#app security

[thecouncilofidiots]

SimplyPlural Guide!

How to lock the app / require a code to open the app!!

Step 1 : Open the Navigation on the side, and click Settings

Step 2 : Click App Settings

Step 3 : Click App Security

Step 4 : Choose and Set Up whatever Security Option you want ^.^

Hope this helps @confused-canid :3

[net-craft]

Mobile App Security: What’s Your Contingency Plan? Safeguard Your Users and Your Reputation

In today’s digital landscape, where mobile apps hold a treasure trove of personal information, mobile app security is no longer an afterthought; it’s a paramount concern. As a leading provider of best mobile app creation services in Arizona, we at Net-Craft are committed to helping you navigate the complex world of app security and develop a robust contingency plan.

[loudlittleecho]

I DON’T HAVE THE SPACE ON MY PHONE TO KEEP DOING THIS

I don’t want to download an app to get a discount on a burger.

Stop it.

sorry for being such a boomer but i can't stand having to download a different app for every event i want to go to just to access my fucking tickets. i should be able to call a number and order them over the phone without the internet and then go to a nice old man at a booth on the day of the show and pick up my little paper tickets.

[anvi-cybernetics]

App Development Mistakes to Avoid: Lessons from Industry Experts

Mobile application development is arguably one of the most competitive and rapidly evolving fields in technology today. Developers around the world invest more in their apps to reach a large number of people, create a more exciting customer experience, and fuel business growth. However, despite the immense opportunities, an effective way to develop a successful mobile application is difficult and even risky. Even the best developers may fall victim to common traps that can hinder the success of their app.

Here, in this blog, we will discuss some critical mistakes in the development of applications that industry experts say must be avoided. We will also stress on how insights from professionals and leveraging cutting-edge technologies, like artificial intelligence, can lead to more successful app outcomes.

1. Neglecting Market Research

One of the most fundamental mistakes in app development is skipping or underestimating the importance of thorough market research. Without understanding your target audience, their needs, preferences, and pain points, it's easy to develop an app that misses the mark.

Market research must include:

Analyzing competitors' apps to identify strengths and weaknesses

Understanding user behavior through surveys, interviews, and analytics

Identifying the specific problem your app will solve

A lack of proper market research can result in an app that doesn't connect with the user or doesn't offer the features that users actually need. Other than having a low adoption rate, this tends to heighten the tendency for app abandonment.

2. Poor UI/UX Design

User Interface (UI) and User Experience (UX) are two major elements of any mobile application. The UI/UX design issue is one of the most common ones, which seriously can damage retention rates of the user. According to experts, users make up their mind whether to stay in an app or leave it in just a few minutes of use.

Some common mistakes in UI/UX design are:

Overloading the interface with extraneous elements

Using vague or ambiguous navigation

Weakness in consistency of design

Ignoring platform-specific design guidelines (iOS vs. Android)

To ensure a successful app, developers must prioritize a seamless and engaging design that aligns with users’ expectations. Prototyping and testing the app at various stages of development can help identify and resolve UI/UX issues early.

3. Overlooking Performance Optimization

It seems performance was pretty much a key driver because if it doesn't load fast enough or sucks up too much battery, the user may uninstall it or provide poor reviews.

Experts have always emphasized:

File size reduction in the application

Helps the code run fluently on every device.

Limiting back-end processes that are power-hungry

Testing the application under different conditions, like an internet connection, or under older devices.

Testing your app on different platforms and operating systems is important since mobile devices of different specifications exist, meaning it must perform well on all types.

4. No proper updating and service

Most developers believe that their work is done when they launch the application. The launching, however, serves as a starting point. Apps must be updated frequently in order to fix bugs, enhance features, and ensure compatibility with new OS versions.

Failing to update and maintain the app can result in:

Bugs annoying end users

Examples of vulnerabilities include user data exposure.

Compatibility issues with any new device or application update

Industry experts emphasize the need for ongoing app maintenance, which includes collecting user feedback and staying up-to-date with the latest technological trends.

5. Underestimation of security importance:

With the continuous advancement in the rate of cyber threats, more and more app security is expected in today's world. Especially with new developers, their mobile apps are being underrated in securing them, leading to data breaches, causing legal issues, and the loss of user trust.

Common security problems include:

Weak data encryption

Weak Authentication Mechanisms

Poor protection against malware and other cyber risks

To avoid these pitfalls, it's critical to implement robust security protocols, use secure coding practices, and stay updated with the latest security guidelines. For businesses developing mobile apps, partnering with experienced security professionals, such as Anvi Cybernetics, can help ensure that their apps are built with cybersecurity in mind.

