Learn, Grow and DO THE NEW

Introduction

Generative AI (GenAI) has become one of the most exciting innovations in recent years. From chatbots and copilots to content generators, organizations across industries are integrating GenAI into their products. While the opportunities are endless, one critical question remains: How do we ensure quality when testing GenAI applications?

Unlike traditional software, GenAI systems are non-deterministic. The same input can produce different outputs depending on the context, prompt, or even model version. This makes testing GenAI a complex but essential task.

Testing GenAI Applications vs Traditional Software Testing

Traditional applications have well-defined requirements and predictable outputs. For example, a calculator app should always return 4 when you input 2+2. GenAI applications, however, don’t work that way. GenAI applications introduce complexities that go far beyond conventional QA practices.

Traditional software systems operate in a predictable manner, making it relatively straightforward to design test cases, validate outputs, and maintain consistency across versions. GenAI, however, demands a new lens for testing because of its non-deterministic nature, reliance on subjective evaluation, and constant model evolution.

Key differences include:

• Non-determinism: Outputs may vary even with identical prompts
• Subjectivity: “Correctness” of responses often depends on context and user expectations
• Bias and fairness risks: AI may unintentionally generate harmful, biased, or inappropriate content
• Scalability challenges: Testing requires evaluation of outputs across vast combinations of prompts and edge cases

The table below highlights the key differences between traditional testing and GenAI application testing:

Challenges in Testing GenAI Applications

• Defining “Correct” Output: There may be multiple acceptable responses. For example, a GenAI travel assistant could suggest different itineraries for the same query
• Handling Bias and Safety: GenAI models can unintentionally produce offensive or biased outputs. QA teams need to test for inclusivity, fairness, and ethical use
• Evaluating Performance Across Contexts: Models must be tested for different domains, user personas, and languages—an enormous testing surface
• Maintaining Consistency: Frequent model updates can change outputs, making regression testing more complex than in traditional systems
• Scalability of Testing: Manual validation is not feasible for thousands of prompts. Automated evaluation pipelines are essential

Best Practices for Testing GenAI Applications

• Define Clear Quality Metrics:
  • Accuracy (fact-checking outputs)
  • Relevance (is the response useful to the user?)
  • Coherence (is the response logical and well-structured?)
  • Safety (no harmful or biased outputs)
• Adopt Human-in-the-Loop Testing: Combine automated pipelines with human review for subjective aspects like tone, creativity, or ethical sensitivity
• Build a Test Prompt Library: Maintain a large, evolving set of test prompts covering:
  • Happy paths
  • Edge cases
  • Adversarial or malicious inputs
  • Domain-specific use cases
• Leverage AI to Test AI: Use AI-based tools for automated evaluation, clustering outputs, and detecting anomalies at scale
• Continuous Monitoring in Production: Implement monitoring and feedback loops to catch real-world failures, bias, or hallucinations
• Collaborate Across Teams: QA, data scientists, and domain experts must work together to define acceptable outcomes and align on ethical standards

Key Tools and Frameworks for Testing GenAI Applications

Testing GenAI apps is not just about running scripts, it needs special tools that can deal with unpredictable answers, changing outputs, and even risks like bias or harmful content. Over time, different tools have come up to help QA teams test GenAI more effectively.

Here are some key ones:

• Model Evaluation Tools: OpenAI Evals, HELM, and LM Evaluation Harness help teams compare models, track progress, and find weak spots.
• Prompt Testing and Guardrails: Tools like LangChain testing, Guardrails AI, and Promptfoo ensure answers are relevant, safe, and follow the right structure.
• Quality and Reliability: Tools such as TruLens and DeepEval check if responses are accurate, logical, and useful, while also allowing human review for tricky cases.
• Bias and Safety Checks: Tools like Fairlearn, Aequitas, and Google’s Perspective API help test for fairness, inclusivity, and safe language.
• Security and Stress Testing: Tools like TextAttack, IBM ART, and Garak test how secure and robust the system is under adversarial prompts or attacks.
• Monitoring in Production: Platforms like LangSmith, Arize AI, and WhyLabs track outputs in real-world use, spotting issues like bias, drift, or hallucinations early.

Introduction

When the Centers for Medicare & Medicaid Services (CMS) released the Interoperability and Prior Authorization Final Rule (CMS-0057-F) in early 2024, it marked one of the most significant regulatory shifts in healthcare data exchange and utilization management in recent years. The rule aims to enhance the electronic exchange of health information while streamlining prior authorization processes. This rule is part of CMS’s ongoing efforts to make data flowing through the healthcare system, particularly consumer data, more interoperable. By mandating the use of standardized APIs and imposing stricter timelines for prior authorizations, CMS-0057-F aims to reduce administrative burdens, enhance patient access to data, and promote a more interconnected healthcare ecosystem.

In this article, we’ll explore the rule's core elements, requirements of payers in the new rule, potential benefits for healthcare providers and patients, and highlight both challenges and opportunities it presents.

Understanding CMS-0057-F: The Basics

For decades, prior authorization (PA) frustrated both patients and clinicians. And the main reasons were lengthy approval times and opaque denial reasons, while the lack of seamless data sharing between payers and providers hampered coordination.

CMS-0057-F applies to a range of "impacted payers," including Medicare Advantage (MA) organizations, state Medicaid and Children’s Health Insurance Program (CHIP) Fee-for-Service (FFS) programs, Medicaid managed care plans, CHIP managed care entities, and Qualified Health Plan (QHP) issuers on the Federally Facilitated Exchanges (FFEs).

CMS-0057-F builds on earlier interoperability efforts, mandating new timelines for PA decisions and requiring a suite of modern APIs built on HL7® FHIR® standards. In essence, the rule aims to make data flow more freely and decisions arrive more quickly - two things patients and providers have been demanding for years.

Introduction

The world of mobile technology is evolving at a stunning pace and iOS devices remain at the forefront of innovation, style and user-friendliness. In 2026, iOS app development leverages powerful new tools, modern programming languages and advanced design standards. Known for their unmatched security and privacy features, Apple devices continue to set the benchmark for trusted digital experiences. Developing an iOS app in 2026 is not only a rewarding technical endeavor but also a strategic business move. Whether you’re exploring how to develop iOS apps or simply want to understand what an iOS application is, this guide will help you navigate every stage and bring your ideas to millions through the sleek interface of the iPhone and iPad.

