Learn, Grow and DO THE NEW

Introduction

Artificial Intelligence (AI) is no longer just a buzzword; it’s becoming an everyday reality for developers. With the rapid evolution of AI tools, frameworks, and APIs, Drupal CMS is also embracing AI to improve how we build, manage, and deliver digital experiences. For frontend developers especially, this shift is opening new doors: from faster prototyping and intelligent design suggestions to automating repetitive tasks.

AI + Drupal: Where the Shift Begins

Drupal has always been a powerful CMS with its modular architecture and flexibility. Traditionally, frontend developers working with Drupal handled theme customization, Twig templating, CSS frameworks, and JavaScript integrations. But now, with AI entering the picture, the landscape is changing.

• Contrib Modules like ChatGPT integration, AI content assistant, and OpenAI API
• Personalization tools powered by machine learning
• Headless Drupal + AI-driven frontend frameworks

AI in Frontend Development with Drupal

Automated Design Suggestions

AI can analyze user behavior, accessibility standards, and industry design patterns to suggest layout improvements or color contrasts. Developers get AI-generated wireframes or style suggestions that can be directly applied in Drupal themes.

AI-Assisted Theming

Working with Twig templates in Drupal often requires repetitive markup adjustments. AI-powered coding assistants now generate Twig snippets, autocomplete CSS classes, and even suggest responsive breakpoints, saving time and reducing errors.

[Also read: Enhancing Drupal with custom Twig functions and filters]

Personalized Frontend Experiences

Drupal’s strength in personalization becomes more powerful with AI. Using real-time data and predictive analytics, AI can dynamically adjust the frontend:

• Displaying different hero banners based on user intent
• Recommending personalized content blocks
• Adjusting UI elements for accessibility preferences

Natural Language to Code

Imagine writing: “Generate a responsive two-column layout with an image on the left and text on the right.” AI tools integrated with Drupal can now generate the HTML, Twig, and CSS needed, making frontend development faster and more intuitive.

Content & SEO Enhancements

AI modules in Drupal can:

• Suggest semantic HTML improvements
• Automate meta tag generation
• Optimize images and videos for faster rendering

Tools & Modules Bringing AI into Drupal Frontend

• Drupal OpenAI Module: Connects Drupal with OpenAI APIs
• Acquia Lift with AI: Enhances personalization and A/B testing
• ChatGPT & AI Chatbots: For conversational interfaces in Drupal sites
• AI-powered Headless Frontends: Drupal as backend + AI-enhanced React/Next.js frontend

[Also read: How AI and Headless Drupal Are Shaping the Future of Frontend Development]

Benefits for Frontend Developers

• Faster prototyping with AI-generated layouts
• Smarter debugging and error detection
• Consistency with accessibility and branding
• Reduced repetition in Twig, CSS, and JavaScript
• More creative freedom for UX/UI innovation

Challenges Ahead

• Accuracy & Over-reliance: AI suggestions still need human review
• Learning Curve: Developers must adapt to AI-driven workflows
• Privacy Concerns: AI personalization depends on secure data handling
• Module Maturity: AI modules are evolving; stability may vary

The Future: AI as a Co-Pilot in Drupal Frontend

The adoption of AI in Drupal CMS isn’t about replacing frontend developers; it’s about empowering them. In the near future, we can expect:

• AI-powered theme builders that create entire layouts from prompts
• Code-review bots embedded in Drupal workflows
• Real-time UX adaptation using predictive AI models

For frontend developers, this means less time debugging and more time crafting meaningful, human-centered experiences.

Conclusion

AI adoption in Drupal is already reshaping frontend development. From automating design tasks to enhancing personalization, AI is acting as a co-pilot that makes developers more efficient and creative. For frontend developers in the Drupal ecosystem, now is the time to embrace AI, not as competition, but as a partner in building the web of the future.

Introduction

Recently we realizedIn today’s competitive digital economy, the organizations that embrace DevOps automation early are moving at a fundamentally different pace than those that do not. One of our clients, for instance, successfully rolled out their fourth feature update in a single week, while their closest competitor remains tied to a quarterly release cycle that continues to face delays. This contrast underscores a larger truth: automation in DevOps has become the defining factor in achieving accelerated software delivery and market leadership.

The data confirms this trend. The global DevOps market is projected to surpass $25.5 billion by 2028, reflecting the growing adoption of automation in software engineering. Already, more than 83% of developers use DevOps practices in some form. What was once considered an emerging trend has now become a business imperative for modern software delivery.

Why Traditional Development Teams Struggle with Speed and Agility

Traditional software development often resembles moving at a slow pace on a fast highway. Developers finish new features, but delays in QA environments, operational reviews, and multiple handoffs slow down delivery. These problems lead to long release cycles that make organizations vulnerable to quicker competitors.

By bridging the gaps between the development, operations, and security teams, DevOps addresses these problems. Businesses can move from releasing updates every few months to deploying them in a matter of hours by integrating automation throughout the process, all while preserving improved quality and resilience.

Redefining DevOps Automation in Software Delivery

Many assume automation means replacing jobs, but in reality, DevOps automation enhances human expertise. It acts as a reliable system that never misses tasks, doesn’t suffer from fatigue, and continuously improves processes.

The numbers tell the story. DevOps automation tools market size grew from $9.74 billion in 2023 to $12.11 billion in 2024 – a 24.4% growth rate. That’s companies scrambling to catch up. Organizations are rapidly adopting automation to accelerate delivery pipelines, improve quality, and reduce manual dependencies.

AI-assisted DevOps automation is becoming more common with predictive monitoring, real-time anomaly detection, and automated bug detection. These technologies do not remove human judgement; they support it - teams can identify problems sooner and make informed decisions.