6. Not Leveraging Artificial Intelligence (AI)

Today, the infusion of artificial intelligence dramatically enhances the user experience and process flows in app development, but many developers are blind to the true power of AI or possibly underestimating its potential in the marketplace.

AI can be used in mobile apps to:

Personalize user experiences

Automate customer support with chatbots

Predict user behavior and improve app engagement

Provide advanced data analytics and insights

For more in-depth information on how AI can revolutionize your app development process, explore this article on the role of artificial intelligence in mobile app development.

7. Overloading the Application with Too Many Features

The most appealing approach is to pack in every feature possible under one roof so as to attract a wider audience. However, the result of this often manifests itself in a cluttered and perplexed user experience. Piling up useless features in the app can also lead to performance issues or very long timelines for development.

According to market pundits, core functionality of the app should be provided. A Minimum Viable Product (MVP) approach brings the app to the market by using the minimal set of features; later they introduce new features based on user feedback once they are in demand.

8. Inadequate Testing

Skipping proper testing is one of the most critical mistakes that can lead to an application being full of bugs. Users have little patience for apps that crash, freeze, or behave unpredictably. Therefore, testing should be an integral part of the app development process.

Types of testing to consider:

Unit Testing: To determine if the small units of an application are working as they should.

Integration Testing: That ensures different parts of the app work flawlessly together.

Usability testing: Testing the app with real users, using it to highlight pain points and usability problems.

Performance Testing: Ensuring the app performs well under different conditions, such as low bandwidth or heavy usage.

By incorporating thorough testing throughout the development cycle, developers can catch and fix issues before the app reaches users.

9. Failure to Plan for Scalability

Generally, with the growth of popular apps comes a variation that requires a scalability plan. When having an insufficient plan for scalability, servers are likely to crash, and the application might not load quickly enough, meaning poor user experience during peak usage times. To avoid this, developers should build scalable architectures from the start, ensuring the app can handle future growth. This includes optimizing databases, ensuring efficient server communication, and considering cloud infrastructure for easy scaling.

10. Ignoring App Store Optimization (ASO)

It's surprising how many developers leave out App Store Optimization (ASO). Just like SEO helps websites rank on search engines, it is ASO that significantly determines the ranking of your application in the app stores.

The key factors of ASO are:

Choosing the right keywords for your app title and description

Encouraging positive user reviews and ratings

Using eye-catching icons and screenshots

Writing a compelling app description

Optimizing your app for the app stores is key to maximizing visibility, thus potentially increasing downloads and, ultimately, its success.

Conclusion

App development is a complex process, and even minor mistakes can have significant repercussions. However, by learning from industry experts and avoiding these common pitfalls, developers can create apps that not only meet users' needs but also stand the test of time. Leveraging technologies like artificial intelligence and partnering with experienced development teams, such as Anvi Cybernetics, can further enhance the success of your mobile app.

[magicmindsblog]

Have you ever pondered the effect artificial intelligence (AI) has on our world? AI is becoming prevalent in our everyday lives, from self-sufficient cars to virtual assistants — AI is everywhere! However, are you conscious that AI is likewise changing how we secure our mobile packages?

This blog explores how AI is being used to enhance mobile app security and discusses the limitations and benefits of this implementation.

[jcmarchi]

Yariv Fishman, Chief Product Officer at Deep Instinct – Interview Series

New Post has been published on https://thedigitalinsider.com/yariv-fishman-chief-product-officer-at-deep-instinct-interview-series/

Yariv Fishman, Chief Product Officer at Deep Instinct – Interview Series

Yariv Fishman is Chief Product Officer (CPO) at Deep Instinct, he is a seasoned product management executive with more than 20 years of leadership experience across notable global B2B brands. Fishman has held several prominent roles, including leadership positions with Microsoft where he led the Cloud App Security product portfolio and initiated the MSSP and security partner program, and Head of Product Management, Cloud Security & IoT Security at CheckPoint. He holds a B.Sc in Information Systems Engineering from Ben Gurion University and an MBA from the Technion, Israel Institute of Technology.

Deep Instinct is a cybersecurity company that applies deep learning to cybersecurity. The company implements AI to the task of preventing and detecting malware.

Can you tell us about your journey in the cybersecurity industry and how it has shaped your approach to product management?

Throughout my 20 year career, I’ve worked at several global B2B organizations, including Check Point Software Technologies and Microsoft, where I led product management and strategy and built my cybersecurity experience across public cloud, endpoint, network, and SaaS application security.

