Atlantic Business Technologies, Inc.

Category: AWS

  • Advanced Pipeline Orchestration Methods

    Advanced Pipeline Orchestration Methods

    Using Microservices and Containers

    Microservices and containers have revolutionized the way CI/CD pipelines operate, improving agility and efficiency. By breaking down large applications into smaller, independent services, teams can work faster and with more flexibility. Containers package these services, ensuring consistency across various environments.

    Key Benefits of Microservices and Containers:

    BenefitDescription
    Better Fault HandlingIf one service fails, the rest of the application remains operational.
    Easier UpdatesYou can update or modify one microservice without affecting others.
    Faster DevelopmentTeams can build, test, and deploy smaller, independent components more quickly.

    Steps to Implement Microservices and Containers:

    1. Break down your application into smaller, manageable services.
    2. Select a containerization tool like Docker.
    3. Use orchestration tools like Kubernetes to manage your containers at scale.

    Working with Serverless Systems

    Serverless architectures, such as AWS Lambda, allow you to focus on writing code without worrying about managing infrastructure. This can lead to cost savings and faster deployment times.

    Best Practices for Serverless Systems:

    • Utilize frameworks like AWS SAM or Serverless Framework to simplify the creation and deployment of serverless applications.
    • Design your applications to respond to specific events (event-driven architecture).
    • Continuously monitor your serverless functions’ performance and optimize them for better efficiency.

    Managing Multi-Cloud Pipelines

    Multi-cloud environments introduce additional complexity into CI/CD pipelines, but they offer flexibility and resilience.

    Tips for Managing Multi-Cloud Pipelines:

    ApproachDescription
    Use Cloud-Neutral ToolsChoose tools like Terraform or Jenkins that work across multiple cloud providers.
    Unified PipelinesBuild a single CI/CD pipeline such as  bitbucket pipelines or Jenkins that can deploy across different cloud platforms.
    Monitor PerformanceImplement monitoring tools to track the performance of your pipeline across clouds.

    Applying AI and ML in Orchestration

    AI and machine learning are transforming CI/CD pipelines, helping to predict and prevent issues, optimize performance, and improve testing processes.

    How AI and ML Can Enhance Pipelines:

    • Error Prediction: AI can predict potential failures based on historical data.
    • Pipeline Optimization: Machine learning can automate performance tuning, making pipelines run faster.
    • Intelligent Testing: AI can identify high-risk areas of the application to prioritize testing.

    Getting Started with AI/ML in CI/CD:

    1. Collect pipeline performance data.
    2. Choose an AI platform like Google Cloud AI or AWS SageMaker.
    3. Develop models to automate and enhance your pipeline processes.

    Common Challenges in CI/CD Pipeline Orchestration

    CI/CD pipelines, while powerful, come with their own set of challenges. Here are common issues and how to address them:

    MistakeSolution
    Insufficient TestingImplement comprehensive testing: unit, integration, and end-to-end tests.
    Lack of MonitoringUse monitoring tools like Prometheus or Grafana to track pipeline health.
    Outdated DependenciesAutomate dependency updates using tools like Dependabot or Renovate.
    Resource InefficiencyOptimize resources with containerization, serverless services, or cloud resources.
    Manual ProcessesAutomate repetitive tasks, such as testing, building, and deploying.

    Handling Large-Scale Projects

    Managing large-scale projects can be daunting, but with the right strategies, you can break down the complexity.

    Strategies for Managing Large Projects:

    • Break the project into smaller, modular parts.
    • Design reusable pipelines to speed up development.
    • Run parallel tasks to save time and improve efficiency.
    • Use Git and other version control systems to track code changes effectively.

    Dealing with Complex Pipelines

    Complex pipelines require careful management to maintain efficiency and avoid bottlenecks.

    Simplifying Complex Pipelines:

    • Use pipeline visualization tools like Jenkins Blue Ocean to get a clear view of your pipeline flow.
    • Break down large pipelines into smaller, manageable segments.
    • Automate repetitive tasks and monitor pipeline performance regularly to identify areas for improvement.