CICD – Continuous Integration & Continuous Delivery/Deployment

CI/CD: The Cornerstone of Modern Software Delivery

The way businesses release software has changed as a result of continuous integration and continuous delivery/deployment, or CI/CD. The days of demanding weekend deployments that involved high risk and prolonged downtime are long gone.

Organizations can confidently and regularly deploy updates with CI/CD pipelines. Automated testing cycles guarantee speed and dependability by finishing in a matter of minutes. Actually, businesses that use too many CI/CD tool frequently see a decline in performance, which emphasizes the necessity of a simplified DevOps toolchain approach.

Integrating Security Through DevSecOps

Historically, security teams resisted automation, fearing that automated scans might overlook risks. In practice, the opposite has proven true. Automated DevSecOps pipelines identify vulnerabilities far faster and with higher accuracy than manual reviews.

In one of our client implementations, automated scans flagged potential vulnerabilities within minutes of code being committed. Issues were immediately assigned back to developers, who could fix them while the context was still fresh - saving both time and cost.

Cloud and DevOps: Driving Scalable, Cost-Efficient Delivery

Cloud platforms have become a natural partner for DevOps automation. By leveraging cloud services and DevOps as a service, organizations accelerate adoption and scale without being constrained by legacy infrastructure.

[You may like reading: 5 Benefits of Using DevOps with Cloud]

Cloud strategy consulting plays a critical role in aligning technical implementation with organizational needs. One client reduced infrastructure costs by 38% through automated scaling policies, while Generative AI-driven cloud optimization. Generative AI services analyze usage patterns and suggest improvements that humans just don’t think of.

Leadership and Culture: The Foundation of DevOps Success

Though automation and tools are valuable, the ultimate strength of DevOps lies in altering the culture. It needs to be fostered by leadership through encouraging collaboration between development, operations, and security.

Small wins, like marking the first automated deployment or cutting deployment time in half, make teams embrace change. With time, such a shift in attitude transforms DevOps into a process and then into a competitive advantage. Companies with change leadership as a focus always enjoy superior adoption of DevOps automation, CI/CD pipelines, and cloud-native approaches. Absent this cultural consensus, even top-of-the-line tools cannot deliver their full benefit.

Leadership, Cultural Change to Competitive Advantage

Measuring Success in DevOps Automation

The success of DevOps automation can be tracked through four key performance indicators (KPIs):

• Lead time for changes
• Deployment frequency
• Mean time to recovery (MTTR)
• Change failure rate

Clients adopting this measurement-driven approach often see immediate improvements. One enterprise now deploys to production six times daily with increased system reliability, proving that smaller, automated releases enhance both stability and speed.

[ You may like reading: 5 Benefits of Using Strategic Approach to Automation in DevOps: Knowing When and What to Automate]

What’s Next: The Future of DevOps Automation

The DevOps ecosystem is changing, especially with the rise of serverless computing, edge architectures, and AI-driven development. Those organizations investing now in digital engineering services and cloud migration services are laying the groundwork for greater agility in the long-term.

The principles remain the same, automation, reliability, and speed, but the tools will continue to evolve. Organizations that think ahead, should put flexible architectures as a priority to remain competitive.

Conclusion

DevOps automation is no longer optional. It is a strategic necessity for companies that want to speed up software delivery. By breaking down silos, using automation, and adopting cloud-native practices, businesses change from slow-moving organizations into market leaders.

The message is clear: speed wins. Companies that embrace DevOps automation respond faster to market changes and also shape them. They become innovation leaders in their industries.

Introduction

The right decision isn’t a guess - it’s in the metrics. Let's understand “How”?

In the fast-paced, goal-oriented project environments of today, intuition is insufficient. Experience is important, but what really sets reactive project managers apart from strategic leaders is data-driven decision-making.

Throughout the project lifecycle, this guide explains how a project manager (PM) can use metrics to make better, quicker, and more assured decisions. Before getting into how to make data or metric driven decisions, lets understand what is a data driven decision and why is it important?

Data-driven decision-making (DDDM) is the process of making choices based on actual data rather than intuition, guesswork, or personal experience alone.

What Kind of Decisions Can a Project Manager Make Using Data?

For a project manager, the right data can be a best friend in driving project success. Below are key responsibilities of a PM and how data-driven decisions help in each area:

• Aligning project goals with measurable outcomes
• Ensuring scope, cost, and timelines are realistic and controlled
• Improving stakeholder communication with evidence-backed updates
• Detecting risks early and reprioritizing resources effectively
• Optimizing quality and user adoption with clear benchmarks

To understand better, the metrics a Project Manager should be tracking in each phase of project delivery:

Types of Metrics for a Project Manager:

• Schedule Metric – Measurement of actual progress vs. the planned schedule
  • Eg: SV -(Schedule Variance = Earned Value - Planned Value)
    • A positive SV means you are ahead of schedule
    • A negative SV means you are behind your planned schedule
  • SPI -Schedule Performance Index= SPI = EV / PV
• Cost Metric – Measurement of planned vs. actual cost of implementation
  • -CV - Cost Variance = Planned Cost - Actual Cost
    • Positive CV is you are under budget
    • Negative CV is you are over budget
  • CPI - Cost Performance Index- CPI = Planned Value / Actual Cost
• Quality Metric – Defines how well your product meets the planned quality
  • Defect Density = No. of defects / Lines of code
  • Customer Satisfaction = Happiness percentage received in a product survey
• Risk Metric measure used to identify, assess, and track risks in a project, business process, or organization. It helps decision-makers understand the likelihood, impact, and exposure of risks so they can be managed effectively.
  • RAID log
  • Impact / severity
  • Issue Resolution Rate
  • Probability of risk occurrence

How to Use Metrics to Drive Decision-Making

Identify What Matters Most

Not all metrics are created equal. Align your metrics with:

• Project goals
• Stakeholder expectations
• Business value

Example: New Product Development Project (Mobile App Launch)

• Project goals:
  • Deliver a high-quality mobile app within 6 months
  • Ensure adoption by at least 10,000 active users in the first quarter after launch
• Key Metrics Chosen:
  • Schedule Variance (SV) measures whether the project is on track for a timely release
  • Defect Density tracks the quality of features delivered
  • Cost Performance Index (CPI) ensures that costs stay under control
  • Monthly Active Users (MAU) → Measures product stickiness and revenue potential

Establish a Regular Reporting Cadence

Use dashboards or WSRs (Weekly Status Reports) to:

• Track progress
• Spot trends
• Communicate insights clearly

Example: ERP Implementation Project

• Track Project Progress:
  • Dashboard Metric: % completed modules (Finance 80%, HR 60%, Procurement 40%)
  • WSR Content: “This week, we finished HR data migration, which is 60% overall. The Finance module is on track to complete UAT by next Friday.”a migration (60% overall). Finance module is on track to finish UAT by next Friday”
• Look out for Trends:
  • Dashboard Metric: Defects found vs. resolved (trend line week over week)
  • WSR Content: “Defect closure rate improved from 65% to 78% this week. However, recurring integration issues in Procurement suggest we may need extra testing cycles
• Visualisation of Dashboard:
  • RAG (Red/Amber/Green) status for schedule, budget, and scope
  • A burndown chart that compares planned and actual tasks

Use Metrics to Ask Better Questions

Instead of just reporting, use metrics to:

• Forecast risks
• Re-prioritize tasks
• Justify trade-offs in time, scope, or budget

Example: Project to Redesign a Website

• Estimate the Risks
  • Metric: The burn-down chart indicates that the number of tasks left is not declining as quickly as anticipated
  • Question Asked: “At this velocity, will we miss the launch date by 2 weeks? Should we add another front-end developer to mitigate the delay?”
• Re-prioritize Tasks
  • Metric: Defect severity trend shows 70% of open defects are in the checkout/payment module
  • Question Asked: “Should we reprioritize testing to focus on checkout before UI enhancements, since it directly impacts revenue?”

The image shown above is the Eisenhower Matrix (also called the Urgent-Important Matrix), a time management and prioritization tool. It helps people decide what tasks to focus on, schedule, delegate, or eliminate based on urgency and importance.

 

Justify Trade-offs in Time, Scope, or Budget

• Metric: Earned Value (CPI = 0.85, SPI = 0.9) indicates cost & schedule slippage
• Question: “Should we reduce scope (drop low-priority features) or request a budget increase?”

Benefits of Data-Driven Decision-Making in Project Management

• Forecasting & Reduces Project Risk
  • When we have data upfront showing the gaps/progress, we can sense risk in much advance and mitigate it timely
• Improves Stakeholder Confidence
  • Data brings transparency, and hence a real picture can always be presented to all the stakeholders leading to stakeholders trust & confidence
• Aligns team performance with business goals
  • Metrics shows the areas which are to be improved, and hence tasks can be updated time to time to make the team aligned with the end goal

Tools for Tracking Metrics in Project Management

• Power BI or Tableau for visual analytics
• JIRA and Confluence for Agile metrics
• Smartsheet or MS Project for schedule and resource tracking
• Gen AI tools to summarize, analyze, and recommend based on real-time data

Common Challenges & How to Overcome Them

Every project faces roadblocks - whether it’s unclear goals, shifting priorities, or resource constraints. The key isn’t avoiding challenges but knowing how to anticipate, measure, and tackle them with the right strategies. Below are the obstacles being faced in creation & management of metrics

• Data Overload: Too many metrics can distract; focus on KPIs tied to business goals
• Lack of Stakeholder Buy-in: Use visualizations (dashboards, charts) to make data persuasive
• Tool Fragmentation: Integrate systems (JIRA, Power BI, MS Project) for consistency
• Skill Gaps: Train PMs in data literacy and use of analytics platforms

Conclusion

Data-driven project management transforms guesswork into confidence. By leveraging metrics and past performance, managers gain control over scope, time, cost, quality, and risk - delivering projects that are not only successful but predictable. Turn data into your greatest ally, and watch your team’s true potential come to life.

Let data lead the way and turn every project into a predictable success.

Introduction

The debate of CMS VS no CMS always comes up in every enterprise project numerous times. The Drupal and WordPress debate is quite popular. Although both platforms have their strengths, in a conversation about scalable websites that require user experience design excellence, Drupal continues to outshine WordPress for heavyweight digital platforms.

Here’s why Drupal should be your top choice in a CMS for scale, and how it directly affects the user experience in the ways that matter most to both your users and your business goals.

Scalability and Performance: UX vs Technical Excellence

When serving enterprise clients, the scalability discussion isn’t only around traffic spikes. Instead, it's about retaining that seamless, highly responsive web design experience even as your website grows from thousands to millions of users. This is where Drupal excels.

Drupal is super scalable and can handle large amounts of content and traffic with ease, so it’s a good fit for large and enterprise websites. This is apparent when sites are hit by unexpected amounts of traffic. TheJustice.gov website, built using Drupal development services and saw a 7,000% spike in traffic when it published the Mueller report, but had no downtime. That’s the type of performance that equals directly to user trust and satisfaction.

From a UX standpoint, this scale means your users have the same experiences when coming to your site whether it’s 4am, or if there’s 1 user, or 2,000. Drupal gives more out-of-the-box capabilities for that enterprise-grade architecture and custom user roles, while WordPress might need more third-party plugins to achieve the same thing, driving up your maintenance overhead.