Along the way, I’ve learned different best practices – from how to manage a team to how to inform the proper strategy – that have shaped how I lead at Deep Instinct. Working for numerous cybersecurity companies of various sizes has allowed me to get a holistic view of management styles and learn how to best create processes that support fast-moving teams. I’ve also seen first-hand how to release products and plan for product-market fit, which is critical to business success.

What drew you to join Deep Instinct, and how has your role evolved since you started as Chief Product Officer?

As an industry veteran, I rarely get excited about new technology. I first heard about Deep Instinct while working at Microsoft. As I learned about the possibilities of predictive prevention technology, I quickly realized that Deep Instinct was the real deal and doing something unique. I joined the company to help productize its deep learning framework, creating market fit and use cases for this first-of-its-kind zero-day data security solution.

Since joining the team three years ago, my role has changed and evolved alongside our business. Initially, I focused on building our product management team and relevant processes. Now, we’re heavily focused on strategy and how we market our zero-day data security capabilities in today’s fast-moving and ever-more-treacherous market.

Deep Instinct uses a unique deep learning framework for its cybersecurity solutions. Can you discuss the advantages of deep learning over traditional machine learning in threat prevention?

The term “AI” is broadly used as a panacea to equip organizations in the battle against zero-day threats. However, while many cyber vendors claim to bring AI to the fight, machine learning (ML) – a less sophisticated form of AI – remains a core part of their products. ML is unfit for the task. ML solutions are trained on limited subsets of available data (typically 2-5%), offer only 50-70% accuracy with unknown threats, and introduce false positives. They also require human intervention because they are trained on smaller data sets, increasing the chances of human bias and error.

Not all AI is equal. Deep learning (DL), the most advanced form of AI, is the only technology capable of preventing and explaining known and unknown zero-day threats. The distinction between ML and DL-based solutions becomes evident when examining their ability to identify and prevent known and unknown threats. Unlike ML, DL is built on neural networks, enabling it to self-learn and train on raw data. This autonomy allows DL to identify, detect, and prevent complex threats. With its understanding of the fundamental components of malicious files, DL empowers teams to quickly establish and maintain a robust data security posture, thwarting the next threat before it even materializes.

Deep Instinct recently launched DIANNA, the first generative AI-powered cybersecurity assistant. Can you explain the inspiration behind DIANNA and its key functionalities?

Deep Instinct is the only provider on the market that can predict and prevent zero-day attacks. Enterprise zero-day vulnerabilities are on the rise. We saw a 64% increase in zero-day attacks in 2023 compared to 2022, and we released Deep Instinct’s Artificial Neural Network Assistant (DIANNA) to combat this growing trend. DIANNA is the first and only generative AI-powered cybersecurity assistant to provide expert-level malware analysis and explainability for zero-day attacks and unknown threats.

What sets DIANNA apart from other traditional AI tools that leverage LLMs is its ability to provide insights into why unknown attacks are malicious. Today, if someone wants to explain a zero-day attack, they have to run it through a sandbox, which can take days and, in the end, won’t provide an elaborate or focused explanation. While valuable, this approach only offers retrospective analysis with limited context. DIANNA doesn’t just analyze the code; it understands the intent, potential actions, and explains what the code is designed to do: why it is malicious, and how it might impact systems. This process allows SOC teams time to focus on alerts and threats that truly matter.

How does DIANNA’s ability to provide expert-level malware analysis differ from traditional AI tools in the cybersecurity market?

DIANNA is like having a virtual team of malware analysts and incident response experts at your fingertips to provide deep analysis into known and unknown attacks, explaining the techniques of attackers and the behaviors of malicious files.

Other AI tools can only identify known threats and existing attack vectors. DIANNA goes beyond traditional AI tools, offering organizations an unprecedented level of expertise and insight into unknown scripts, documents, and raw binaries to prepare for zero-day attacks. Additionally, DIANNA provides enhanced visibility into the decision-making process of Deep Instinct’s prevention models, allowing organizations to fine-tune their security posture for maximum effectiveness.

What are the primary challenges DIANNA addresses in the current cybersecurity landscape, particularly regarding unknown threats?

The problem with zero-day attacks today is the lack of information about why an incident was stopped and deemed malicious. Threat analysts must spend significant time determining if it was a malicious attack or a false positive. Unlike other cybersecurity solutions, Deep Instinct was routinely blocking zero-day attacks with our unique DL solution. However, customers were asking for detailed explanations to better understand the nature of these attacks. We developed DIANNA to enhance Deep Instinct’s deep learning capabilities, reduce the strain on overworked SecOps teams, and provide real-time explainability into unknown, sophisticated threats. Our ability to focus the GenAI models on specific artifacts allows us to provide a comprehensive, yet focused, response to address the market gap.