For many organizations, iOS apps have become a cornerstone of broader digital transformation services, enabling businesses to connect with customers and optimize operations. Whether you’re building on your own or leveraging expert iOS mobile app development services, working with the right mobile app development services provider ensures your product is designed for performance, security, and scalability. This comprehensive guide will walk you through everything you need to know to successfully plan, design and launch an iOS app that stands out in 2026.

State of the iOS App Market in 2026

Apple’s ecosystem is more powerful than ever. With iPhones, iPads, Apple Watches and even the new Apple Vision Pro, iOS apps are not just smartphone companions - they’re now immersive, multi-platform experiences.

Here’s why iPhone app development is still a top choice in 2026:

• Higher revenue per user compared to Android
• Premium global audience with high spending behavior
• Easier testing and deployment with fewer device types
• Deep integration with AI, health, AR and spatial computing

Whether you’re planning to build a fitness tracker, productivity tool or game, the iOS market offers massive ROI potential. Many businesses begin by asking how to make an iOS application or what is the easiest way to make an iPhone app - a reflection of the platform’s reputation for simplicity, scalability and strong user adoption. This is why iPhone app development continues to be one of the most sought-after areas in mobile technology.

Native vs. Hybrid: What Should You Choose in 2026?

You’ve probably heard of Flutter, React Native or other cross-platform tools. While they’re useful, native iOS development is often the better long-term bet, especially in 2026.

So to capture the elegance and precision “Apple” is known for, build it natively. For those learning how to code an iPhone app, choosing native tools like Swift and SwiftUI ensures maximum performance and long-term flexibility.

Can You Develop iOS Apps on Windows in 2026?

This is one of the most common questions:
“Can you develop iOS apps on Windows?” or
“How can I develop iOS apps on Windows?”

The short answer? Not natively.

Apple’s development tools - especially Xcode and the iOS Simulator - are tightly integrated with macOS. That means you can’t install Xcode directly on Windows. But in 2026, there are more workarounds and tools than ever before to help Windows users get started.

Thanks to cloud platforms, cross-platform frameworks and remote access tools, it’s entirely possible to code, build and even test iOS apps on Windows - though you’ll still need access to macOS at some point in the process.

Why You Can’t Natively Develop iOS Apps on Windows

One of the biggest roadblocks for Windows users is that Apple’s official iOS development tools only run on macOS. This includes:

• Xcode – the primary IDE for iOS development
• Interface Builder – for designing iOS UI visually
• iOS Simulator – to test your app without a physical device
• Transporter or Xcode Organizer – for uploading to the App Store

These tools are essential to:

• Build and sign final .ipa files
• Access and run the iOS Simulator
• Submit your app to the App Store

So, while you can write code using other tools or frameworks, you’ll still need macOS to finalize and publish your app.

Your Options to Develop iOS Apps on Windows in 2026

Here’s a breakdown of tools and methods you can use to develop iOS apps on Windows, along with their pros and cons:

So, while you can start developing iOS apps on Windows, you’ll still need macOS at some point - especially for running the iOS Simulator, compiling the final build or submitting to the App Store.

If you're serious about iOS application development, investing in a Mac (or using a cloud-based solution) will save you significant time and effort.

iOS App Development Best Practices in 2026

Building a successful iOS app is about more than writing code - it’s about programming an app for iOS thoughtfully, crafting experiences that delight users and drive business growth. Businesses seeking how to develop iOS apps in 2026 should focus on strategic planning, user experience and scalability rather than only the technical steps. Here’s what leading companies prioritize:

• User-Centric Design: A well-designed iPhone app improves engagement, retention and conversion. Companies today prioritize designing iPhone apps with accessibility, intuitive navigation and aesthetic appeal in mind. A thoughtfully crafted UI ensures that application development for iPhone feels seamless to users, while adhering to Apple’s Human Interface Guidelines guarantees a smooth and consistent experience.
• Performance Optimization: Minimize load times, optimize animations and manage memory efficiently to reduce crashes, enhance responsiveness and improve battery usage.
• Scalable Architecture & Modular Code: Design modular backend systems and APIs that can grow with your app’s features, future expansion and increasing user base. Adopt code organization patterns to simplify maintenance and updates.
• Code Quality & Maintainability: Write clean, modular and well-documented code. Use version control, automated testing and CI/CD pipelines to maintain consistent quality across teams and releases.
• Testing & Debugging Practices: Regularly perform unit, integration and UI tests. Leverage Xcode tools and simulators to identify issues early, preventing critical bugs in production.
• App Monitoring & Analytics: Implement tools for crash reporting, performance tracking, and user behavior analytics to continuously improve app quality and measure real-world performance effectively.

Professional teams provide custom iOS application development services, helping brands tailor features, scale apps and reduce long-term costs.

What It Takes: Cost, Time and Skills to Build an iOS App

Before you start, understand the basic costs and time investments involved in iOS app development:

Whether you want to build an Apple app as a simple utility or a complex enterprise tool, planning costs and timelines upfront prevents delays.

Also factor in ongoing updates, backend management and possible design/QA outsourcing. Modern development teams often rely on cloud and DevOps services to streamline builds, deployments, and monitoring. By incorporating DevOps automation services, businesses can reduce release cycles, maintain continuous integration, and deliver updates faster - keeping pace with evolving user expectations.