    Evaluating CI/CD Pipeline Success

    Measuring the success of your CI/CD pipeline requires tracking specific key performance indicators (KPIs).

    Key Performance Indicators for CI/CD Pipelines:

    IndicatorDefinition
    Pipeline Success RateThe percentage of pipeline runs that complete without errors.
    Pipeline Failure RateThe frequency of pipeline failures or errors during execution.
    Average Pipeline DurationThe average time it takes for a pipeline to complete.
    Deployment FrequencyHow often code is deployed to production environments.
    Mean Time to Recovery (MTTR)The average time taken to fix an issue after a failure.

    Measuring Pipeline Efficiency

    Pipeline efficiency can be assessed by analyzing a few crucial metrics:

    MetricDescription
    Cycle TimeTime taken for new code to go live.
    Lead TimeThe time from ideation to delivery.
    ThroughputNumber of new features delivered over a specified time period.
    Work-in-Progress (WIP)The number of tasks currently being worked on in the pipeline.

    By tracking these metrics, you can identify bottlenecks and continuously improve your CI/CD process.

    Continuous Improvement of CI/CD Pipelines

    To maintain and improve your CI/CD pipeline over time:

    • Add more automated tests to catch issues early in the process.
    • Continuously monitor pipeline performance using tools like Datadog or New Relic.
    • Regularly review metrics and make data-driven decisions to optimize pipeline steps.
    • Foster a culture of collaboration by encouraging team members to suggest improvements.
    • Stay updated on the latest CI/CD tools and techniques to keep your pipeline cutting-edge.

    What’s Next for CI/CD Pipeline Orchestration?

    As the tech landscape evolves, new advancements in CI/CD pipeline orchestration are emerging.

    Upcoming Technologies Impacting CI/CD:

    TechnologyImpact on CI/CD
    AI and Machine LearningMakes pipelines smarter, automating error detection and optimization.
    Serverless ComputingReduces the need for managing infrastructure, enabling faster deployment.
    KubernetesEnhances the management of large, complex microservice architectures.
    DevSecOpsIntegrates security practices directly into the pipeline process.

    Future Changes in CI/CD Practices

    With the introduction of new technologies, CI/CD practices will continue to evolve:

    ChangeWhat It Means
    More Complex PipelinesPipelines will incorporate multiple branching paths and conditional logic.
    Enhanced SecurityIncreased focus on integrating security throughout the CI/CD process.
    Stronger CollaborationDev, QA, and Ops teams will work more closely for faster, seamless deployments.
    Increased AutomationMachine learning and AI will further automate testing and deployment tasks.

    How does Atlantic BT fit in this picture?

    We empower clients to leverage their existing tools like Bitbucket, Packer, and Terraform, augmented by cutting-edge AI, to create advanced CI/CD pipelines. Our approach optimizes deployment across multi-cloud environments by combining automation with intelligent insights for continuous improvement.

    Key Features:

    1. Microservices & Containers: We break down monolithic applications into modular microservices using containers (Docker) to ensure scalability and fault tolerance. Packer automates the creation of machine images while Bitbucket Pipelines handles efficient code integration and testing.
    2. Infrastructure as Code (IaC): Using Terraform, we automate multi-cloud infrastructure provisioning, ensuring a consistent and repeatable deployment process across AWS, Azure, or Google Cloud.
    3. AI-Powered Automation: We integrate AI to predict errors and optimize performance by analyzing historical pipeline data. Machine learning models help improve test prioritization and auto-tune deployment times, making pipelines faster and more reliable.
    4. Smart Monitoring & Security: With AI-driven monitoring, we detect potential issues in real-time, providing actionable insights for resource optimization. Additionally, DevSecOps practices are embedded directly into the pipeline, ensuring security at every stage of the development lifecycle.

    By leveraging your current tools and integrating AI, AtlanticBT helps clients achieve faster, smarter, and more resilient CI/CD pipelines, driving efficiency and agility in their software development workflows.

