anti-Malware Protection
   • Detects and removes malware from servers and workloads in real-time.
6. Compliance Management
   • Helps organizations meet compliance requirements like GDPR, HIPAA, and PCI-DSS through log inspection and integrity monitoring.
7. Application Control
   • Restricts unauthorized applications from running, ensuring only approved software operates on workloads.
8. File Integrity Monitoring
   • Monitors critical system files for unauthorized changes to detect potential tampering or attacks.
9. Firewall Protection
   • Provides a host-based firewall to prevent unauthorized network access to workloads.
10. Log Inspection
    • Collects and analyzes logs for suspicious activity to enhance threat detection and compliance.

Features of Trend Micro Deep Security

1. Intrusion Detection and Prevention (IDS/IPS) – Protects workloads from vulnerabilities and exploits.
2. Anti-Malware – Offers advanced malware detection and removal capabilities.
3. File Integrity Monitoring – Tracks changes to critical files for signs of tampering.
4. Web Application Protection – Secures web applications against common attacks like SQL injection and cross-site scripting.
5. Host-Based Firewall – Filters traffic to and from workloads, reducing the attack surface.
6. Log Inspection – Monitors and analyzes logs for suspicious activity and compliance reporting.
7. Cloud Integrations – Works seamlessly with AWS, Azure, Google Cloud, and VMware environments.
8. Centralized Management – Provides a unified dashboard for managing and monitoring security across environments.
9. Scalable Deployment – Protects workloads in on-premises, cloud, and hybrid environments.
10. Automation – Automates security processes, including patching and policy updates.

How Trend Micro Deep Security Works and Architecture

1. Agent-Based Protection

Trend Micro Deep Security uses lightweight agents installed on workloads to provide real-time protection. These agents monitor traffic, inspect logs, and enforce security policies.

2. Centralized Management Console

A centralized console allows administrators to configure, manage, and monitor security policies across all workloads.

3. Integration with Cloud Platforms

Deep Security integrates natively with AWS, Azure, and Google Cloud to provide automatic discovery and protection of cloud workloads.

4. Multi-layered Protection

The platform combines intrusion detection and prevention, anti-malware, firewall, and other features to deliver comprehensive security.

5. Automation and Orchestration

Automation features enable the platform to respond to threats in real-time, apply patches, and enforce compliance policies without manual intervention.

How to Install Trend Micro Deep Security

Trend Micro Deep Security is a security solution designed for protecting physical, virtual, and cloud servers from malware, vulnerabilities, and other threats. It typically uses an agent-based architecture where security agents are installed on the target systems. The installation process generally involves setting up the agent on servers or virtual machines, and configuring Deep Security Manager for centralized management.

While you generally use the Deep Security Manager (DSM) web interface for configuration and managing the agents, there are also command-line options for installation and automation. Below is a general guide for installing Trend Micro Deep Security on a Linux system or Windows server, and automating the installation of agents.

Steps to Install Trend Micro Deep Security

1. Obtain Trend Micro Deep Security Installer

• Sign up for Trend Micro Deep Security by visiting the Trend Micro website.
• Download the appropriate installer for your operating system (e.g., Linux or Windows).
• You will typically get a management console (Deep Security Manager) and agent installers.

2. System Requirements

Before installation, ensure the target machine meets the system requirements for the Deep Security Agent. These requirements can vary depending on your environment (e.g., virtualized, cloud, or on-premise servers).

3. Install Deep Security Manager (For Centralized Management)

The Deep Security Manager (DSM) is the web interface where you can manage your agents, define policies, and monitor security. The installation steps for DSM typically depend on whether you’re using a cloud or on-premise setup.

If you’re setting up a DSM on a Linux machine, the installation typically looks like this:

# Download the DSM installer (RPM for Linux)
wget https://download.trendmicro.com/DeepSecurityManagerInstaller.rpm

# Install the Deep Security Manager (DSM)
sudo rpm -ivh DeepSecurityManagerInstaller.rpm

Follow the on-screen instructions for setting up the Deep Security Manager.

4. Install Deep Security Agent on Servers or Virtual Machines

Once DSM is set up, you can install the Deep Security Agent on your servers or virtual machines.

Install on Linux (Example):

• Download the Deep Security Agent package for your Linux distribution (RPM, DEB).
• Run the following commands:

# Download the agent installer (for example, RPM)
wget https://download.trendmicro.com/DeepSecurityAgentInstaller.rpm

# Install the Deep Security Agent
sudo rpm -ivh DeepSecurityAgentInstaller.rpm

# After installation, the agent will attempt to register itself with the DSM

• During the installation, the agent will attempt to connect to the Deep Security Manager. If needed, you can manually register the agent using the registration key.

Install on Windows (Example):

• Download the Deep Security Agent installer for Windows.
• Use PowerShell or Command Prompt for silent installation:

# Run the installer silently
Start-Process -FilePath "C:\path\to\DeepSecurityAgentInstaller.exe" -ArgumentList "/quiet /install"

This command will install the agent without any prompts.

5. Configure the Deep Security Agent

After installing the agent, configure it to connect to the Deep Security Manager:

• The agent automatically registers with the Deep Security Manager if the connection is successful.
• You can also manually configure the agent by editing its configuration file located in /etc/ (for Linux) or through the Deep Security Manager web interface.

6. Automation with API (Optional)

If you need to automate the installation or configuration of Deep Security Agents, you can use Trend Micro Deep Security APIs. The API allows you to programmatically manage agent registration, deployment, and configuration.

Here’s an example of using the API to retrieve the status of an agent:

import requests

api_url = "https://your-dsm-server/api/v1/agents"
api_key = "your_api_key"

headers = {
    "Authorization": f"APIKey {api_key}",
    "Content-Type": "application/json"
}

response = requests.get(api_url, headers=headers)

if response.status_code == 200:
    agents = response.json()
    print("Agent Status:", agents)
else:
    print("Error fetching agent status", response.status_code)

This API request fetches the agent status from your Deep Security Manager instance.