• Apple Dev Fee: $99/year
• Ongoing updates and feature improvements
• Possible design/QA outsourcing

[You may like reading: Understanding iOS app Development cost: Factors governing the estimations]

Top Strategies for iOS App Success in 2026

Launching a successful iOS app is a strategic business decision. Leading companies in 2026 prioritize aligning app features with market needs, leveraging Apple’s ecosystem, and innovating continuously to maximize user engagement and long-term growth.. Leading organizations in 2026 prioritize sustainable growth and meaningful user experiences over quick launches. Key insights include:

• Prioritize ROI and Market Fit: iOS apps provide access to premium audiences who are more likely to engage and spend. Aligning app features with target user behavior maximizes adoption and revenue. Analyzing user patterns and market trends ensures your app delivers measurable business outcomes.
• Leverage the Apple Ecosystem: Successful apps extend beyond the iPhone, integrating seamlessly with iPads, Apple Watches and Apple Vision Pro. By designing experiences across devices, businesses can deliver immersive AR, spatial computing and multi-device interactions that enhance engagement and brand perception. Harnessing Apple’s tightly integrated hardware and software ecosystem enables unique, high-quality user experiences that competitors may find difficult to replicate.
• Focus on Innovation, Not Just Implementation: Incorporating emerging technologies such as AI or advanced AR features differentiates your app in a competitive market, leading to higher retention and user satisfaction. Continuous innovation positions your app as forward-thinking, helping your brand stay relevant as new iOS capabilities emerge. Some teams even add gamification while developing iOS applications, while others rely on custom iOS application development services to tailor unique features that boost retention and long-term loyalty.
• Ensure Security & Privacy: Prioritize secure authentication, data encryption and privacy-first design. Protecting user data builds trust, enhances app credibility, and aligns with Apple’s stringent security standards.
• Accessibility & Inclusivity: Design your app to be accessible to all users, including those with disabilities. Following Apple’s accessibility guidelines not only broadens your audience but also enhances user satisfaction and compliance.
• Engage Professional Expertise: Using professional custom iOS application development services ensures your app is optimized for performance, security and scalability, reducing long-term maintenance costs. Partnering with experts also accelerates development timelines and allows your team to focus on strategic growth initiatives.
• Iterative Growth Strategy: Rather than building a fully-featured app upfront, adopt an iterative approach - launch MVPs, gather user feedback and add features gradually - to control costs, validate product-market fit and reduce risk. This approach encourages continuous learning and adaptation, making your app more resilient to market changes.
• Cross-Platform Decisions as Strategic Choices: When considering Windows or hybrid tools, make decisions strategically - balancing cost, timeline and user experience - rather than focusing solely on technical limitations. Viewing platform choices through a business lens ensures your resources are invested where they deliver the highest impact.

Post-Launch Considerations: Marketing, User Acquisition and ASO

Launching your iOS app is just the beginning. To ensure long-term success, focus on post-launch strategies that drive visibility, engagement and growth:

• App Marketing: Promote your app through social media, email campaigns, content marketing and paid ads to reach your target audience effectively. Spread awareness to your ideal users.
• User Acquisition Strategies: Optimize onboarding, referral programs, retention campaigns and gamification elements to attract and retain high-quality users. Convert visitors into loyal, active users.
• App Store Optimization (ASO): Improve your app’s discoverability on the App Store using keyword optimization, compelling descriptions, screenshots and positive reviews. Maximize visibility for higher downloads.

By combining iOS app development best practices with these post-launch strategies, you can maximize user engagement, retention and ROI in the competitive iPhone app development ecosystem.

Conclusion: Start Small, Launch Smart

Developing an iOS app in 2026 is both an exciting technical challenge and a strategic opportunity to reach millions of premium users. Whether you choose in-house development or rely on professional mobile app development services, the right strategy ensures long-term success. By following the process, you can successfully make an app on iPhone that delights users and meets business goals. Once the core app works smoothly, you can expand functionality with backend services, APIs or additional features to create a more complex, professional-grade product. With proper testing, iteration and adherence to Apple’s guidelines, your app can deliver a seamless experience, achieve App Store approval and stand out in the competitive iOS ecosystem. In short, when you build an Apple app with the right foundation, it not only drives your digital transformation but also scales for future growth, positioning your brand strongly in the digital marketplace.

Event Overview

Recently, we got an opportunity to attend TribeQonf 2025 in Bengaluru, which was all about Testing in the Age of AI. It was a thrilling event with more than 600 software testers, enthusiasts, and industry leaders from all corners of India. The conversations centered around a powerful question: How is AI transforming the world of quality engineering?

The two-day event organised by The Test Tribe featured several different engaging talks, panel discussions, practical sessions, and demonstrations of the latest tools, also addressing several topics like responsible use of AI, best practices of prompt writing, and how the role of testers may change with the upcoming technology.

What Everyone was Talking About

Human-AI collaboration

One of the biggest takeaways from the event was the evolving relationship between testers and AI tools.AI will not replace testers but will assist them in working more efficiently. The emphasis was on the ability to harness both AI’s speed and scale and human insight. The testers continue to ensure the much-needed critical thinking and context that AI does not have. AI can do the heavy lifting, scanning logs, identifying patterns, generating test cases - but it’s still the human mind that provides critical thinking, domain context, and decision-making. The future of QA isn’t about man versus machine, rather it's about man with machine.

Responsible Uses of AI

A large number of sessions have emphasized on the need of ethically driven AI technologies, ensuring transparency, fairness and avoiding emergence of new risks using AI technologies.

The Rise of Prompt Engineering

Since AI-powered testing tools are strongly based on prompts, effective prompt writing became a defining skill that was widely discussed. Testers are now expected to communicate clearly with AI tools to extract accurate results, making prompt writing a core competency.

Automation Evolution

The debates concerning how the automation landscape is changing (autonomous testing agents and test case generation via AI, etc.) were eye-opening.

Testing Careers Are Changing Fast

Another aspect that was discussed by speakers is the fact that the role of the tester is evolving and it is expected to promote lifelong learning and continuous change with AI disrupting the environment.

Testers are no longer just viewed as users of tools to identify bugs, they are now being viewed as: quick responder engineers, tools analyzer, moral artificially intelligent reviewer, data quality advocates. This is a movement going on, and it is establishing new directions in quality engineering.

Busting the Myths

The testers will be substituted by AI.

Reality: AI could help, but it has no context, logic, or compassion as a human being.

The AI tools do not require monitoring.

Reality: They require close guidance, observation, and reinforcement.

We are too early to dig into AI tools.

Reality: Quite a number of teams are experimenting now and achieving valuable results.

Everything will be alright with AI.

Reality: AI is not always right. Even the testing of AI-generated responses is emerging as a major QA responsibility.

What Are We Taking Back With Us

Shifts of paradigm are happening in the QA role. Get prepared to learn, forget, and evolve.