  • AWS Certified Cloud Practitioner Journey with AWS She Builds

    AWS Certified Cloud Practitioner Journey with AWS She Builds

    The Certified Cloud Practitioner journey is about as simple as you allow it. The AWS She Builds program is strictly for women or individuals who identify as women. However, this post will discuss the free resources offered in a non-structural format, allowing all individuals interested in getting certified to learn more. Most importantly, studying for 1-3 months and taking the exam while it’s fresh in your mind is recommended.

    The initial sign-up process for AWS She Builds was simple. There are individual cohorts that span eight weeks. To clarify, you’re not limited to those eight weeks as it’s self-paced. Therefore, taking this journey with a full-time job is possible. However, they request that you get through the process within a specific time frame to get a free voucher ($100 USD value). There is a requirement to attend the initial onboarding meeting. After that, they will send an email, including an invite to Slack, and your journey will begin! They offer near-immediate responses to questions within the Slack channels. In addition, previous cohorts and other women who are more experienced can also provide guidance.

    The next cohort starts on August 25th (2022):

    AWS She Builds – SkillUp with CloudUp – Cloud Practitioner

    What is AWS She Builds

    • Flexible 8-week program for individuals who identify as women only.
    • Community-driven learning environment.
    • Utilizes AWS Skillbuilder for structure.
    • It provides a baseline structure to keep you on track.
      • A weekly guided academic-based flow with modules and dates to help you stay on track.
    • Live training sessions and webinars.
    • Study groups within Slack that are available online across the globe.
    • Resources from external sources are provided for extra practice.
    • Weekly live Q&A sessions for any individuals who feel ‘stuck’ or just want to listen.

    Why is AWS She Builds just for women?

    • AWS She Builds desires to bridge the gender gap, pay discrepancies, and lack of diversity in IT teams.
    • Allows women to bond and come together to discuss tech, leadership, and network for career expansion.

    What about others?

    The resources used for AWS She Builds are available for free for others. The caveat to that is that you cannot participate in live webinars or Q&A sessions or get the detailed structure paths the mentorship provides.

    Resources to get certified for Cloud Practioner:

    AWS Training and Certification Skill Builder

    Digital Cloud Cloud Practitioner Cheat Sheet

    AWS Educate

    AWS Power Hour

    Cloud Practitioner YouTube Playlist

  • Deploying a .NET Core API to AWS Lambda & API Gateway using Bitbucket

    Deploying a .NET Core API to AWS Lambda & API Gateway using Bitbucket

    After a transition to Bitbucket for our repository hosting last year, we’ve been setting up more and more CI/CD using Bitbucket Pipelines. I recently converted a .NET Core API project that was deploying to an auto-scaling group of EC2s into a project that could be deployed in AWS Lambda behind API Gateway using Bitbucket. The web project that consumes this API is written in Angular.

    As a shop that leverages automated deployments in multiple environments, I found documentation on the web to be in short supply other than very basic “deploy using VS Studio” articles.

    As part of updating the API project to Lambda, I did make my LambdaEntryPoint reference APIGatewayHttpApiV2ProxyFunction and the serverless template event of type HttpApi. A guide to these updates can found here: One Month Update to .NET Core 3.1 Lambda

    In this post, I provide some snippets of YAML code which I found out in the world that didn’t work, and also what ultimately did.

    Jump to What Worked

    What Didn’t Work

    aws-sam-deploy

    caches:
  - dotnetcore
steps:
  - export PROJECT_NAME=this-dotnet-project
  - dotnet restore
  - dotnet build $PROJECT_NAME
  - pipe: atlassian/aws-sam-deploy:1.5.0

    When trying to use the aws-sam-deploy pipe, I wasn’t able to leverage enough options or get the API to run the .NET code successfully. The API Gateway endpoint was running and hitting Lambda, but I was getting system errors I just couldn’t resolve.