7. Monitor and Maintain

After the agent is installed and configured, you can monitor its status from the Deep Security Manager web interface. It will show active protections, detected threats, and health information for all connected agents.

Basic Tutorials of Trend Micro Deep Security: Getting Started

Step 1: Log In to the Management Console

• Access the Deep Security Manager console using your credentials.

Step 2: Add Workloads

1. Navigate to Computers in the console.
2. Add workloads manually or enable automatic discovery for cloud environments.

Step 3: Assign Security Policies

1. Select a workload and assign a predefined or custom security policy.
2. Apply intrusion prevention, anti-malware, and other security modules.

Step 4: Monitor Threats

• Use the dashboard to monitor threats, alerts, and security events in real-time.

Step 5: Generate Reports

• Access the Reports section to generate compliance or threat analysis reports.

The post What is TrendMicro Deep Security and Its Use Cases? appeared first on Artificial Intelligence.

CyberSponse, now rebranded as DFLabs after its acquisition, is a security orchestration, automation, and response (SOAR) platform that helps organizations improve their incident response workflows and automate security operations. It provides a centralized platform to integrate various security tools, streamline processes, and enhance collaboration among security teams. CyberSponse allows for the creation of automated workflows for detecting, investigating, and responding to cybersecurity threats, reducing manual effort, improving efficiency, and accelerating response times. The platform supports real-time alerts, incident tracking, and reporting, making it easier for organizations to manage and respond to security incidents in a coordinated manner.

CyberSponse is widely used across industries like finance, healthcare, and government for incident management, threat intelligence integration, and compliance reporting. It helps security operations teams automate repetitive tasks, such as data enrichment and threat analysis, enabling them to focus on more complex and critical issues. Its integration with various security tools like SIEMs, firewalls, and endpoint protection systems also makes it valuable for security operations centers (SOCs) in enhancing the effectiveness of their threat detection and response capabilities. Overall, CyberSponse is critical in improving security posture by reducing response times and ensuring more consistent and accurate handling of security incidents.

What is CyberSponse?

CyberSponse is a SOAR platform designed to streamline and automate security operations. It integrates with an organization’s existing security tools, providing a centralized interface to manage incidents, automate workflows, and enhance collaboration among security teams. CyberSponse helps organizations handle the growing complexity of cybersecurity threats by improving the speed and efficiency of incident response.

Key Characteristics of CyberSponse:

• Automation-Driven: Reduces manual intervention by automating repetitive security tasks.
• Centralized Platform: Combines incident management, case management, and workflow orchestration into one solution.
• Customizable Playbooks: Allows teams to create tailored workflows to address specific threats.
• Tool Integration: Works seamlessly with SIEMs, firewalls, endpoint detection tools, and more.

Top 10 Use Cases of CyberSponse

1. Incident Response Automation
   • Automates the investigation and response to security incidents, reducing time to resolution.
2. Threat Intelligence Management
   • Aggregates threat intelligence feeds and correlates data with incidents to prioritize and address threats effectively.
3. Phishing Response
   • Automates the identification and remediation of phishing attacks, reducing the burden on security analysts.
4. Malware Analysis
   • Orchestrates sandbox analysis and automates containment and remediation workflows for malware incidents.
5. Vulnerability Management
   • Integrates with vulnerability scanners to prioritize and remediate vulnerabilities based on risk levels.
6. Compliance Reporting
   • Automates the generation of reports to ensure compliance with regulations like GDPR, HIPAA, and PCI-DSS.
7. Endpoint Security Orchestration
   • Coordinates endpoint protection tools to quarantine compromised devices and prevent lateral movement.
8. SIEM Integration and Alert Management
   • Enriches and triages alerts from SIEM tools, helping analysts focus on high-priority incidents.
9. Security Policy Enforcement
   • Automates the application of security policies across the organization’s tools and environments.
10. Collaboration and Team Coordination
    • Facilitates collaboration between security teams with case management and automated workflows.

Features of CyberSponse

1. Automated Playbooks – Predefined and customizable playbooks automate incident response workflows.
2. Real-Time Collaboration – Enables teams to collaborate on cases in real-time for faster resolution.
3. Comprehensive Integration – Connects with hundreds of security tools, including SIEMs, EDRs, and threat intelligence platforms.
4. Case Management – Tracks and manages incidents from detection to resolution.
5. Drag-and-Drop Workflow Builder – Simplifies the creation of custom workflows without coding.
6. Threat Intelligence Integration – Enriches incidents with actionable threat intelligence.
7. Dashboard and Reporting – Provides detailed analytics and reports on incident trends and team performance.
8. Role-Based Access Control (RBAC) – Ensures secure and controlled access to sensitive data.
9. Multi-Tenancy – Supports multiple environments for MSSPs and large organizations.
10. Scalability – Handles large volumes of alerts and incidents for enterprise-scale security operations.

How CyberSponse Works and Architecture

1. Integration Layer

CyberSponse integrates with existing security tools such as SIEMs, firewalls, endpoint detection systems, and threat intelligence platforms to aggregate data and trigger workflows.

2. Playbook Execution

Playbooks automate security workflows by defining a series of actions for specific incident types, such as quarantining a device or blocking an IP.

3. Case Management

The platform centralizes incident management, allowing analysts to track progress, collaborate, and close cases efficiently.

4. Orchestration Layer

CyberSponse orchestrates workflows across tools, ensuring seamless communication between systems and automating repetitive tasks.

5. Analytics and Reporting

CyberSponse provides dashboards and reports to monitor security trends, team performance, and compliance metrics.