The performance optimizations in Drupal are amazing. Drupal has great full-page caching support, dynamic content caching, CDN integration and lazy loading, revealing itself in faster page loading times, fewer server resources, and SEO improvements. We know the vale of a second as UX people. Research has also shown that users leave slow websites that take more than 3 seconds to load, so Drupal's performance improvement is a direct contributor to a more positive user experience.

One of its particular strengths is its ability to manage and traverse complex relationships between data, without sacrificing performance. And from designing enterprise-level journeys with wide varieties of content types and user-role-defined and personalized experiences, Drupal’s architecture natively fits with those use cases than trying to make some workarounds and that might affect the performance of the User Experience.

Introduction

The traditional method of security testing is no longer adequate in the context of a more advanced threat environment in the ever-changing world of cybersecurity. Attackers are becoming more advanced, attack vectors are becoming more complex, and the online presence of organizations is growing by the minute. In response to this seismic shift, Artificial Intelligence (AI) is on the verge of becoming a game-changer, revolutionizing how security testing is performed by organizations. AI offers a massive increase in the attainment of optimum digital security owing to its ability to process an enormous amount of data, recognize patterns, learn in real-time, and predict upcoming threats.
This piece of writing is about how artificial intelligence (AI) is revolutionizing security testing, its biggest benefits, the technology that is leading this transformation, and the future.

What is Security Testing and Why It Matters in Cybersecurity

In essence, security testing guarantees that networks, applications, and software systems do not have vulnerabilities that can be exploited by attackers. Though conventional testing techniques, such as penetration testing, static code analysis (SAST), and dynamic analysis (DAST), are required, they are largely manual, reactive in nature, and often not scalable enough in today's digital landscape.

Today's security forces are faced with challenges like:

• Unseen attack methods and zero-day exploits
• Massive distributed systems and large cloud infrastructures
• Code changes are pushed several times a day by CI/CD pipelines and DevOps
• Advanced attackers use computer-based intelligence and automation methods
• In this regard, artificial intelligence enhances existing practices rather than merely augmenting them

How Artificial Intelligence Enhances Security Testing

AI for Intelligent Vulnerability Detection

AI improves security testing through automated detection of application and infrastructure vulnerabilities. AI-scanners leverage machine learning (ML) to identify suspicious activity, anomalies, and possible threats - irrespective of whether they are a known exploit or not - instead of known signatures or by-hand code review.
Artificial intelligence, for instance, will be able to monitor past attack patterns, infer trends from past attacks, and spot abnormal activity that other technologies will miss. This, in turn, leads to detection occurring earlier and fewer false alarms.

AI-Powered Predictive Threat Modeling

The predictive power is likely to be the largest benefit of artificial intelligence. Based on millions of indicators such as security logs, attack patterns, and dark web discussions, AI is capable of predicting upcoming attacks and suggesting proactive mitigation steps.
In doing so, companies can construct threat-resistant frameworks from fluid threat environments, instead of reactive testing.

AI in Automated Penetration Testing

In comparison with manual testers, AI-powered penetration testing tools can perform more realistic attacks and faster. AI-powered pen testing tools can automate exploitation tasks and reconnaissance tasks.
Adjust based on system responses.
Mimic human decision-making in experimental settings.
This makes penetration testing more frequent, more predictable, and more cost-effective, especially in agile development.

AI for DevSecOps and CI/CD Security Testing

Speed is a key element of contemporary software release pipelines. Security can be effortlessly infused in CI/CD pipelines through the utilization of artificial intelligence to automatically scan code repositories for misconfigured or vulnerable libraries.
The examination of commit messages and ticket reports for signs of insecure practice employs natural language processing (NLP) methods.
The procedure of identifying issues to be solved before deployment entails learning from past builds.
This allows developers to correct security flaws at the most economically viable stage of the development lifecycle.
Key Technologies Facilitating AI-Driven Security Testing Several areas of artificial intelligence and technology are spearheading this change:
Machine Learning (ML): For predictive modeling, pattern recognition, and anomaly detection.

• Natural Language Processing (NLP) is utilized to examine documentation, logs, and bug reports in order to look for possible indicators of risk
• Reinforcement Learning: For learning and mimicking attack tactics in real time
• Graph Neural Networks enable the understanding of relationships between objects in complex network topologies
• AI-powered SIEM (Security Information and Event Management): To analyze and prioritize incidents from billions of data points

Products such as Microsoft Defender for Endpoint, Cortex XDR, and Darktrace have increasingly integrated artificial intelligence into their threat response and detection features, reflecting an increased alignment with development and testing activities.

Challenges of Using AI in Security Testing

AI is not without disadvantages, however, with:

Data Quality: The efficacy of artificial intelligence models is determined by the quality of data used to train them. Incorrect or biased data will result in incorrect predictions.

Adversarial AI: In this manner, the illicit use of artificial intelligence has brought new forms of attacks referred to as adversarial inputs or AI poisoning.

Competency Gap: Companies need machine learning and cybersecurity experts.

Cost and Complexity: There is a requirement for initial investment and infrastructure to implement AI-based testing.

Therefore, the enhancement of security testing through the use of artificial intelligence requires the establishment of proper governance, oversight, and human intervention.
Real-World Use Cases
Google's OSS-Fuzz uses artificial intelligence techniques to scan for open-source software vulnerabilities.

• GitHub Advanced Security uses machine learning to scan repositories for sensitive information and vulnerabilities
• Darktrace employs unsupervised learning to identify threats without marked attack data
• Machine learning-based bug bounty platforms like Bugcrowd use ML to prioritize the reported vulnerabilities for faster verification

All these instances show how AI is already part of modern security operations.

Future Directions: Merging Human and Artificial Intelligence

The role of Artificial Intelligence is to augment the role of security analysts and ethical hackers and not take their place. In the future security testing scenario, a blend of methodologies will be visible, in which human experts will take up strategic responses, threat modeling, and out-of-the-box thinking, and Artificial Intelligence will undertake repetitive, bulk, and predictive work.