    Using project appsettings files

    Since appsettings.json files contains secrets, we don’t check them into the repo. At some point I was receiving these errors, and I realized that the appsettings files weren’t getting deployed correctly. 

    run_dotnet(dotnet_path, &args) failed

Could not find the required 'this-dotnet-project.deps.json'. This file should be present at the root of the deployment package.: LambdaException

    We ended up injecting the appsettings content using the AWS Parameter Store with aws ssm get-parameter.

    dotnet publish, zip, and aws-lambda-deploy

    - apt-get update && apt-get install --yes zip
- dotnet restore
- dotnet publish ${API_PROJECT_NAME} --output "./publish"  --framework "netcoreapp3.1" /p:GenerateRuntimeConfigurationFiles=true --runtime linux-x64 --self-contained false
- curl -o /bin/jp -L https://github.com/jmespath/jp/releases/download/0.1.3/jp-linux-amd64 && chmod a+x /bin/jp
- aws ssm get-parameter --name "/this-project/api/dev/appsettings" --with-decryption --region us-east-1 | /bin/jp -u "Parameter.Value" | base64 -d > ./publish/appsettings.json
- zip -r -j package.zip publish/*         
- pipe: atlassian/aws-lambda-deploy:1.5.0
  variables:
     AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID}
     AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY}
     AWS_DEFAULT_REGION: ${AWS_REGION}
     FUNCTION_NAME: "this-dotnet-project-AspNetCoreFunction-LZj5pbvV0GRT"
     COMMAND: "update"
     ZIP_FILE: "$BITBUCKET_CLONE_DIR/package.zip"

    We have some single Lambda functions (not behind API Gateway) that use this method for deploying. It works great. I tried using this method pushing to a function that was built with a stack published via VS Studio. No luck. It’s possible there was a problem with the stack that was built, but I think this package wasn’t exactly right.

    What Works

    The following is the pipeline for a single branch, our develop branch. I haven’t yet refactored using template steps, but this is easier to read through for this article anyway.

    I have scrubbed the contents of this YAML, but the repo contains:

    • Root (.sln file)
      • .Net Core API project directory (named this-dotnet-project here)
      • Angular web project directory (named this-web-project here)
    pipelines:  
  branches:
    develop:
      - step:
          name: API (.Net Core) Build & Deploy 
          image: mcr.microsoft.com/dotnet/core/sdk:3.1
          deployment: Develop
          script:
          - apt-get update && apt-get install -y zip && apt-get install -y awscli
          - dotnet tool install -g Amazon.Lambda.Tools
          - export PATH="$PATH:/root/.dotnet/tools"
          - curl -o /bin/jp -L https://github.com/jmespath/jp/releases/download/0.1.3/jp-linux-amd64 && chmod a+x /bin/jp
          - aws ssm get-parameter --name "/this-project/api/dev/appsettings" --with-decryption --region us-east-1 | /bin/jp -u "Parameter.Value" | base64 -d > ./this-dotnet-project/appsettings.json
          - cd this-dotnet-project/
          - dotnet lambda deploy-serverless --aws-access-key-id ${AWS_ACCESS_KEY_ID} --aws-secret-key ${AWS_SECRET_ACCESS_KEY} --region ${AWS_REGION} --configuration "Development" --framework "netcoreapp3.1" --runtime linux-x64 --s3-bucket $API_S3_BUCKET --stack-name $API_STACK_NAME --stack-wait true
      - step:
          name: Web (Angular) Build
          image: atlassian/default-image:2
          caches:
          - node
          script:
	      - cd this-web-project #The angular project is currently in a subfolder in the same repo
          - nvm install 12
          - nvm use 12
          - npm install @angular/cli
          - npm run build:dev
          artifacts: # defining the artifacts to be passed to each future step.
          - dist/**
      - step:
          name: Web (Angular) Deploy
          script:
          - pipe: atlassian/aws-s3-deploy:0.5.0
            variables:
              AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID} 
              AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY}
              AWS_DEFAULT_REGION: ${AWS_REGION}
              S3_BUCKET: ${WEB_S3_BUCKET_DEV}
              LOCAL_PATH: "this-web-project/dist/this-project-output-path/"

    apt-get install and dotnet tool install

    Items that were quickly apparent as missing before adding them, more of a “duh” leaving them out when trying so many things.

    dotnet lambda deploy-serverless

    This was the big command that mostly got things working. I finally found this is effectively what’s happening when you deploy the API project from VS Studio.