How to Install CyberSponse

CyberSponse now rebranded as DFLabs, is a Security Orchestration, Automation, and Response (SOAR) platform, and typically, it is deployed on enterprise environments using a web-based interface or cloud service. Since CyberSponse is an enterprise-grade solution, it doesn’t follow the typical “install in code” style like open-source software. Instead, it is typically installed on a server, with the necessary configuration done via the platform’s user interface or API. Below is a general guide on how to set up DFLabs (formerly CyberSponse) in your environment:

1. Sign Up and Access DFLabs (CyberSponse)

To use DFLabs (CyberSponse), you will need to sign up for an account or request a demo through the DFLabs website.

• Visit the DFLabs website: DFLabs
• Contact the sales or support team to get access to the platform.

For on-premise installation or self-hosted setups, you will likely need to engage directly with the DFLabs team for access to the software and a license.

2. Install CyberSponse (DFLabs) Platform

DFLabs (formerly CyberSponse) can be installed either on-premises or accessed via their cloud platform. The on-premise installation process typically involves these steps:

• On-Premise Installation: This will require you to have a dedicated server or virtual machine. The DFLabs team typically provides an installation package and documentation for setting up the platform on your own infrastructure.
• Cloud Platform: Alternatively, you can opt for the cloud version where DFLabs is hosted on cloud infrastructure (AWS, Azure, etc.), and there is no installation required on your local servers. This version allows you to access the platform via the web interface.

3. System Requirements

Before installing, ensure the system meets DFLabs’ hardware and software requirements:

• Operating System: DFLabs may require specific Linux-based distributions (like CentOS or Ubuntu) or Windows Server environments.
• Dependencies: Ensure you have necessary dependencies like Python, Docker (for containerized environments), or PostgreSQL database if required for the setup.
• Firewall and Ports: Ensure necessary ports are open for communication between your internal security tools (SIEM, firewalls, etc.) and the DFLabs platform.

4. Use DFLabs APIs for Integration

Once installed and set up, you can integrate CyberSponse with your other security tools using DFLabs’ APIs for automation and orchestration.

For example, to interact with the API, you would typically use authentication tokens and perform API calls to fetch or manipulate incidents. Here’s an example of using Python to interact with the DFLabs (CyberSponse) API:

import requests

# Define the API URL and token
base_url = "https://your-cybersponse-instance.com/api/v1"
api_token = "your_api_token_here"

headers = {
    "Authorization": f"Bearer {api_token}",
    "Content-Type": "application/json"
}

# Example: Fetch incidents
response = requests.get(f"{base_url}/incidents", headers=headers)

if response.status_code == 200:
    incidents = response.json()
    print("Incidents:", incidents)
else:
    print("Failed to fetch incidents", response.status_code)

Replace your-cybersponse-instance.com with your actual instance URL, and provide your API token for authentication.

5. Configure Playbooks and Workflows

After installation, you will need to configure playbooks and automated workflows for managing security incidents. DFLabs provides an intuitive interface to design playbooks that can be customized based on your organization’s response procedures.

Steps to create and configure workflows:

• Define your incident response plans using the built-in editor.
• Automate repetitive tasks such as data enrichment, threat intelligence lookup, or generating tickets.
• Integrate with external systems such as SIEMs, endpoint protection, and firewall systems to automatically gather data and initiate responses based on predefined triggers.

6. Monitoring and Reporting

After the system is set up and integrated with your security tools, you can use the DFLabs dashboard to monitor ongoing incidents, generate reports, and track your security operations.

7. Maintenance and Updates

Regularly check for updates or patches from DFLabs to ensure your platform remains secure and up-to-date. Most cloud versions will update automatically, but for on-premise setups, you may need to manually install patches or upgrades.

Basic Tutorials of CyberSponse: Getting Started

Step 1: Log In to the Dashboard

• Access the CyberSponse platform using your credentials and explore the interface.

Step 2: Add Integrations

• Navigate to the integrations section and connect security tools, such as SIEMs, firewalls, and threat intelligence feeds.

Step 3: Create a Playbook

1. Go to the Playbook Builder.
2. Drag and drop actions (e.g., quarantine, block IP) to define the workflow for specific incident types.

Step 4: Automate a Response

• Assign the playbook to an incident type (e.g., phishing) to automatically trigger workflows when such incidents are detected.

Step 5: Monitor Cases

• Use the case management dashboard to track active incidents, collaborate with team members, and resolve cases.

Step 6: Generate Reports

• Access the reporting section to generate compliance and performance reports.

The post What is CyberSponse and Its Use Cases? appeared first on Artificial Intelligence.

IT certifications have always been playing a vital role in getting a job or required knowledge.

In an interview, if you have a certification, you have more advantages to get the job and I have experienced it personally.

There are lots of other channels as well to learn or to enhance the knowledge and skills these days but the thing which matters a lot is the certification, and no one can give a certified degree instead of an institute, and like the way things are evolving the demand of certification is getting increased as they need an expert for their work.

So having knowledge before going to ask for the job is much beneficial to you.

So today I am going to share the top 10 high-paying IT certifications in the world in 2022. So let’s begin.

Master in DevOps engineering (MDE) certification – This certification gives you entire information about DevOps and their related toolsets.

DevOps is just a process to be followed to achieve a high quality of software by continuous integration and continuous delivery, and their open-source tools help it to achieve the goal efficiently and effectively.

Basically, DevOps only direct the way but the major works are done by these toolsets and you can have the proper knowledge and skills by only getting trained in any institute and have the completion certification.

The demand for certification is getting higher to get a good job role in the IT sector.

DevOps is liable to do the planning, designing, coding, testing, deploying, and monitoring.

As DevOps has shown its capability the salary of candidates will be more in 2022 and will be continued.

Site reliability engineering (SRE) certification – SRE is also one of the important certifications. SRE is mainly focused on operations where the goal of SRE is to improve the reliability of software systems, through automation and continuous integration and delivery.

SRE has also open-source toolsets that cover during the certification. SRE has shown tremendous growth till now and getting used all over the world.

It’s expecting the demand of SRE would be consistent and will be on a high-paying salary list.