There were many insightful moments, but a few core lessons stood out:

• AI + Human = Better Together Let the heavy lifting be done by AI; however, humans remain in control
• Domain Knowledge is Gold: AI does not possess the same level of intelligence around your users or product as you would
• The Art of Learning Prompting: The way you write prompts to AI is becoming an essential skill
• Don't Buy What People Sell You: No, the AI will not perform all the tests. Rather, it can make your testing better, provided you use it properly

Our greatest lesson is simply that AI is a supplement, not a simple workaround. It was nice to cut down the hype and hear actual stories from teams using, trying, failing fast, learning, and then getting better at AI. We returned with a stronger faith in:

• The significance of the domain expertise
• The potential of the human-AI collaboration
• The necessity to evolve and keep up with the tech

What do We Plan to do Next?

We came back with practical goals:

• Get hands-on AI testing tools
• Write more effective prompts
• Familiarize ourselves more on our product and domain expertise this cannot be substituted by AI
• Continue the discussion of the morality of AI in QA

Final Thoughts

TribeQonf 2025 was not any regular conference; it was an opportunity to draw a breath, recollect and recalibrate the way we look at the world of quality engineering and testing. With changes in the development of software because of AI, our role as testers is also changing. We are not being left behind but are entering into a new chapter.

If you’re a tester, now’s the time to lean in. Ask questions. Experiment. Learn how to work with AI, not against it. Because in the age of intelligent tools, the most powerful asset is still the human mind.

Introduction

In today’s era, Cloud computing has become the most popular choice for businesses and organizations, as it is the most optimal and efficient way to deploy, manage, and use applications in a secure and isolated environment with minimal downtime and high availability. But with rising Cloud usage,there is an increase in online cyber attacks as well, and hackers around the world are continuously looking for vulnerabilities in applications and loopholes in infrastructure to extract data from data - as we all know, data is an asset these days.

With the rising threats and vulnerabilities, we can use AI to prevent these attacks in an smarter, efficient and faster way to detect the attacks and threats. With AI we can use its intelligence to detect patterns of malicious activity and misconfigured infrastructure and automate the remediation process for the vulnerabilities.

How AI is Transforming Cloud Security

Real-time Threat Protection

Real-time analysis is the most significant part of security as it enables the cloud environments to detect, respond to threats before they can cause damage to the infrastructure. AI uses machine learning algorithms to analyze real-time data, patterns, and learnings from past data feeds. Like analysis of users, login patterns, login from new devices, and login at odd hours from unusual locations. AI can analyze the logs from services like Cloud Trail and Azure Monitor to find patterns in it.
Result: Faster identification, reduced dwell time, and minimized damage.

Predictive Analytics

Predictive analysis in cloud security is its ability to predict potential threats before they affect the infrastructure. Predictive analysis uses the past and real-time data to recognize the patterns and based on the prediction, it suggests the action needed to secure the environment or even act on the basis of the remediation integrated based on actions. We can use AI models to classify the data as well in order to prevent it from blocking legitimate access or patterns. AI can analyze millions of data points that humans cannot read or analyze quickly.
Imagine: predicting a DDoS attempt hours before it starts. That’s the power of AI.

Global Threat Integration

AI can help security systems stay ahead by pulling in threat intelligence from all over the world—things like cybersecurity community alerts, dark web activity, and government warnings. By analyzing this data in real-time, it can automatically update detection rules and fine-tune security policies to match the latest threats. This helps protect systems from issues that are being reported globally, often before they can cause any real damage. It will check global databases and security issues around the globe to verify the issues.
Enterprises get proactive global defense, not just local protection.

Automated Response and Remediation

AI can take a lot of the manual work out of handling security incidents, helping teams respond much faster. It can automatically isolate infected systems, block suspicious IP addresses, close vulnerable ports, and take other protective actions - often without any human input. This not only cuts down the time it takes to react but also helps reduce the overall damage from a breach. Based on behavior patterns, AI can decide whether to block or allow access, keeping the environment secure and adaptive.
AI can automatically create runbooks or remediation steps based on the issue and act on it based on its activated data sets. Cuts response times from hours to seconds, reducing breach impact dramatically.

Smarter User Authentication

User authentication can be enhanced by analyzing user behavior and identifying unusual login anomalies or logging from systems which are unidentified. For example, AI can detect unusual login patterns or access attempts from unfamiliar locations, suspicious devices and flagging them as potential threats.
This helps in preventing unauthorized access to the cloud account and block the access of unauthorized users before causing any damage to the system. Think of it as multi-factor authentication powered by intelligence.

Protecting APIs from Abuse

APIs play a crucial role in cloud and AI infrastructures, facilitating communication between services and enabling seamless integration. Management of APIs become har sometimes with human intervention so AI comes into play acting as a safe guard for API usage.When APIs are overutilized or lack proper controls, they can become targets for attacks such as denial-of-service (DoS), leading to system slowdowns or crashes. We can implement strict rate limiting and robust authentication for all API endpoints and let AI analyze the behavior and patterns in API usage and the common error rates encountered. Combine AI-driven anomaly detection with traditional security measures for a comprehensive defense strategy. APIs abuse can be easily prevented by using AI’s pattern recognition and predictive analysis before overuse of API endpoints.
Prevents slowdowns, crashes, and shadow API risks.

Compliance Made Easy with AI

Compliance auditing and standards are equally important in the Infrastructure because this ensures that the environment follows the rules and compliances as per the global enterprise standards. With the help of AI we can scan and verify compliance with standards such as PCI , and others. AI models maintain detailed logs of security events and responses to facilitate audits. This will reduce human intervention and increase efficiency as well for Engineers and also make Infrastructure secure as per Enterprise security standards.
Less manual effort, more confidence during audits.

AI Tools for Cloud Security

Microsoft Defender for Cloud

Microsoft Defender for Cloud is a tool designed to protect the cloud infrastructure it provides a security management framework for Cloud, Hybrid, and even on-premises servers. It offers threat analysis, anomaly detection, and a misconfigured environment. It is most compatible with Azure. It can be integrated with Microsoft Sentinel and Azure OpenAI Service for log analysis.