    –stack-wait true

    In Bitbucket, without this, the build shows as successful when the stack build is kicked off. By adding this flag, bitbucket will wait for the full build or update before continuing.

  • Machine Learning as a Service: It doesn’t have to be complicated.

    As I was watching the AWS re:Invent 2019 keynote addresses and product releases, I was struck by a realization, namely, that machine learning isn’t some science fiction future yet to come – it’s already here, if you know where to look and how to use it.

    Machine Learning is increasingly available, but some approaches are easier than others.

    Our clients are starting to ask more and more about implementing machine learning into the solutions we provide for them. They do this because they have heard of the increasing ability of machine learning to enable automation of tasks that, until recently, could only be performed by human intelligence. The cost and time required for humans to perform these tasks meant they were often too expensive or couldn’t be offered in real-time – for example, document translation services.

    What you may not know is that many services leveraging machine learning (ML for short) are already available. For example, Amazon Web Services (AWS) is continually developing and expanding a broad range of technology services – we watch their annual re:Invent conferences very carefully to learn more about their new offerings. In fact, AWS re:Invent 2019 introduced or expanded twenty ML based services!

    We categorize ML solutions into two models.

    I like to think of these services in two broad categories: “Ready-to-Use” and “Build-Your-Own” models. Why do I make this distinction? It comes down to what machine learning involves.

    Think about what “learning” entails for a human: years of experience, from crawling to graduate school; feedback in forms ranging from trial-and-error to peer review; and the sheer repetition involved to internalize what we learn.

    The process with machines is fundamentally the same. It takes large amounts of raw data, intense processing, and guidance to develop the algorithms. For humans, this takes years of full-time processing by the human brain. For machines, the effort required is comparable – developing effective machine learning is no small task!

    For this reason, the ready-to-use models are the ones that excite me the most. In these cases, the data gathering, algorithm development, and validation have all been done for you.

    Think of all the login captcha images you’ve identified over the years. You were “training” a machine learning algorithm.

    Which Machine Learning services are easy to implement?

    Being an AWS Certified Partner, we use many of the ML enabled services from Amazon Web Services. These are just a few:

    • Comprehend – topic, sentiment, and relationship analysis of text.
    • Transcribe – automatically convert speech to text.
    • Translate – natural and accurate language translation.
    • Polly – turn text into lifelike speech.

    As you can see from these examples, these are broadly applicable services that could be developed from widely available data sources and input for training the models. Being broadly applicable, there’s a good chance one of these could be useful for your business. Fortunately, these services are ready to use and integrate in your applications.

    If you have a very specific task for a limited use case, you will likely need to use the Build-Your-Own model. As with building anything, you need the appropriate tools and techniques. Amazon Sagemaker is a tool designed for just that purpose. Frankly, building your own ML model is a complex topic beyond the scope of this post.

    If you would like to learn more about how to leverage the Ready-to-Use services, watch for my next two posts in this series revolving around these topics:

    Ready to learn more?

    If you’re interested in learning more about how you can apply machine learning, reach out for a consultation to get started.

  • A Look Inside Atlantic BT’s DevOps Process

    In order to deliver robust solutions to clients; code must be robust, reliable, scalable, maintainable, and secure. This level of quality only be achieved through building a solid software development process throughout the Software Development Life Cycle (SDLC).

    The Benefits of DevOps Methodology

    DevOps is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deliver applications and services at high velocity.

    Atlantic BT adopted DevOps methodology because we saw the following benefits, both tangible and intangible, to our ability to deliver quality solutions to our clients:

    Tangible Benefits

    • Shorter development cycle
    • Increased release velocity
    • Improved defect detection
    • Reduced deployment failures and rollbacks
    • Reduced time to recover upon failure

    Intangible Benefits

    • Increased communication and collaboration
    • Improved ability to research and innovate
    • Promotion of a performance-oriented culture

    How will working with a DevOps partner benefit me?