Expected areas of expansion are Explainable AI (XAI) improves the auditability of decisions. Security testing of autonomous agents is directly embedded into infrastructure. Utilization of AI-based compliance verification drastically reduces audit and documentation time. In conclusion, Artificial Intelligence (AI) offers security testing with the critical advantage of having a proactive, flexible, and robust methodology in a constantly evolving threat landscape. AI transforms the security model from a reactive process to a strategic imperative of safe innovation through actively uncovering threats, anticipating risks, and automating complex testing processes. Application of artificial intelligence in security testing is a vital imperative not only for digitally remodeling business organizations but also for their survival in today's threat landscape.

Introduction

Before we dive into prompt engineering, let’s first clarify: What is a prompt?
A prompt is the input or instruction we provide to an AI tool or chatbot. The response quality depends heavily on how well the prompt is designed.
Prompt engineering is the art of creating precise and structured inputs that guide AI tools - like ChatGPT, Gemini, or Perplexity - to generate accurate, relevant, and context-aware responses. By carefully crafting prompts, we help Large Language Models (LLMs) interpret context, follow instructions, and respect language nuances. The result? Rich, actionable, and highly relevant answers instead of generic outputs.

Core Concepts & Terminology

• Large Language Model: A Machine learning language model trained on vast text datasets to generate human-like text
• Generative Pre-trained Transformer: It's a type of AI Language model which are trained to understand & generate human language
• Generative AI: AI systems that can create new content based on it's deep learning models
• Token: Token is a basic units of text that the model processes. i.e Word
• AGI: An AI that possesses broader human-level cognitive abilities
• AI Alignment: Ensures that AI actions and outputs align with human intent, ethics and values
• RLHF: A technique that refines AI behavior using human inputs

AI Prompt Engineering Fundamentals: How Prompts Work

Breakdown of Prompt Interaction

Prompt (Input): The instructions or queries user provides to AI Tool e.g, “Explain Zero Trust Exchanges in simple terms.”

Output: A response generated by AI based on the prompt

Parameter Controls: It's a settings that influence output styles & randomness of the response:

• Temperature: Higher = More creative; Lower = More focused.
• Max Tokens: It is used to limit response length.
• Stop sequences: It defines where AI should cease generation.

Iterative AI Prompt Refinement

Iterations are frequently necessary for prompts to achieve quality standards.

Lesson: Iteration refines unclear queries into high-value, well-structured AI responses.

AI Prompt Engineering Principles for DevSecOps

• Always be clear and specific: Example: "Write a 250-word summary of Zscaler Private Access."
• Provide clear context for queries: Define AI’s role, audience, and purpose. "You are a DevSecOps engineer. Explain CICD security to junior engineers."
• Break complex tasks into smaller segments: Use multiple prompts for step-by-step refinement.
  • Prompt 1a: List 5 security risks in config change automation.
  • Prompt 1b: Elaborate on risk #2 with mitigation strategies.
• Mention desired output format: Request summary, bullet points, code snippets, tabular format, or in-depth guides as needed. Example: "Provide a comparison table of ZPA vs. VPN for private access."
• Use iteration and role prompting.
• Encourage AI reasoning: Use chain-of-thought style prompts for step-by-step problem solving. Example: "As a security engineer expert, think through the potential attack vectors in this code."

Advanced Prompting Techniques

Role and Nested Prompting

• Role Prompting: Designate AI as a subject matter expert to enhance pertinence. E.g. “Act as an AppSec Engineer ....”
• Nested Prompting: Takes the result from one prompt as the input for another to create multi-reasoning workflows.

Chain of Thought (CoT) Prompting

CoT encourages the AI to reason step-by-step, improving accuracy and transparency. It is vital for troubleshooting & debugging code.
Example:
Prompt: “As a Security Engineer, detail the process for configuring secure access to a private server, step-by-step.”
Output: A Comprehensive answer along with security recommendations.

Use Cases of AI Prompt Engineering in DevSecOps

Conclusion

Prompt Engineering helps us to unlock the true potential of the AI Tools. By mastering the list of things we discussed in this blog, such as the Role Prompting, CoT Techniques, you will be able to generate precise responses.
For the DevSecOps engineers, this means :

• Generating faster documentation
• Assisting in setting up automation workflows
• Enhanced training and knowledge sharing
• Much effective technical communication.

Bonus Tip: Use Custom Instructions (You can find this in almost all the AI Tools) to tailor AI to follow specific role, language tone and formats.

Steps to setup Custom Instructions in ChatGPT:

• Click on profile
• Then click on Settings
• Go to personalization & enable Custom Instructions

For Perplexity:

• Click on Account
• Go to personalization & write custom instruction in "Introduce Yourself" section.

Introduction

When organizations first move to the cloud, the excitement typically focuses on speed. You can launch a database in minutes, spin up a Kubernetes cluster before lunch, and scale applications without ever touching a data center. However, soon after, another reality sets in: the monthly cloud bill continues to increase.

Finance teams notice the rising costs, but they don’t always know why. The truth is simple: in the cloud, every technical decision has a financial consequence. And because DevOps teams are the ones making those decisions, cost optimization is and must be their responsibility.

The Cloud Changed How We Spend Money

In the on-premises world, IT costs were primarily capital expenditures (CapEx). You bought large servers once, planned for 3–5 years, and that was it. Finance could track large purchases and plan budgets easily.

In the cloud, everything is OpEx (operational expenditure). Instead of buying hardware once, you’re renting resources continuously. An idle EC2 instance, an oversized RDS database, or logs that no one ever queries still cost money every single hour.

That means DevOps decisions - what instance family to choose, how to size containers, whether to enable detailed CloudWatch logs & monitoring - directly shape the bill. Finance can only see the result. DevOps controls the actual levers.