Amazon GuardDuty (AWS)

Amazon GuardDuty Cloud is a tool designed to protect the cloud infrastructure it provides a security management framework for Cloud for AWS. It uses ML and anomaly detection for threat identification from VPC flow logs, CloudTrail, and DNS logs. It can be integrated with AWS bedrock service.

Introduction

Mobile apps are no longer just utilities, they're our wallets, health records, IDs, and entertainment hubs. With such a wide scope of use, mobile app security is now a make-or-break element for user trust and business resilience. Yet, in many organizations, security still enters the picture only after the core functionality is built. The result? Vulnerabilities, leaks, and costly patches that inflate the iOS app development cost and jeopardize the entire product lifecycle.

This article explores the evolving mobile threat landscape, reveals why conventional security practices fall short, and lays out a forward-looking, secure-by-design approach for mobile app development-especially for iOS app development services and Android platforms. Security is not just a technical necessity but a pillar of your organization’s digital transformation strategy.

Why mobile app security fails in modern app development

Despite an explosion in security tools and SDKs, mobile data breaches are increasing. A 2024 report by Mobile Security Watch revealed that 65% of financial and health apps store sensitive data in unencrypted form. Often, the issue isn’t technical capability-it’s organizational mindset.

Common reasons security fails today:

• Security checks are delayed until the QA stage.
• Developers hardcode secrets due to tight deadlines.
• Teams use outdated dependencies without regular audits.
• Business pressure prioritizes features over protection.

Evolving threat landscape: Smarter attackers, bigger stakes

Attackers are becoming faster, more resourceful, and increasingly automated:

• AI-based reverse engineering tools can now decompile apps and locate weak points within minutes.
• Frida-based runtime manipulation enables live code interception, bypassing app logic.
• Credential stuffing via leaked app secrets and tokens is on the rise.
• Even state-sponsored spyware is leveraging mobile zero-days.

This is no longer a game of hiding keys or writing clever if-checks. The security landscape demands architectural reform. Ignoring it can lead to breaches that cost more than the entire iOS app development cost itself.

Secure-by-design: The future of mobile app security

Moving from reactive fixes to a proactive security-first approach means embedding security into every development phase. Digital engineering teams must adopt security-first principles early in the lifecycle to mitigate risks at scale.

1. SSL/TLS pinning done right

Problem: MITM attacks and forged certificates.

Solution: Certificate pinning binds your app to trusted certificates or public keys.

• Android: Use NetworkSecurityConfig for pin lists, and host keys remotely for rotation.
• iOS: Implement URLSessionDelegate with key comparison logic and fetch pins from secure sources.
• Always fail securely-never allow fallback to an unverified cert.

2. Jailbreak/root detection as a layered defense

Problem: Compromised devices run arbitrary code.

Solution: Detect root/jailbreak via known binaries, unsafe syscalls, and Frida traces.

• Android: Look for Magisk, su binaries, and modified build.prop files.
• iOS: Scan for /Applications/Cydia.app, substrate files.
• Obfuscate checks using ProGuard (Android) or LLVM (iOS).
• Integrate Play Integrity API (Android) or DeviceCheck (iOS).

3. Store nothing in plaintext - Secure data storage

Problem: XML/Plist data is often readable by attackers.

Solution:

• Android: Use EncryptedSharedPreferences with keys in Android Keystore.
• iOS: Use Keychain for sensitive info and enable file protection options.
• Automate audits to spot usage of putString or NSUserDefaults.set with secrets.

4. Avoid hardcoded secrets

Problem: Reversed apps expose API keys, access tokens, etc.

Solution:

• Fetch secrets post-authentication from a secure backend.
• Use Android Keystore or iOS Secure Enclave to store session credentials.
• Avoid using Info.plist or constant files for sensitive data.

5. Manage permissions and component exposure

Problem: Overexposed components increase attack vectors

Solution:

• Apply the principle of least privilege.
• Android: Use android: exported=false wherever possible.
• iOS: Enable granular access and validate runtime authorization requests.

6. Internal over external storage

Problem: External storage is world-readable.

Solution:

• Android: Use getFilesDir() and implement Scoped Storage
• iOS: Stick to the app sandbox and enable Data Protection.

7. Validate input, sanitize queries

Problem: SQL injection and command injection.

Solution:

• Use parameterized queries and ORM frameworks.
• Avoid shell execution APIs.
• Validate all user input with regex and strict type checks.

8. Prevent visual leakage

Problem: Screenshots capture sensitive data.

Solution:

• Android: Use FLAG_SECURE for sensitive activities.
• iOS: Monitor UIApplication.userDidTakeScreenshotNotification and blur sensitive views.

Security as a product differentiator

In an age where every app claims user-first innovation, security can be your competitive edge. Fintech, healthtech, and government apps are increasingly judged on how securely they handle data.

Examples:

• Signal gained millions of users because of its security-first reputation.
• Apps like LastPass suffered due to perceived negligence in encrypting user vaults.

Building secure apps boosts user trust, app store ratings, and regulatory compliance.

Who owns mobile security? Developers or leadership?

This is not just a developer’s job-it’s a cross-functional imperative:

• Product Managers must prioritize security stories in sprints.
• Engineering Managers must invest in code scanning tools and secure CI/CD pipelines.
• Business leaders must understand that security delays now prevent financial loss later.

Adopt a threat modeling approach in every release and involve security from design through deployment.Security integration into cloud and DevOps workflows—like CI/CD pipelines—is key to reducing attack surfaces.

Looking ahead: Mobile app security, now and beyond

• OS-level security APIs will offer better isolation but require developers to keep pace.
• Privacy regulations (India DPDP, EU GDPR) will demand more secure data handling.
• Zero-trust mobile architecture will evolve, with ephemeral tokens and decentralized identity.
• Expect AI-enhanced malware that learns how to bypass static checks-making runtime detection critical.

Conclusion

Mobile app security is not a checklist-it’s a mindset. To truly protect user data and build trust, organizations must shift from reactive patching to proactive protection. From encrypting storage and validating runtime environments to educating teams and integrating security into CI/CD, every step counts. This is especially critical for custom mobile app development teams working in regulated sectors like healthcare, finance, and government.