    You can benefit from partnering with a company that follows DevOps practices in the following ways:

    • Faster delivery of features
    • More stable operating environments
    • More time available to add value (rather than fix/maintain existing features)

    DevOps Process Chain

    Because DevOps is a cultural shift and collaboration between development, operations, and testing; DevOps focuses on process and approach.

    Atlantic BT takes the following steps in our DevOps process for software development and delivery:

    • Code – Conduct code development and review, version control tools, and code merging
    • Build – Implement continuous integration tools and build status
    • Test – Test results to measure performance
    • Package – Create artifact repository and application pre-deployment staging
    • Release (Deploy) – Set up change management, release approvals and release automation
    • Configure – Implement infrastructure configuration and management, as well as Infrastructure as Code tools
    • Telemetry – Implement application performance monitoring and end user experience measurements

    Elements of Atlantic BT’s DevOps Process

    Automation with Jenkins

    Because automation is an important part of DevOps, your tool set is essential. Atlantic BT’s primary Continuous Integration (CI) tool is Jenkins automation server. Jenkins is an extensible, cross-platform, continuous integration and delivery automation server for open source projects.

    Jenkins supports version control systems like Git, making it easier for developers to integrate changes to the project and for users to obtain a fresh build. It also allows us to define build pipelines and integrate with other testing and deployment technologies.

    Automated Testing

    We have a dedicated QA department and include QA time as part of the development plan as a best practice. As a minimum baseline, we evaluate the platform using unit and functional testing.

    Our Continuous Integration tools perform the following key test elements:

    • Unit Test validation
    • Integration Test validation
    • Code analysis
    • Functional Tests

    Once sections of an application have been QA’d through unit and functional tests, automated tests can be developed for ongoing quality assurance.

    Infrastructure-as-Code Approach

    ABT optimizes cloud architecture for maximum reliability and scalability while maintaining security. We take an infrastructure-as-code approach, scripting all instance builds so they can be automated—and thus reliably replicated—in the production process.

    The ability to reliably configure and stand up server instances is critical, as most complex projects require many servers of different configurations at different stages of the project to accommodate development, testing, migration, and production needs. This approach also facilitates Disaster Recovery planning and implementation.

    Monitoring, Metrics, and Alerting

    Understanding the importance of metrics, we maintain a fully-staffed NOC that monitors key performance parameters and alerts 24/7/365. We take responsibility for monitoring application and infrastructure health, including:

    • Application availability and response time
    • CPU, Memory and Disk
    • Throughput
    • Http response codes
    • DB Connections

    Metrics for applications hosted on Amazon are collected in AWS Cloudwatch; others are determined as appropriate by hosting method.

    DevOps and AWS

    Atlantic BT’s AWS partnership enables us to fully tap into their set of flexible services, which are designed to empower companies to deliver products using DevOps practices. These services simplify provisioning and managing infrastructure, deploying application code, automating software release processes, and monitoring application and infrastructure performance.

    AWS Command Line Interface

    As part of using the AWS console, advanced website developers can manage their websites via command line management tools like the AWS Command Line Interface (CLI). CLI is a unified tool to manage AWS services. With just one tool to download and configure, you can control multiple AWS services from the command line and automate them through scripts.

    The AWS CLI has over 140 simple file commands for making efficient calls to AWS services.

    CI/CD Pipeline on AWS

    CI/CD Pipeline on AWS allows you to automate your software delivery process, such as initiating automatic builds and deploying to Amazon EC2 instances. AWS CodePipeline will build, test, and deploy your code every time there is a code change. Use this tool to orchestrate each step in your release process.

    Other Amazon Tools

    Other Amazon tools we use include:

    • Amazon API Gateway: a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any scale.
    • AWS CloudTrail: a web service that records AWS API calls for your account and delivers log files to you.
    • AWS CodePipeline: a service that builds, tests, and deploys your code every time there is a code change, based on the release process models you define.
    • AWS Identity Access Management: manages access, where you can specify which user can perform which action on a pipeline
    • Amazon CloudFront Reports and Analytics: offers a variety of solutions including detailed cache statistics reports, monitoring your CloudFront usage, getting a list of popular objects, and setting near real-time alarms on operational metrics.