SRE is for those software engineers who want to work as an operation team.

DevSecOps certified professional certification – It had been forecasted to be achieved a growth of 33.7% during the period of 2017-2023. And even it has been seen the growth in the market.

So as per the result, it will dominate the market in 2022 as well.

The national average salary for a Devsecops Engineer is Rs 10,00,000 in India.

DevSecOps course is for security professionals who are willing to work in the security field like cyber security.

DevSecOp’s assumption is security is everyone’s priority and everyone should work by keeping security concerns in mind. DevSecOps also works in the collaboration with DevOps.

Docker Certified Associate (DCA) certification – Docker is a containerization tool that creates containers and allows to build, test and deploy applications.

This certification helps to learn how Docker is used to package and ship the app as well as how to create containers and so many things.

Docker has become the number 1 choice of all companies and its demand is high.

The average salary of Docker candidates in India is Rs 4,79, 074 to Rs 8,14,070, and in the USA $1,45000.

Being a Docker certified candidate is much important to get a job.

Certified Kubernetes Administrator (CKA) Certification – It has been seen CKA course is at the top to get the certification into. Kubernetes are much important to organize the containers. So Kubernetes certification is important as here you will learn so many things and most significantly how to integrate with Docker to work with.

Kubernetes has already shown its growth as it is in demand at all companies.

Kubernetes candidates can earn salaries up to 6 to 8 lakh in India and in USA between $92,500 and $147,500 per year as per a new report.

AIOps Certified Professional (AIOCP) certification – AIOps stands for artificial intelligence for operations drive automation to solve the issues by speed analyzing the root cause of the issue and taking care of the events with any human interruption.

AIOps is now trending to market and is achieving heights of success. So the growth of AIOps is getting really good and opening so many job roles in AI.

AIOps certification is very important to get into this job domain as certification can grow your chances more to pass the interview and to get the full knowledge.

Based on research the average salary of AIOps is 21 lakh per annum in India.

Master in artificial intelligence – The AI is future and there is no doubt the candidates who are trying to achieve mastery in AI studies have a great future.

Certification is playing a key role here to get your foot into the AI world.

Having good knowledge and the advantage to get the priority in an interview is not so bad. This is the advantage of certifications.

The average salary of AI in the USA is $164, 769 and in India Rs 9,01,800 per annum.

AI is the main driver of emerging technologies like big data, robotics, and IoT.

GitOps certification – GitOps is a set of practices to manage infrastructure and application configurations using Git.

Gitops uses Git as the main repository for managing all the information, documentation. It maintains infrastructure as code and keeps them too in Git.

Some developers believe Gitops is the future of DevOps that replace the Developer part with a single repository that grasps all the information needed by a developer. 

That’s why GitOps certification is important.

GitOps employee’s salary is also high according to experience. One candidate has 45 lakh per annum.

The job openings are also in good numbers to apply.

MlOps certification – Mlops is communication between data scientists and the operation or production team, it is deeply collaborative in nature, designed to eliminate waste automate as much as possible, and produce richer and consistent insights through machine learning.

Mlops is the major function of machine learning engineering.

Mlops Goals –

• faster experimentation and model development
• faster deployment of the updated model into production
• Quality assurance.

The salary for an MLOps Engineer in India is approx Rs 11,40,000 per annum.

It has been predicted to be more job openings in 2022 and the certification is a must to get the job as certification can give you an advantage during an interview.

Its shows at least you have such knowledge pertaining to this and you are trained.

DataOps certification – As per Andy Palmer “DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals”.

The aim of DataOps is to quickly deliver business value from data.

The DatOps engineer’s salary in India is Rs 7,78,290 and  $92,468 in the United States per annum.

It is predicted, to improve data quality and reduce time to insight, enterprises will increasingly embrace DataOps practices across the data life cycle in 2022.

The certification will play a vital role here to get the job as Dataops is new and also it has so many scenarios to cover so certification is a must.

And it has always been seen certifications always give an advantage during an interview. That means certification increase the status of your knowledge as well as your resume.

                      Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

The post Top 10 high paying IT certifications in the world in 2022 appeared first on Artificial Intelligence.

But, still, there are a lot of high-paying certification programs that professionals can opt for to compete in the competitive world.

If you are planning for a next move in USA, then completing these IT certification programs will boost your skills and abilities, and it will surely help you to stand out in the job market of united states.

With the changing business landscape, the demand for skilled individuals has increased immensely and organizations are focusing more on candidates who have completed certificate courses in these specific fields.  The demand for below-mentioned skills is sky-high, and the supply of professionals is meager.

Master in DevOps Engineering (MDE):- DevOps positions are consistently ranked among the highest paying salaries in the IT industry. DevOps is here to stay. And with that, the need to obtain a DevOps certification is no longer trendy, it’s quickly becoming a non-negotiable. Having DevOps certification makes you ready to work in a team of cross-functional members, including QA, developers, operation engineers, and business analysts.

DevSecOps Certified Professional (DSOCP):- Security in IT’s is a significant issue in today’s digital era, and Cyber attacks are on the rise and the Cyber threats won’t go away overnight. With this harsh reality, it’s inconceivable that any organization today would neglect the security aspect of the DevOps methodology. With this in mind, all businesses are driven digitally and IT management have moved to prioritize security and compliance at all levels. As more organizations see the benefit of end-to-end security implementation, DevOps will either fade away or get absorbed into DevSecOps.

SRE Certified Professional (SRECP):- SRE certification proves that the professional have understanding of set principles and practices designed to help organizations reliably and economically scale services. A certified professional can make an organization’s infrastructures far more stable, predictable, and scalable – all essential elements of software engineering, development, and operations.

Docker & Kubernetes Certification:- The container management tool Docker and Kubernetes are used in DevOps process to manage software parts as isolated, self-sufficient containers that can be deployed and run in any environment. In today’s market, professionals with Docker and Kubernetes skills are highly sought.