Security is no longer optional-it’s your moat, your brand promise, and your best investment.

Need help building secure-by-design mobile apps? Let’s talk . TO THE NEW’s mobile engineering teams specialize in crafting apps with robust, scalable security built into every layer.

Introduction

Agentic AI, autonomous, decision-making systems, is reshaping the way enterprises operate in the digital age. When combined with Multi-Channel Platforms (MCPs), these AI agents do more than automate tasks - they optimize end-to-end business processes, deliver personalized customer experiences at scale, and enable faster, data-driven decisions across multiple touchpoints.

Organizations adopting this approach are not just improving operational efficiency; they are transforming their business models. From reducing manual workload and accelerating time-to-market for new services to enabling hyper-personalized engagement across web, mobile, chat, and voice channels, agentic AI within MCPs creates measurable impact.

This article explores how enterprises can strategically leverage agentic AI with MCPs, highlighting practical benefits, lessons learned, and emerging trends, providing a roadmap for organizations aiming to harness AI as a competitive advantage.

Understanding the Agentic AI

Traditional AI systems were reactive, they responded to queries and performed predefined tasks. Today's agentic AI systems are proactive, autonomous entities that can perceive their environment, make independent decisions, and take actions to achieve specific goals. Think of them as digital employees who never sleep, never get tired, and continuously learn from their experiences.

What makes an AI system "agentic"?

• Autonomous decision-making: Making independent choices based on context and data
• Integrated multitasking: Combining various AI functionalities to handle complex workflows
• Adaptive learning: Continuously improving performance through experience
• Contextual understanding: Deep comprehension of nuanced situations and requirements

The Model Context Protocol: A Game-Changing Standard

The Model Context Protocol (MCP), introduced by Anthropic in November 2024, addresses one of the most critical challenges in AI development: connecting AI models to real-world data and tools. Think of MCP as the "USB-C for AI applications" - a universal standard that enables seamless integration between AI systems and external resources.

Benefits of MCP

Before MCP, developers faced an "N×M problem" - every AI application needed custom integrations for each tool or data source it wanted to access. This created a maze of one-off connections, each requiring separate development, maintenance, and troubleshooting.

MCP solves this by providing:

• Standardized communication: One protocol for all integrations
• Dynamic tool discovery: AI agents can automatically discover and use available tools
• Real-time bidirectional communication: Continuous data flow and action execution
• Secure, controlled access: Robust permissions and monitoring systems

MCP in action: Real-world examples

• Enterprise knowledge management: An AI agent using MCP can simultaneously access your company's Confluence documentation, Slack conversations, CRM data, and GitHub repositories to provide comprehensive insights without requiring separate API integrations for each platform.
• Automated workflow orchestration: A marketing AI agent can check Google Calendar for campaign deadlines, pull performance data from analytics platforms, generate content using design tools, and schedule social media posts, all through standardized MCP connections.
• Development acceleration: Code assistants in IDEs like Cursor and Replit use MCP to access file systems, version control, package managers, and documentation simultaneously, providing contextually aware suggestions and automated refactoring.

[You may like reading: ChatGPT, DeepSeek, Gemini, and GitHub Copilot: Conquer the code with your AI dream team]

The AI agent ecosystem: Powerful tools transforming industries

Multi-agent frameworks

• CrewAI: Specializes in role-based multi-agent systems where different agents have specific responsibilities and collaborate like a human team. Perfect for complex projects requiring diverse expertise.
• AutoGen: Excels in conversational workflows and autonomous code generation. Agents can debate, self-correct, and iteratively improve their output through multi-party discussions.
• LangGraph: Offers sophisticated state management and graph-based workflows, ideal for complex, cyclical processes that require precise control over agent interactions.
• OpenAI Swarm: A lightweight, experimental framework focusing on agent handoffs and coordination, designed for rapid prototyping and simple multi-agent scenarios.

Industry-specific applications

• Healthcare: AI agents are revolutionizing medical administration by processing insurance claims, managing patient records, analyzing lab results, and coordinating care across multiple providers while maintaining HIPAA compliance.
• Financial Services: Automated trading agents analyze market conditions in real-time, execute transactions, and manage risk portfolios. Compliance agents monitor transactions for fraud while customer service agents handle account inquiries 24/7.
• Manufacturing: Predictive maintenance agents monitor equipment health, schedule repairs before failures occur, and optimize production workflows. Quality control agents analyze product specifications and identify defects in real-time.
• Retail & E-commerce: Personalization agents analyze customer behavior to recommend products, inventory management agents optimize stock levels across multiple channels, and customer service agents handle inquiries across various platforms.

Productivity powerhouses

• Automation Platforms: Tools like Gumloop and n8n enable non-technical users to create sophisticated AI-driven workflows that connect multiple services and automate complex business processes.
• Content Creation Agents: Platforms like Jasper, Notion AI, and Synthesia help businesses generate marketing copy, create videos with AI avatars, and maintain consistent brand messaging across channels.
• Development Accelerators: GitHub Copilot, Cursor, and similar tools act as intelligent pair programmers, understanding project context, suggesting code improvements, and even implementing entire features based on natural language descriptions.

Strategic use cases driving business value

Customer service revolution

Modern customer service agents don't just answer questions - they solve problems end-to-end. They can:

• Access customer history across multiple touchpoints
• Process returns and exchanges autonomously
• Escalate complex issues with full context
• Provide proactive support based on usage patterns
• Generate personalized recommendations

ROI Impact: Companies report 60-80% reduction in response times and 40-50% decrease in support costs while improving customer satisfaction scores.

Financial operations automation

AI agents in finance departments are transforming traditionally manual processes:

• Automated Accounts Payable: Processing invoices, matching purchase orders, and handling approvals
• Fraud Detection: Real-time transaction monitoring and risk assessment
• Financial Reporting: Generating insights from multiple data sources and creating executive dashboards
• Compliance Monitoring: Ensuring adherence to regulations and flagging potential issues

Business Impact: Organizations achieve 70-85% reduction in processing time for routine financial tasks and significantly improved accuracy in compliance reporting.