    Start Implementing DevOps Today

    Ultimately, implementing DevOps evolves products faster than organizations using traditional software development and infrastructure management processes. This speed enables organizations to better serve their customers and compete more effectively in the market.

    If you’re interested in getting help implementing DevOps or looking for a software development partner that follows best practices, contact us to learn more.

  • The Urgent Need for Vulnerability Scanning

    One might think that IT system vulnerabilities are decreasing. With the spread of virtualization and cloud adoption, we assume that security is getting stronger. Configuration and hardening technologies continue to evolve, resulting in a lower surface vulnerability – right? 

    Wrong! Not even close.

    Hackers are finding new ways to target and exploit your organization’s vulnerabilities. The National Vulnerability Database maintains over 110,000 common vulnerabilities entries. In fact, by January 4th 2019, and the NVB has already logged 39 new vulnerabilities entries for 2019.

    Why You Need Vulnerability Management

    Vulnerability Scanning is vital; it protects the hygiene of your systems by reducing attack surfaces. This protection can (and should) take a number of forms:

    External Protection

    An external attack is one done from the outside. A hacker tries to gain access to your organization’s devices and systems via the Internet. Oftentimes, your environment will have unnecessary ports open. Since they’re not in use, they are easy-to-miss open doors for a potential breach. When a breach occurs, you should disable these ports and any other insecure communications protocols.

    Internal Protection

    An internal attack is when a hacker tries to gain access through your organization’s personal wired and wireless networks. Password credentials can be one of the main issues here. They often allow for more access to systems than is necessary for that user’s role. Your organization should be leveraging identity management tools. These provide the appropriate level of access to systems needed, typically based on an employee’s position.

    Phishing Protection

    No explanation needed here, right? Hackers today are taking advantage of multiple ways to socially engineer access to your organization, and they’re doing it through your employees! Phishing’s reputation precedes it, keeping everyone on high alert. Unfortunately, the majority of breaches still happen at the human level. Educating your employees on phishing remains critical, but you can take this a step farther. Increase awareness by gaining actual business insight with testing results.

    Application Pen Testing

    Whether your application is for your internal operations or customer-facing, pen testing is essential. Vulnerabilities are often present in all application code. Best practices for development involve SecDevOps, or having security built into the development life cycle. If your company has developed an application for client use, be ready. Legal negligence will be your fault if you’re not rigorously performing security testing. While Equifax is a prime example, this can happen to organizations of any size. Hackers don’t care about the general scope of your company. They’re after the data!

    How Vulnerability Scanning Works

    With proper planning, you can do these types of testing in a non-disruptive way. It’s important to notify any Cloud providers when you schedule scans to run. They should be aware of when the scans will take place. Good deliverables should contain specific details about the vulnerabilities. This would include a ranking according to severity. Each vulnerability should have a recommended remediation approach. This is a productive action that your IT teams can tackle. When remediation is not viable, you must stay up to date with documentation. This is especially important if your organization must comply with specific Cybersecurity Frameworks.

    At Atlantic BT, we’re always ready and alert. Our Managed Vulnerability Scanning service is dependable and efficient. It provides our clients with an ongoing peace of mind. Their technical vulnerabilities and security issues are being identified. Best practice remediation is being suggested. Even better, risks are actively minimized around data loss and disruption.

    Security From Top to Bottom and Beyond

    ABT’s Security Solutions leadership and engineers have over 20 years of field experience. Our range of work includes:

    • Information Security Consulting
    • Security Operations
    • Incident Response
    • Managed Security Services

    We would never tell a client to do something we wouldn’t do ourselves. Therefore, we’ve integrated security best practices into our own daily operations. We’ve also navigated a variety of scenarios that our clients have faced. While doing so, we’ve utilized cybersecurity tools that continue to evolve in the marketplace.

    Our security team has helped many customers assess their security posture. We ensure they are covered by implementing security layers around every access point. Protection includes access controls and permissions, data encryption (both on-premise and in the cloud), and in-depth analysis to pinpoint cracks in the wall. To learn the ins and outs of your security needs, contact us today for a security assessment.Â