Master in Microservices:- Designing your product or application architecture can be tough as much a business decision as a technological one. Microservices is a particular way of developing software, where applications are structured as a collection of autonomous services OR we can say its a way where a large complex application are broken down into individual small-apps that are responsible for one specific product function. This skill allows large companies to gain agility and new tech capabilities to meet the growing customer demands.

Master in Big Data:- Big data professionals helps organizations to work with their data efficiently and use that data to identify new opportunities. Different techniques and algorithms can be applied to predict from data. Multiple business strategies can be applied for future opportunities and success of the organization and that leads to smarter business strategies, more efficient operations, and higher profits. With huge opportunities and investment in the Big Data technologies, certified professionals carrying the skills of big data are in huge demand.

Master in Artificial Intelligence:- This is one of the best certifications you can own if you want to lead the AI-driven technological revolution. It is not just about replacing the human component of the industry. It’s also about making it easier to make decisions based on observable patterns, use logic and reasoning to form conclusions, and build pathways to boost efficiency and production. It is not an easy discipline, but this is the reason why salaries in the AI industry are much higher than average.

Master in Machine Learning:- Machine Learning is one of the fast-emerging technology with high demand in the industry. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about Machine Learning and becoming a Certified Machine Learning Professional is a great idea and may even be a very wise career move! Naturally, you will be a hot asset for potential employers if you possess domain knowledge and skills in this field.

Master in Data Science OR Analytics:- Data Science is the latest tech trend that has taken the industry by storm. Companies and organizations, irrespective of their trade, are adopting Data Science tools, technologies, and solutions to promote innovation, increase productivity, boost sales, and maximize customer satisfaction. Once you receive the certification, you can apply for promising roles like Data Science, Data Analyst, and many more DATA-driven roles.

Dynatrace

Quantum Computing

AIOps

DataOps

OpenShift/Tanzu/Rancher/Linkerd/Envoy/Traefik/istio/consul

These certifications program will require a few months of hard work and an investment of time and money but once you’ll successfully complete the course you will be ready to achieve your goals no matter what they are.

Wrapping Up

Hope you would found our list of In-Demand IT Certifications For A Great Career in USA useful. Whether you’re a new working professional or an experienced professional, you won’t have trouble following these courses. At DevOpsSchool.com All of these certification courses are delivered by best-in-class trainers and mentors who will guide you every step of the way. Before selecting any certifications program, one only needs to be clear about their goal, know which path to take, and preserve in order to make the best of that opportunity.

Talk to our certification advisor if you need more information and guidance. (contact@devopsschool.com or +91 700 483 5930 (Call/WhatsApp))

The post The 13 In-Demand IT Certifications For A Great Career in USA appeared first on Artificial Intelligence.

StackRox today added additional DevSecOps workflow capabilities to its security platform that runs natively on Kubernetes platforms.

IT teams can now identify vulnerabilities in active software dependencies during runtime and scan for vulnerabilities in container images that have no base operating system.

Remediation efforts can now be prioritized based on vulnerability scores. The StackRox Kubernetes Security Platform surfaces recommendations so teams can identify the most efficient and effective course of action for threat remediation. They can also track mean time to remediation and refine and improve procedures and processes as teams gain DevSecOps expertise.

In addition, the StackRox Kubernetes Security Platform can now discover vulnerabilities in container images within .NET Core and ASP.NET Core frameworks.

Finally, StackRox has added integration with AWS Security Hub, Google Artifact Registry, Splunk and Red Hat OpenShift Admission Controller. Red Hat announced earlier this month it is acquiring StackRox . After the acquisition is completed, the StackRox portfolio of container security software will be made available as open source software.

David Van Everen, vice president of marketing for StackRox, says integration with platforms IT organizations rely on to manage application development and IT infrastructure is critical, because it surfaces container security insights within an existing workflow rather than requiring IT teams to log into a separate platform.

In general, Van Everen says the “shift left” of application security responsibility occurs at a time when organizations are also embracing microservices, built using containers, to build and deploy cloud-native applications. That shift is creating a unique opportunity for those organizations to embrace DevSecOps best practices, as the processes for building those types of applications are still being defined, Van Everen says. The goal is to enable organizations to continuously apply and enforce security policies as those applications are constructed and deployed, Van Everen says.

IT organizations making that transition should foster increased collaboration between developers and cybersecurity teams, Van Everen says. Kubernetes, by default, is insecure, so it’s up to each organization to learn what controls need to be applied, he says.

The StackRox Kubernetes Security Platform runs natively on Kubernetes, which Van Everen says eliminates the need to rely on proxies or container sidecars that are cumbersome to deploy and manage. However, securing Kubernetes platforms remains a specialized skill that IT organizations need to acquire, Van Everen says.

Significant progress is being made toward achieving DevSecOps, Van Everen says, but most organizations are still a long way from completely embedding security workflows within their DevOps processes. The challenge in the context of a Kubernetes environment is enabling IT teams to achieve that goal with the least amount of friction, Van Everen says.

The shift to DevSecOps will soon require security to be embedded within those workflows. As organizations embrace microservices to drive digital business transformation, initiatives that rely on platforms such as Kubernetes will find the security stakes are too high to ignore.

The post StackRox Adds Workflows to Advance DevSecOps on Kubernetes appeared first on Artificial Intelligence.

Fresh of raising $20 million in funding, Traceable CEO Jyoti Bansal said the goal is to leverage distributed traces generated via open source Hypertrace agent software to capture streaming data to which machine learning algorithms can be applied to better secure microservices.

Bansal, who is also the founder and former CEO of AppDynamics, said this approach leverages software engineering techniques and principles to address application security issues both before and after an application is deployed.

As part of this effort, Traceable has developed TraceAI, a machine learning engine that has been trained to identify application security issues.

Once those issues are discovered, developers will be able to apply policies to remediate vulnerabilities using Hypertrace agent software, he said.