Human resources transformation

HR agents are reshaping talent management:

• Intelligent Recruiting: Screening resumes, scheduling interviews, and matching candidates to roles
• Employee Onboarding: Automating paperwork, coordinating training, and providing personalized guidance
• Benefits Management: Answering policy questions, processing requests, and providing enrollment assistance
• Performance Analytics: Identifying trends, predicting turnover, and recommending interventions

IT Operations Excellence

IT agents are becoming indispensable for modern organizations:

• Automated Incident Response: Identifying issues, diagnosing root causes, and implementing fixes
• Security Monitoring: Continuous threat detection and automated remediation
• Asset Management: Tracking hardware and software lifecycles, optimizing usage, and managing licenses
• User Support: Handling password resets, access requests, and technical troubleshooting

Challenges & Lessons Learned

While agentic AI offers transformative potential, organizations must navigate key pitfalls:

• Data privacy & compliance: Autonomous agents handle sensitive data; rigorous governance and encryption protocols are critical
• Over-automation risks: Not all processes should be fully automated. Balancing human oversight with AI execution ensures quality and accountability
• Unclear governance & roles: Defining ownership, escalation paths, and ethical boundaries for AI agents prevents operational conflicts and risk exposure

Implementing Agentic AI: A step-by-step approach

Phase 1: Foundation building

• Identify repetitive, rule-based processes perfect for automation
• Implement simple chatbots and task-specific agents
• Establish data governance and security protocols
• Train teams on AI collaboration best practices

Phase 2: Integration and expansion

• Deploy MCP-compatible systems for standardized tool access
• Create multi-agent workflows for complex processes
• Implement monitoring and optimization systems
• Develop custom agents for industry-specific needs

Phase 3: Autonomous operations

• Enable agents to make independent decisions within defined parameters
• Implement continuous learning and improvement mechanisms
• Scale agent networks across departments and functions
• Develop predictive and proactive agent capabilities

Best practices for success

Security First: Implement robust authentication, encryption, and access controls. Ensure agents operate within clearly defined boundaries with human oversight for critical decisions.

Start Small, Scale Smart: Begin with low-risk, high-impact use cases. Learn from early implementations before expanding to mission-critical processes.

Human-AI Collaboration: Design agents to augment human capabilities rather than replace them entirely. Maintain human oversight for complex decisions and edge cases.

Continuous Monitoring: Implement comprehensive logging, performance tracking, and feedback mechanisms to ensure agents perform as expected and improve over time.

[You may like reading: Understanding Generative AI and predictive analytics]

The Road Ahead: Emerging Trends and Opportunities

Industry-specific agent specialization

We're seeing the emergence of highly specialized agents tailored to specific industries:

• Legal AI Agents: Contract analysis, regulatory compliance, and case research
• Medical AI Agents: Diagnostic assistance, treatment recommendations, and administrative support
• Educational AI Agents: Personalized tutoring, curriculum development, and student assessment
• Hyper-personalization: Combining AI insights with customer context to deliver highly tailored experiences across every channel
• Cross-Platform Intelligence: MCPs will evolve to unify AI agent networks across SaaS platforms, internal systems, and external APIs

The Agent Marketplace Revolution

Platforms like MCP marketplaces are creating ecosystems where businesses can discover, customize, and deploy pre-built agents for specific functions. This democratizes access to sophisticated AI capabilities and accelerates implementation timelines.

Edge AI and Local Agents

As processing power increases and costs decrease, we're seeing more agents deployed locally for enhanced privacy, reduced latency, and improved reliability. This enables sensitive operations to benefit from AI without cloud dependencies.

Predictive and Proactive Agents

The next generation of agents won't just respond to events - they'll anticipate them. These systems will identify potential issues before they occur, suggest improvements proactively, and optimize processes continuously without human intervention.

Conclusion: Embracing the agentic future

The convergence of MCP and advanced AI tools is creating unprecedented opportunities for businesses to automate complex processes, improve customer experiences, and drive innovation. Organizations that embrace this agentic transformation will gain significant competitive advantages through:

• Operational Efficiency: Automated processes that operate 24/7 with consistent quality
• Enhanced Decision Making: Data-driven insights delivered in real-time
• Improved Customer Experience: Personalized, responsive service across all touchpoints
• Innovation Acceleration: Freed human resources focused on strategic initiatives
• Scalability: Systems that grow and adapt with business needs

The future belongs to organizations that can effectively orchestrate human creativity with AI agent capabilities. By implementing MCP-based systems and leveraging powerful AI frameworks, businesses can build the foundation for autonomous operations while maintaining the flexibility to adapt to changing market conditions.

The agentic revolution isn't coming - it's here. The question isn't whether your organization should adopt these technologies, but how quickly you can implement them to stay competitive in an increasingly automated world.

Ready to transform your business with agentic AI? Start by identifying your most repetitive processes, explore MCP-compatible tools, and begin building your first AI agents today. The future of work is autonomous, intelligent, and incredibly exciting.

Introduction

KubeCon + CloudNativeCon India 2025 in Hyderabad wasn’t just a gathering of technologists; it was a powerful showcase of where the cloud-native ecosystem is heading. Entering the venue was an experience in itself. With thousands of engineers, architects, and innovators under one roof, the event reflected the speed at which enterprises are rethinking scalability, observability, and AI-driven workloads.

Instead of simply attending sessions, our lens was on what these developments mean for enterprises looking to scale with resilience, efficiency, and security. For us, the two days were a combination of learning, networking, inspiration, and just soaking it all up.

What The Sessions Revealed About The Industry

• Scalability at enterprise scale: The GitOps at enterprise scale talk blew my mind. Scaling ArgoCD to 30,000+ applications? That talk got me rethinking how we manage deployments at scale.
• AI + Kubernetes: From hype to reality: PepsiCo's session on running LLMs on Kubernetes was another high point. Managing cost, GPUs, and fine-tuning is a tall order, but they presented an extremely pragmatic path forward.
• The hidden complexity of terminating pods: A dive deep into terminating pods took me aback - I never imagined turning off a pod could have so much behind-the-scenes.
• Observability as a business enabler: Observability sessions were real gems. The way companies were using Grafana dashboards to monitor hundreds of clusters, and how they set up Prometheus to handle massive data volumes without breaking.
• Infrastructure efficiency through eBPF & GPU tuning: And then there was GPU tuning with eBPF. I’m not even into GPU-heavy workloads day-to-day, but the tricks they shared were fascinating. Half went over my head, but I got enough. The session was about addressing GPU underutilization in AI workloads.