DIANNA is a significant advancement for the industry and a tangible example of AI’s ability to solve real-world problems. It leverages solely static analysis to identify the behavior and intent of various file formats, including binaries, scripts, documents, shortcut files, and other threat delivery file types. DIANNA is more than just a technological advancement; it’s a strategic shift towards a more intuitive, efficient, and effective cybersecurity environment.

Can you elaborate on how DIANNA translates binary code and scripts into natural language reports and the benefits this brings to security teams?

That process is part of our secret sauce. At a high level, we can detect malware that the deep learning framework tags within an attack and then feed it as metadata into the LLM model. By extracting metadata without exposing sensitive information, DIANNA provides the zero-day explainability and focused answers that customers are seeking.

With the rise of AI-generated attacks, how do you see AI evolving to counteract these threats more effectively?

As AI-based threats rise, staying ahead of increasingly sophisticated attackers requires moving beyond traditional AI tools and innovating with better AI, specifically deep learning. Deep Instinct is the first and only cybersecurity company to use deep learning in its data security technology to prevent threats before they cause a breach and predict future threats. The Deep Instinct zero-day data security solution can predict and prevent known, unknown, and zero-day threats in <20 milliseconds, 750x faster than the fastest ransomware can encrypt – making it an essential addition to every security stack, providing complete, multi-layered protection against threats across hybrid environments.

Thank you for the great interview, readers who wish to learn more should visit Deep Instinct.

[rubengrey]

Low-Code Integration - Enhance Your Mobile App Development

Integrate low-code development into your agency to enhance mobile app development for startups, delivering faster, cost-effective, and innovative solutions.

[crossdevverse]

What to Look for When Hiring Android App Developers | AIS Technolabs

Discover the key qualifications and skills to seek in Android app developers. Make informed hiring decisions with our essential guide

[technology-and-beyond]

Essential Best Practices for Successful Mobile App Development

Discover key strategies for mobile app development success. Learn essential best practices to enhance your apps' performance, user experience, and security.

[understanding-react-native]

Essential Best Practices for Mobile App Development

Discover the crucial 'Essential Best Practices for Mobile App Development' to streamline your app creation process. From efficient coding techniques to optimal UI/UX design principles, this guide covers all essential strategies for building successful mobile applications. Learn how to enhance security, boost performance, and ensure user satisfaction with proven best practices for mobile app development. Stay ahead in the competitive app market by implementing these expert-recommended methods today.

[marketology]

Mobile App Security: Protecting Your Data Fortress

Mobile App Security: Protecting Your Data Fortress

Mobile App Security: Fortifying Your Digital Sanctuary In our hyper-connected digital era, mobile applications have become indispensable tools for personal and professional endeavors. However, as our reliance on these apps grows, so does the need to prioritize mobile app security. These powerful software gems serve as gateways to our most sensitive data, making their protection paramount. Join us…

[marsdevs]

Security Matters: Building Trustworthy and Secure Android Apps

Security and data privacy issues are the main concern for users in the modern digital era. As mobile applications are continuously gaining popularity, it is important to make sure Android apps are reliable and secure. Maintain user privacy, establish credibility, and abide by the law to provide a safe and satisfying experience. In this post, we'll examine the importance of security in app development and offer crucial advice for creating reliable and safe Android apps.

[hybridappbuilder]

Stay Ahead: Empower Your Mobile App Security with Proven Strategies. Dive into Our Latest Blog for Effective User Data Protection:

[techdriveplay]

Why Apple Continues to Lead in The Phone Game

In the ever-evolving landscape of technology, where innovation and sophistication intersect, Apple stands out as a beacon of excellence and ingenuity. With its roots deeply embedded in the art of creating not just devices, but experiences, Apple has continuously set the benchmark for what a smartphone can be. It’s not merely the advanced technology or the sleek designs that position Apple as a…

[magicmindsblog]

The advancement of AI-powered testing techniques has significantly enhanced mobile app security. AI-driven mobile app testing can proactively identify vulnerabilities, detect potential threats, and lower security risks more efficiently. This approach not only enhances mobile app resilience but also builds strong customer engagement in this digital world.

This article aims to illuminate how AI can disrupt the mobile security testing world, strengthening security more than ever. So, what’re you waiting for? Let’s begin our journey!

[love-is-normal]

rock around the security flaw

dual life - rock and security flaws