In effect, Traceable is applying many of the principles of application performance management (APM) platforms to securing applications. However, rather than simply aggregating and analyzing data, Traceable has added a streaming engine to collect massive amounts of cybersecurity data in real-time that is analyzed by machine learning algorithms to determine, for example, when and how data might be exfiltrated via an application programming interface (API).

As the number of microservices that are deployed in enterprise IT environments increases, the number of API endpoints that can be compromised does as well. By adding open source Hypertrace agent software to microservices, Bansal said it becomes possible to leverage distributed tracing to better understand the context in which how business logic embedded within a microservice is being compromised. That context also serves to enable development teams to remediate that issue by either updating or replacing a microservice.

While the rise of best DevSecOps practices has increased collaboration among developers and cybersecurity teams, Bansal said application security remains largely a software engineering challenge. Application development teams need to find a way to instrument agents to address application security issues within their code instead of relying on overlays deployed by network engineers, he said.

Most cybersecurity professionals would nod their heads in agreement with that principle. However, it may take cybersecurity teams some time to get used to the idea that they might not need to be as involved with application security as they are today. In fact, the amount of time spent looking for the root cause of any vulnerability is likely to be substantially reduced.

In the meantime, it’s apparent a primordial soup of agent software, distributed traces, machine learning algorithms and streaming data engines is coming together in a way that will transform application security. It may be a little while longer before the catalyst that converts all those elements into a widely employed platform arrives. However, it appears it’s now more a question of when rather than if there will be a major advance in application security that could take DevSecOps processes to a much higher level.

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The use of artificial intelligence (AI) and machine learning (ML) is fundamentally changing the way we think about DevOps. Most notably, it is delivering a new form of DevOps that recognizes the need to have systems that are intelligent by design and underpinned by comprehensive security (DevSecOps). For many, this will be the crucial next step if DevOps is to shorten the software development lifecycle for all connected intelligent systems, ensuring the continuous delivery of secure high-quality software.

By now, most organizations understand DevOps is a substantial discipline that they must adopt – according to Deloitte, organizations adopting DevOps see an 18%-21% reduction in time to market. By breaking down the silos between business and IT operations, DevOps can ensure consistent levels of productivity, efficiency and service delivery, all of which hold weight in these times of heightened uncertainty.

To put it simply, DevOps can help businesses compete in already congested marketplaces. Through a foundation of continuous integration (CI) and continuous delivery (CD), organizations can ensure the customer receives the product they demand in the fastest time possible, while mitigating any elongated frustrations experienced from a lack of harmony between systems engineers and operations teams. Incorporating AI and ML into that DevOps strategy will take things to the next level.

AI and ML Are the Next Evolutionary Step for DevOps

Today’s companies are data-driven and being built as digital platforms. However, just 28% of organizations are currently succeeding in their digital transformation journeys. Increasingly, corporations are looking at what AI and ML can do to help them realize their transformative ambitions.

Both the AI and ML markets are expected to experience huge growth over the next three to four years. Analyst firm IDC, predicts worldwide spending on AI systems will reach $97.9B in 2023, while the market for ML is forecast to have grown at a Compound Annual Growth Rate (CAGR) of 42.8% by 2024.

What AI and ML reveals for these companies, and the increasing value of what Al, ML and devices can do together, are changing the way organizations view the world as they digitally transform. Coupling AI and ML with DevOps will lead to a significant shift in what DevOps gets involved with. 

Primarily, it sets DevOps front and center of an organization’s wider digital transformation ambitions. If digital companies run on living data, then the development of these intelligent systems is an enticing environment for DevOps to prove its wider value to the organization like never before.

Security Takes the Center Stage

According to a TechTarget survey of key IT decision makers, cybersecurity and risk management were found to be the number one area for spending this year, with more than 53% of respondents saying they saw budgets increase in this area. And security is a crucial element that any organization employing a DevOps strategy should be considering. 

DevSecOps is the simple premise that everyone involved in the software development cycle is responsible for security. In fact, the saying “security is everyone’s responsibility,” is a mantra that has been repeated often, and just as often ignored. Embedding security within every part of the development process, ensures security and compliance monitoring tools keep pace with the speed and agility that DevOps offers. It is a common understanding that security is the biggest block to the rapid and seamless development and deployment of systems, as security solutions have not traditionally been built to test and code at the speed DevOps requires. 

Ultimately though, more automation from the start, reducing the need for manual configuration from security architect is where DevSecOps shines. This reduces the chance of misadministration and occurring faults, which can lead to downtime, and potential breaches or attacks. Quite often, the best ways to secure your organization is through simple hygiene factors, such as regular patching and software updates. 

The reality is that every organization employing DevOps should really be approaching it as DevSecOps. Security is of ever growing importance to each and every organization and, through empowering it with AI and ML processes, it can be enhanced too, simplifying the processing of data to easily identify threats or potential vulnerabilities in the security makeup. 

Reaching Beyond Human Capability

The future will be made up of connected intelligent systems that span the intelligent edge, all the way to the cloud. The expectations of these systems are based on the lifecycle of a typical mobile application with constant feature enhancements. But, how do you know what to deliver next, especially when things are moving so quickly, and as developers are inundated with information and data from a number of different sources? This data deluge can be powerful, but it can also be overwhelming.

Crucially, this is where the power of AI and ML comes into play. AI and ML will aid developers in making sense of the information housed across various data warehouses. In DevSecOps, there has always been an approach to automate everything, and AI and ML will be instrumental in automating the analysis and processing of data – a task that is now far beyond the capabilities of humans.

Incorporating AI and ML will enable developers to better understand and use the data at hand. This will see developers understand not only the error, or the occurrence of a fault, but the detail of what happened in the run up to the fault – vastly reducing the chances of incurring that fault again. AI and ML will also be responsible for enabling the transformation from diagnostics to prognostics across any and all tools and systems, making it easier for developers to anticipate, identify and resolve faults or errors. 