Why Devops Owns Cloud Cost Optimization

Let’s break down why cost optimization naturally belongs under DevOps:

• Infrastructure as Code = Infrastructure as Cost

  Every line of Terraform, every Helm chart, every pipeline step is infrastructure - and therefore cost

  • A t4g.medium vs a t4g.large isn’t just an instance configuration difference - it’s a billing difference
  • Choosing ECS Fargate over EC2 Auto Scaling changes the cost structure entirely

  Since DevOps owns the code, they own the costs

• Real-Time Visibility

  Finance teams see the AWS bill at the end of the month. DevOps engineers see it in real time:

  • CloudWatch dashboards
  • OpenSearch log volumes
  • ECS task scaling
  • Compute Costs
  • Data Transfer

  If a data streaming pipeline is suddenly processing terabytes of data, Finance won’t know until next month. DevOps can catch it in minutes.

• Automation Potential

  DevOps is uniquely positioned to automate cost savings:

  • Turn off non-prod environments at night and weekends
  • Configure auto-scaling based on real workloads, not guesses
  • Replace static On-Demand EC2 instances with Spot Instances

  These aren’t finance policies - they’re finops practices.

• Balancing Reliability and Cost

  It’s not just about saving money. It’s about right-sizing.

  • Over-provisioning wastes money
  • Under-provisioning creates outages

  DevOps sits at the sweet spot, close enough to the workload to know performance requirements, but responsible enough to keep things efficient.

The Business Impact of Cloud Cost Optimization

Cloud cost optimization is not only about lowering monthly bills. It directly impacts:

• Profitability reducing wasted spend improves margins
• Innovation savings can be reinvested into R&D or scaling new services
• Sustainability means fewer idle resources and a lower carbon footprint
• Resilience, well-optimized systems often perform better under pressure

By tying cost decisions to business outcomes, DevOps leaders can show tangible value beyond infrastructure management.

Real Examples of DevOps Driving Cost Savings

At TO THE NEW, we work across diverse client projects and have witnessed firsthand how strong DevOps practices significantly reduce cloud bills. Here are a few examples:

• Right-Sizing ECS Fargate Services: We discovered several services running with 4 vCPU and 10 GB memory when they barely needed half of it. By resizing, we cut compute costs for those workloads by ~40%. Thanks to AWS Compute Optimizer for making this easy for us.
• RDS Modernization: Many applications were still using MySQL 5.7 with extended support fees. By migrating to MySQL 8 and shifting to AWS Graviton-powered processors, we not only reduced licensing costs but also got better performance per dollar.
• Weekend Shutdowns for Non-Prod: QA, staging, and integration environments don’t need to run 24/7. Automating shutdowns during off-hours saved 20–30% monthly on those environments - with zero impact on productivity.
• Fluent Bit vs Logstash: Replacing heavy Logstash sidecars with lightweight Fluent Bit reduced CPU/memory consumption on ECS tasks. The result? Lower compute bills and faster log pipelines.
• Compute Optimizer + Graviton Migration: AWS Compute Optimizer flagged dozens of over-provisioned instances. Migrating them to Graviton-based families immediately delivered 20–40% cost savings.
• Spot for the Safety: Leverage spot instances as much as possible. Using a spot in a non-prod is not a brainer. With a proper fallback mechanism, spots can be used in production as well. Just moving to a spot can reduce your monthly expenses drastically.

Each of these wins wasn’t about “negotiating discounts” with AWS. They were about smart engineering choices made by DevOps.

Building a Cost-Aware DevOps Culture

So how can DevOps teams take ownership of cost optimization?

• Make Cost a First-Class Metric

  Latency, error rate, availability… and cost.

  • Set up dashboards that track daily spend by service
  • Treat unexpected cost spikes as incidents
• Tag Everything

  Without tagging, you don’t know which team, environment, or service is responsible for what.

  • Use tags like environment=prod|staging|dev, owner=team-name, module=xyz
  • Enforce tagging via CI/CD pipelines
• Shift Left on Cost

  Don’t wait for the bill.

  • Add cost estimation tools to Terraform pipelines
  • Use policies (OPA, Infracost, custom scripts) to prevent costly resource choices from being deployed
• Use the Right AWS Tools
  • Cost Explorer for trends
  • Budgets + Alerts for thresholds
  • AWS Compute Optimizer for right-sizing
  • Trusted Advisor for savings recommendations

Future of DevOps and FinOps Collaboration

While DevOps engineers must own cloud cost optimization, they don’t operate in isolation. This is where FinOps (financial operations) enters the picture. A strong FinOps practice aligns finance, engineering, and product teams to make cloud costs transparent and accountable.

• DevOps brings real-time visibility and automation
• Finance brings governance and forecasting discipline
• Together, they enable predictable, efficient cloud spend

Forward-looking organizations will see DevOps and FinOps not as competing functions, but as partners in building cost-efficient digital operations.

Conclusion

The era when cloud costs were “someone else’s problem” is over. In the cloud, every engineering choice carries a dollar sign, and that makes DevOps teams the natural owners of cost optimization. Generative AI, automation, and AWS-native tools now make it possible to control costs in real time, but only if DevOps embraces cost as a first-class metric alongside performance and reliability.

Cloud cost optimization isn’t about cutting corners - it’s about building efficient, resilient, and future-ready systems. DevOps teams that embed cost awareness into their culture will not only deliver faster and smarter, but also unlock strategic business value.

Simply put: efficient engineering is cost-aware engineering. And in today’s world, cost-aware engineering is DevOps.

Introduction

In today’s digital-first world, having a mobile app isn’t just an option - it’s often the difference between staying relevant and being forgotten. From startups to global enterprises, every business is racing to put their brand on the home screen of their customers.