The Fun Side - Beyond Technology, The Community Factor

Outside of sessions, I also hung out at the Learning Lounge. Learned some CNCF cert prep tips, listened to an "LLMs for Beginners" presentation, and enjoyed Flipkart's experience with dealing with crazy database loads.

• Between the sessions, the exhibition area was abuzz. I stopped by Google Cloud, AWS, Red Hat, PerfectScale, Akamai, KSUG.AI, and so many others. Some of the conversations were light ("What do you do these days?"). But some ideas ignited when I wanted to attempt back in the office
• The highlight? Meeting old friends and colleagues. There is no better way to catch up over a cup of coffee and learn what awesome things people have been creating
• Hyderabad was perfect for this. Great food, stayed up way too late talking shop over biryani. Good conference city

Event overview

I had the opportunity to participate in Agile Network India’s conference in Noida, themed “Integrating Human-Centered Design (HCD) and UX with Agile.” The gathering brought together passionate professionals exploring ways to seamlessly blend user experience and agile practices for more innovative, human-centered solutions. Hosted by Comdez Communication Pvt. Ltd., the event provided valuable sessions, panel discussions, and hands-on workshops.

Conference experience & key themes

Human-Centered Design (HCD) Meets Agile

The sessions explored how HCD can enrich agile product development. The key takeaways included:

• The role of continuous user feedback in building meaningful products.
• Techniques for integrating design strategy into agile teams.
• Challenges and success factors when cross-functional teams simultaneously pursue speed and empathy in development.

Panelists and speakers highlighted the benefits of collaborative, iterative cycles, and the need to prioritize end-user outcomes over outputs. Metrics linking design success and business value were discussed as essential anchors for agile UX efforts.

My Kanban Workshop: The Kanban Pizza Activity

• Setup Teams simulated a pizza shop, visualizing their workflow for making and delivering (paper) pizzas. In the first round, production was chaotic without structure or limits.
• Core Principles Experienced
  • Visualizing workflow and bottlenecks.
  • Implementing work-in-progress (WIP) limits.
  • Managing flow and improving collaboration.
• Debrief After reflecting on the first round, teams reorganized their stations, set WIP limits, and distributed tasks more efficiently. The second round showed dramatic improvement: smoother flow, less rework, and better teamwork.

Why Kanban Clicked

Participants discovered that:

• Simple visual boards can drastically boost clarity and reduce multitasking.
• WIP limits expose bottlenecks, encouraging timely collaboration.
• Process transparency fosters a culture of feedback, learning, and continuous improvement - key to agile and HCD values.

Many shared that the tangible, playful simulation made Kanban principles far more memorable than any lecture. Several attendees planned to try similar activities back at work to help teams self-organize and adapt processes more effectively.

Personal Learnings & Next Steps

• Synergy of Agile and HCD Integrating human-centered design with agile is not just about adding UX steps; it's about adopting a mindset of experimentation, close user involvement, and cross-functional team learning.
• Practical Workshops Matter Active learning through simulations like the Kanban Pizza Game builds lasting understanding and practical skills.
• Community Strength Connecting with peers and experts motivated me to keep experimenting, sharing, and fostering agile transformation in my own circles.

Introduction

The OTT streaming industry continues to grow and the monetization strategies have become a crucial focus for content providers. And as competition intensifies, the focus has shifted to effective monetization strategies. Among the most impactful innovations reshaping ad delivery is Server-Side Ad Insertion (SSAI). This technology is revolutionizing how ads are integrated into video content, helping platforms deliver seamless viewing experiences, overcome ad-blockers, and unlock new revenue potential.

In this article, we explore how SSAI works, its strategic advantages, implementation challenges, and emerging trends that make it an indispensable tool for OTT providers.

What is Server Side Ad Insertion (SSAI)?

Server-Side Ad Insertion is a technology that dynamically stitches ads into VOD contents on the server before delivery to the viewer.

Unlike client-side ad insertion (CSAI), SSAI ensures a seamless ad experience by eliminating buffering, reducing ad-blocker interference, and maintaining video quality across devices.

The SSAI workflow typically involves:

1. The video content is prepared and segmented into chunks.
2. An ad decision server selects suitable ads based on the viewer profile.
3. The server stitches ads into the content stream before delivery.
4. A manifest is created for each unique viewer, determining the playback sequence.

This allows OTT providers to offer fully personalized, uninterrupted streams at scale.

How SSAI enhances OTT monetization

1. Seamless ad experience for higher engagement

One of the biggest challenges in advertising is viewer retention.

SSAI provides a smooth transition between video content and ads, reducing buffering and minimizing the chances of users dropping off during ad breaks.

This leads to higher engagement rates and better ad completion metrics, directly impacting revenue.

2. Bypassing Ad Blockers for Maximized Revenue

Users were able to block CSAI with the help of Ad Blocker Softwares.

Since SSAI stitches ads within the video stream itself, it makes ad detection and blocking difficult, ensuring that ads are delivered to a larger audience and it increases monetization opportunities for publishers.

3. Personalized Ad Targeting with Dynamic Ad Insertion

Server Side Ad Insertion supports Dynamic Ad Insertion (DAI), allowing content providers to deliver relevant ads based on user preferences, demographics, and behavior.

This level of personalization improves ad relevance, leading to higher conversion rates and better Cost Per Thousand Impressions.

4. Consistent Multi-Device Ad Delivery

OTT viewers consume content on devices including Mobile, Web, and Smart TV Apps.

SSAI ensures that ads are delivered consistently across all platforms, maintaining a high-quality experience regardless of the device or bandwidth conditions.

5. Scalability for Live and On-Demand Content

SSAI is well-suited for high-traffic live events like sports, elections, or reality shows. With cloud-based infrastructure, it scales dynamically to serve millions of personalized manifests without performance lags.

It enables broadcasters to scale ad delivery for large audiences, ensuring a smooth experience even during high-traffic events like sports streaming.