Increasingly, we’ll see organizations benefit and drive value from real-time insights. They’ll do this through AI and ML frameworks deployed on active systems to deliver optimizations based on real-time development, validation and operational data. This level of real-time actionable insight bolsters DevSecOps’ ability to achieve CI and CD, through improved, reduced mean time to resolution, easing the impact on operations and, ultimately, enhancing the end-user experience. It remains to be seen if incorporating AI and ML into the DevSecOps cycle will achieve the delivery of self-healing systems that can detect and resolve issues without the need for human intervention. With true AI, however, this might well be possible.

The benefits and potential use cases of AI and ML are widely acclaimed, and while we are still some way from seeing their full potential, the capabilities they currently possess are revolutionizing many sectors. The fact is, if DevSecOps is to continue to keep pace with the vastly growing digital data economy, then it will become reliant on advanced technologies such as AI and ML. Data is powerful if used correctly, but with so much of it to process and analyze, that can be a challenge.

A move toward secure automated processes is the progressive step that many organizations need to consider if they are to realize their digital transformation ambitions. Organizations cannot continue to do things the traditional way and expect to get the results that the new world is looking for.

The post How DevOps Powered by AI and Machine Learning Is Delivering Business Transformation appeared first on Artificial Intelligence.

Tufin today announced the availability of Tufin SecureCloud, which combines two existing offerings to manage cybersecurity policies for both monolithic applications and emerging microservices-based application environments.

Company CTO Reuven Harrison said rather than moving forward with separate products, previously known as Tufin Orca and Tufin Iris, Tufin is aiming to appease IT organizations that prefer a unified approach to managing all their applications.

Tufin SecureCloud provides the single pane of glass that IT organizations require to secure cloud applications regardless of the underlying architecture employed, he said, noting a single offering provides the added benefit of reducing the total cost of securing those cloud applications.

Part of the Tufin Orchestration Suite of cloud security tools, Tufin SecureCloud is planned to support for instances of Kubernetes running in on-premises IT environments. Kubernetes has emerged as the dominant platform for deploying microservices-based applications built using containers. By extending the current support Tufin provides securing Kubernetes instances in the cloud to on-premises IT environments, it will become possible to secure hybrid cloud computing environments as well, added Harrison.

Because Tufin SecureCloud automatically generates, provisions and synchronizes security policy changes across multiple IT environments, Harrison said the platform will enable organizations to more easily further adoption of best DevSecOps processes spanning both application development teams and cybersecurity professionals. Tufin SecureCloud also provides integration with a variety of continuous integration/continuous delivery (CI/CD) platforms to make it easier to attach cybersecurity policies to application workloads as they are developed and deployed, he added.

Tufin is clearly wading into one of the more contentious issues in cybersecurity. Short-handed cybersecurity professionals need to rely on developers to programmatically embed cybersecurity controls into applications. However, many cybersecurity professionals have historically viewed developers as being the root cause of the cybersecurity problem because of all the vulnerabilities that are inadvertently introduced into application code. Tufin SecureCloud provides a mechanism to verify the proper policies and associated controls have been properly implemented by developers. Unfortunately, the adoption of best DevSecOps processes remains uneven at best. Most organizations are still working through their initial transition to DevOps so it may be years before most organizations are able to extend those practices into the realm of cybersecurity.

In the meantime, the amount of application code that needs to be secured will continue to pile up. Thanks to the rise of agile programming techniques and best DevOps practices, there is more application code to secure than ever. Most cybersecurity teams are already struggling to keep pace. The challenge is that as developers embrace microservices, not only will the rate at which applications are being deployed increase, but also will the number of updates being made to existing applications. Clearly, most tools for currently assessing cybersecurity posture are not going to be able to keep pace. In fact, many organizations are likely to find themselves hard-pressed to audit their application environments, much less actually able to secure them.

The post Tufin Unifies Management of Cloud Cybersecurity Policies appeared first on Artificial Intelligence.

When asked if AI would eventually replace radiologists, Dr. Curtis Langoltz of Stanford University pithily replied “No, but radiologists who use AI will replace those who do not use it.”

This great insight is highly applicable to IT. Yes, there are many ways we can use AI methods to fully automate a broad range of DevOps tasks. Companies, including CA Technologies, are actively involved in the development of solutions that enable our customers to do exactly that.

Algorithmic machine learning, however, doesn’t just empower systems to perform tasks and solve problems autonomously. It also makes them great active partners with human beings. In fact, much of what machines learn they also wind up teaching.

The synergy between AI and human radiologists, for example, stems in part from the fact that digital systems can differentiate about 200 levels of gray in a diagnostic image—compared to only about 16-20 that are discernable by the human eye. Train an AI system with enough images, and that precise cognitive power can more effectively detect that something is going on.

But for the most effective diagnostic process, you don’t just depend on that detection alone. You use that detection to empower a human diagnostician who can apply a broad understanding of pathologies and deep experience with the complexities of individual patients to deliver the highest quality care.

In DevOps, we can do the same. We can use AI to capture insights that teach us how to continuously optimize our workflows and processes. We can also use our AI learnings to push our work up higher on the value chain.

More specifically, the synergy between AI and human intellect can:

Make development smarter. The speed, quality, and efficiency of development pipelines can be affected by all kinds of subtle factors. A less-than-optimally designed API, for example, can be a small but chronic stumbling block to everyone who has to use it. Scrum outcomes can be undermined by anything from a particular type of technical challenge to a nascent personality conflict.

By capturing a rich set of DevOps metrics and applying machine learning to those metrics, development leaders can discover process bottlenecks and skilling shortfalls. They can better coach individuals and promote team collaboration. The result: a better working environment that facilitates digital agility for the enterprise and higher satisfaction/retention for valuable employees.

Make ops smarter. Enterprises are running increasingly volatile and complex workloads on increasingly hybridized infrastructure. At the same time, the tolerance of internal and external users for latency and outages continues to approach zero. There are also real costs associated with performance problems.