But here’s the real question every entrepreneur and business leader faces: “How much does it cost to develop a mobile application?”

The truth? There’s no fixed cost. A basic app might be $15,000–$30,000, while a complex, enterprise-level one can exceed $250,000. The wide gap comes down to factors like complexity, design, tech stack and the team you choose. Also, keep in mind that making applications from scratch and modernizing legacy systems require different approaches and are thus priced differently.

Introduction

In the fast-paced environment of mobile app development, organizations are faced with a key decision: Flutter App development or Native app development – which will give you the best user experience, fastest development and the best return on your investment in the long term?

Since I’ve had the pleasure of leading various Android app development and iOS app development efforts, utilizing both conventional native technologies for them as well as Flutter, the question I frequently receive is

• Can Flutter applications stick with performance in the native space?
• Is Flutter good for enterprise mobile app development or just for MVPs?

Let’s get into how Flutter and native app development compare in 2025 across performance, cost, UX, scalability and more-so you can make the right choice for your product.

Comparing Flutter vs. Native Development

Performance is always the top priority for any app. Native apps have an edge in hardware-level optimization and are best in class especially in resource intensive use cases like gaming, AR/VR or video processing. That being said, Flutter has closed the gap significantly. Thanks to Dart’s AOT and JIT compilation and high-performance Skia rendering engine, most business apps built with Flutter today deliver near-native performance - smooth animations, responsive UIs and low startup times.

• Use Native for apps that require deep performance tuning and optimization
• Use Flutter for UI-rich, data-driven apps that require fast and responsive interfaces

Development speed & cost

With Flutter mobile development, developers can use a single codebase for Android, iOS, and even partially for web and desktop, resulting in:

• Lower development cost
• Shorter release cycles
• Easier QA and maintenance

Native development involves building and maintaining two separate codebases (one for iOS, one for android) which means double the effort and cost - but gives full control over each platform.

• For time-sensitive or budget-conscious projects, Flutter can accelerate delivery
• For platform-specific optimization or long-term ecosystem alignment, native seems to be a good bet

Bonus:

If you anticipate a web version of your app in the future, Flutter’s cross-platform development capability enables reuse of much of the codebase, saving considerable time and effort.

Is Flutter good for enterprise apps?

Today both Flutter and native platforms can power enterprise-grade mobile apps. Flutter has matured into a powerful framework for Flutter enterprise app development. It is used by companies like BMW, eBay, and Alibaba for scalable, secure, and visually appealing apps. For enterprise mobile app development specifically, Flutter delivers:

• Modular architecture
• Role-based UI
• Advanced state management
• CI/CD compatibility

Native development is still the gold standard for security-focused or compliance-intense apps-especially in finance, healthcare and logistics-due to its maturity and platform-level support.

• Flutter is viable for enterprise with scalable architecture
• Native is preferred for long-standing, regulated enterprise environments

How secure are Flutter and Native apps?

When it comes to security, native apps have an edge because built-in security features like:

• Biometric login (Face ID, fingerprint)
• Secure data storage
• Hardware-level encryption
• Support for Mobile Device Management (MDM)-used by companies to control app behaviour on work devices

While Flutter apps can also be made secure, developers often need to manually connect with native code to access some of these advanced features. It’s definitely possible-but it requires more time and expertise.

• If your app deals in sensitive data or needs enterprise-grade security, native development may be the safer choice

Localization and Global Reach

One of Flutter’s underrated strengths is its excellent flutter localization support. Flutter localization is supported by powerful tools like flutter_localizations and plugins like easy_localization to implement dynamic translation, Right-to-Left layouts and locale-based UI-all within a single shared codebase.

Native platforms have deeper OS integration for localization like language-specific system prompts, pluralisation and automatic OS-level switching.

• Use Flutter for faster multilingual rollout across platforms
• Use Native if you require tight integration with OS-level language settings

Product lifecycle & maintainability

Flutter’s shared codebase simplifies regular updates, bug fixes, and feature rollouts. For rapidly evolving products with changing requirements, this means quicker iteration and lower maintenance overhead. Native apps, though are more complex to maintain (two codebases), benefit from strong OS alignment and long-term ecosystem stability.

Here is a more comprehensive table to help you decide which one to opt for basis your preference:

UI/UX Customization & Responsiveness

Flutter provides full control over every pixel, allowing rich custom UI and animations across platforms. Apps can achieve consistent, high-performance UI with 60–120 FPS.

But native platforms deliver the most authentic look and feel, with out-of-the-box support for gestures, animations and system-level interactions that feel instantly familiar to users.

• Choose Flutter for consistent cross-platform UI
• Choose Native when UI fidelity and user expectations matter most

Integrating OS specific features

One of the challenges of cross mobile development is integrating the latest features released with new OS versions. Implementing native features like iOS live activities or those that require device hardware access like bluetooth or system level sharing , is generally easier with native development as you’re working directly with the platform SDKs.

Flutter supports many of these features through plugins or platform channels, but you may need native code for the latest or more complex integrations. Flutter is flexible, but native development gives you immediate access to the latest OS features as soon as they’re released.

So where Native still have an edge?

There are specific scenarios where native app development is still the better fit:

• Deep platform integrations (e.g., Live Activities, Foreground Services, Siri kit, ARKit)
• Performance-intensive apps (e.g., 3D games)
• Use of niche or proprietary SDKs
• Hardware control (e.g., MDM, custom peripherals)

Native development gives you direct access to all system APIs and features without workarounds.

Current state of Flutter

Before we wrap up, let’s address a question that’s been floating around due to some recent rumors. Despite the rumors, Flutter is very much alive and kicking. Backed by Google, it has had steady releases throughout 2024–2025 and is expanding into web, desktop and embedded platforms.