The elastic capacity of public and private cloud does much to help with workload volatility. But adding cloud capacity also has its costs—and end-to-end application performance often depends on back-end systems that are not cloud-based. So not every performance issue can be solved by simply throwing more capacity at it. Nor should it be, if a rearchitecting can fix a bottleneck less expensively.

Here again, AI can teach us a lot. We can uncover opaque interdependencies in processing load and data throughput. We can spot conditions when it may make business sense to throttle cloud costs that aren’t cost-justified. We can even better understand the real-world conditions—whether patterns in customer behaviors or our own marketing programs—that are driving our demand spikes and troughs. All of this helps us deliver consistently responsive digital experiences at a cost that makes good business sense.

Make security smarter. AI is already being broadly implemented in security solutions such as endpoint protection and threat response to automate the detection and neutralization of anomalous activities in the enterprise environment. But effective multi-layer security isn’t just about finding and stopping exploits. It’s also about building applications that are themselves inherently less vulnerable to hacking. This is the essence of DevSecOps.

AI has huge potential value here. We are writing a rapidly growing volume of increasingly sophisticated code. It is very easy for subtle vulnerabilities to hide in that code. As our development practices become more complex—often including multiple contractors—it becomes more difficult to understand exactly where and why these vulnerabilities were introduced into our code. Machine learning can teach us the answers to these questions, so we can more proactively secure our data and our businesses.

It’s especially interesting to consider what may happen as we start to apply AI to DevSecOps across our organizations, as well as within them. More diverse inputs enable machine learning to discover more factors that impact code pipeline performance. By aggregating our knowledge about how we build, deliver and secure our code, we are all likely to benefit with better practices and stronger guardrails.

The post When AI Met DevOps: Machine Teaching appeared first on Artificial Intelligence.

he emergence of microservices boosts business agility, enabling rapid application development, deployment and modification. The challenge is baking in microservices security processes. Traditional security processes can’t secure microservices, because the latter work in and communicate between both internal and external environments, according to Amir Jerbi, CTO of Aqua Security, a container security platform provider.

Using microservices makes security easier for developers or architects in some ways and harder in others. In this Q&A, Aqua Security Co-founder Jerbi offers advice on avoiding mistakes in setting up microservices security and balancing the often-conflicting needs for steel-trap security and rapid deployment of and communication between microservices.

What are the security pros and cons of microservices?

Amir Jerbi: Before cloud, you deployed your software on premises. You had to use the on-prem mechanism in order to secure your application. You had to use a firewall, host-based intrusion protection and maybe a code analysis tool and/or a tool to test for insecure coding. However, when deploying that app in the cloud, you had to use different tool sets and methodologies and build and deploy on one or separate cloud bases. With the shift to using microservices and containers, you can actually use the same tool set and methodologies to deploy on prem or in the cloud and even on every kind of cloud platform.

Microservices make it easier to develop an app that can run on multiple cloud platforms, because you’re using the same packaging and the same artifacts, and you can actually secure them exactly the same way. So, microservices can enable greater consistency in the way that you build, deploy and secure software.

What are some mistakes that could be made in developing and deploying microservices and building a microservices architecture that could lead to security issues?

Jerbi: With the monolithic architecture and application, all of the communications between different parts of apps would be internal. Now, to secure microservices, it’s more complex than that. You have multiple microservices running either on the same machine or distributed host, and there is a lot of communication between those microservices. Some of the communication can be on the same machine, some between different machines and some between different data centers.

When there are so many communications done between those microservices, it means that the perimeter is not something that is well-defined. You can’t put everything inside of a box and just protect that box. It’s not enough just to put a firewall in place, because all those communications must be governed and authenticated.

Easier app updating is a benefit of microservices. A developer can just redeploy a single microservice while letting the application run consistently. Doesn’t that require change management to be done differently than before in order to secure microservices and apps?

Jerbi: Yes. You need to make sure that the change that you’re adding to your system is controlled, and the software that is added doesn’t impact overall security of your application. Now, you need to manage so many small pieces, and that will require different mechanisms for authentication.

Microservice systems, many times, are open, allowing communication between the different services without any security control. So, it’s important to find ways to do strong authentication between microservices.

One way to do microservices authentication well is adopting a well-established authentication framework, like TLS [Transport Layer Security], and implementing two-factor authentication between all of the microservices. But to do that, you need to maintain new methods in order to publish security certificates and maintain those certificates. This is very different from traditional authentication, where they only needed to maintain TLS for the web server, which is much easier.

Are there other security perils that come with microservices?

Jerbi: There is a friction between the need for diligence in security and the need to make and deploy changes very, very fast because, well, you can and your competitors can, too.

Cloud deployment runs so fast today that Netflix and Google can roll out updates many times a day. Others want to do the same but often proceed without thinking through the security piece. The result can be pushing new changes into production apps without understanding the security impact of the change.

Traditionally, a security process ran slow. It required multiple intervals. It was a process that touched all along the pipeline. Now, people traditionally in charge of testing software and pushing it very fast to production are now also tasked with security — largely, securing the code. Wisely, many are taking the DevSecOps approach, wherein a group takes charge of security with a standard set of processes, such as two-factor authentication, RASP[runtime application self-protection], threat [modeling, etc.].

Which other approaches for securing microservices do you find useful?

Jerbi: Shift left testing is more focused on developers, and it allows you to do security analysis of your code or your microservices or your packages. Shift left allows you to fail fast and to fail the build, often to fail due to security issues. It might slow down development processes a bit, but it helps developers understand what’s needed from them in order to build applications with better security. Over time, the knowledge that comes from doing shift left will increase the level of overall security in the organization and allow faster deployment times. That’s because the code will be secure before deployment, and there won’t be after-deployment bottlenecks.

The post Aqua Security CTO reveals how to secure microservices appeared first on Artificial Intelligence.