Recent updates have focused on:

• DevTools improvements
• AI-assisted development (via Gemini)
• Performance enhancements (Impeller engine)

According to GoodFirms, it’s the #1 choice among cross-platform developers, with ~46% usage in 2023.

Digital One Agency reports over 2.8 million monthly developers and some 600,000 published Flutter apps as of early 2025 which clearly indicates Flutter adoption is growing, especially in enterprise and emerging markets like India and Southeast Asia.

Conclusion

Flutter is not just surviving-it’s maturing into a unified development toolkit. In 2025, for mobile app development is one of the best cross-platform frameworks. With Flutter and React Native, businesses no longer need to build and maintain two separate codebases.

To help you make a decision, here’s a quick comparison of Flutter and Native development across all the factors we discussed above. You can use this as a reference to match your tech choice with your app’s goals, timeline and complexity.

The debate of Flutter vs Native app development in 2025 doesn’t have a one-size-fits-all solution. It depends on:

• Target audience and platforms
• App’s performance and security requirements
• Team expertise, skills and timeline
• Plans for scalability or web/desktop expansion

Still not sure about what’s best for your product?

If you're exploring your mobile app development solutions and evaluating Flutter mobile app development versus traditional native stacks, our expert team at TO THE NEW can help assess your technical and business needs and choose the right path for your product’s success. Reach out to us today.

Introduction

How we build digital experiences is changing faster than ever before. Drupal, once widely known as a sturdy monolithic CMS, has now evolved into a decoupled CMS that fully embraces headless architectures. This shift gives frontend developers the ability to build richer, faster, and more dynamic user interfaces using frameworks like React, Next.js, and Vue. The decoupled approach has unlocked a new level of flexibility - but the game-slash-changer is when we introduce artificial intelligence (AI) into the equation.

AI is no longer a buzzword for tomorrow; it is actively transforming frontend development today. From adaptive content presentation and intelligent searching to predictive interfaces, AI is revolutionizing user interaction with websites and apps. When combined with Drupal’s API-first philosophy and robust backend, the possibilities for creating AI-powered digital experiences are limitless.

In this article, we will talk about how headless Drupal frontends powered by AI will build the future of digital experience - what is achievable, what solutions you can use today, and what challenges developers should keep in mind.

Why Headless Drupal is the Future of Frontend

Drupal’s heritage as a powerful content management system remains unmatched, but today’s users demand faster, more interactive, and more personalized experiences. That’s where headless Drupal shines, separating the backend from the frontend to empower developer creativity and performance.

[You may like reading: Content Management in the age of Customer Experience Management]

Key advantages of adopting a Drupal decoupled CMS approach include:

• Decoupled architecture using React, Next.js, Vue, Angular
• API-first strategy providing freedom for frontend developers to innovate
• Faster SPAs, PWAs, and omnichannel content delivery for consistent digital experiences

AI Meets Drupal: Opportunities in Frontend

Headless Drupal delivers flexibility, but AI-driven Drupal frontends elevate it further—making digital experiences smarter, more responsive, and adaptive to real-time user behavior.

Examples of AI personalization in Drupal frontends include:

• AI-driven personalization: Adaptive UIs that respond to user engagement
• Natural Language Processing (NLP): Smart search and voice-based interfaces
• AI performance enhancement: Automated image compression, code optimization, and accessibility improvements
• Predictive UX: Interfaces that suggest the next action or content before the user clicks

This integration of AI with headless Drupal not only improves functionality but also ensures future-proof, intelligent Drupal frontends.

Real-World Use Cases

The real value of AI in Drupal frontend development becomes clear when applied to practical implementations. Organizations are already enhancing customer engagement, accessibility, and conversion rates through AI-enabled features.

Examples include:

• AI chatbots integrated with headless Drupal frontends for customer service
• AI-powered recommendation engines driving eCommerce personalization in Drupal
• Multilingual websites leveraging AI translation and localization for global audiences
• Predictive content delivery, serving the right content to the right user at the right moment

Tech Stack & Tools to Explore

Building AI-powered Drupal frontends is already a reality with the right technology stack. By combining Drupal’s decoupled CMS model with modern frontend frameworks and AI integrations, developers can deliver next-generation experiences.

Essential tools include:

• Drupal JSON:API or GraphQL for data retrieval
• Next.js + AI APIs (OpenAI, Hugging Face, LangChain) for intelligent frontends
• AI-driven testing tools (e.g., Cypress + AI test generators)
• Accessibility tools powered by AI (axe-core + ML integrations)

Partnering with an experienced Drupal consulting team can help enterprises choose the right stack, implement AI integrations, and accelerate digital transformation.

Challenges & Considerations

While the integration of AI with Drupal frontends is powerful, it comes with challenges that developers and enterprises must address responsibly:

• Data privacy and GDPR compliance: Ensuring user data protection across AI-driven workflows
• Balancing AI automation with developer control: Maintaining flexibility without losing human oversight
• Ethical AI: Guaranteeing that AI outputs remain unbiased, ethical, and transparent
• Performance trade-offs: Managing infrastructure impact when integrating heavy AI features

Many of these challenges can be addressed by engaging expert Drupal consulting services, ensuring compliance, performance optimization, and seamless AI adoption.

Conclusion

Headless Drupal and artificial intelligence together are not just a step up in technology - it's a revolution of how we think about and deliver digital experiences. With the powerful backend of Drupal coupled with AI-driven frontends, the possibilities are limitless as developers get to create websites and apps that are not only faster and more responsive but also smart, personalized, and future-proof.

While AI keeps on evolving, Drupal frontends in the future will only see more possibilities - whether predictive interfaces, real-time personalization, or omnichannel delivery without seams. As a frontend developer, this is exactly the moment to start dipping your toes in AI integrations and getting at the forefront of that change.

The future of Drupal is not headless, it's intelligent.