Like humans have gone through their evolution path, so now does artificial intelligence, with human assistance. Becoming smarter, quicker, and more adept at tackling complex tasks already seems the natural way of AI evolving. Its progress is rapidly reshaping technology and expanding its applications.

To fully grasp the AI’s potential, challenges and possibilities it may bring to businesses and the technological world, we need to answer the following questions: How has artificial intelligence evolved? How fast is AI evolving? What are the main stages of AI? What stage of AI are we in?

Understanding AI in a nutshell

Artificial Intelligence (AI) is basically the emulation of human intelligence in machines programmed to think and perform tasks like humans. The powerful AI tools we use today stem from computing breakthroughs spanning over 70 years, from code breakers in the Second World War to the very first pattern-recognition networks in the 1950’s. As AI tackles increasingly complex tasks, we face the birth of new AI models, each offering distinct levels of functionality.

Evolution stages of artificial intelligence

Tracing the stages of artificial intelligence is no less exciting than watching the periods of human history. While different approaches highlight its evolution timeline, we opted for a historical perspective on the AI advancements timeline, to uncover pivotal milestones shaping and laying its foundation for today’s thriving AI ecosystem.

The starting point of creating artificial intelligence goes back to the mid-20th century. The pioneering moment in AI was Alan Turing’s 1950 paper “Computing Machinery and Intelligence”, proposing the concept of machines capable of simulating human intelligence.

The official birth of AI as an academic discipline is credited to the 1956 Dartmouth Conference. This gathering brought together researchers to explore and lay the groundwork for AI, marking its emergence as a recognized field of study.

According to the historical approach, researchers break down the AI evolution into several distinct stages.

Birth of artificial intelligence: This era marked the beginning of AI discussions. Early machines capable of playing games like checkers were developed, and Alan Turing introduced his famous test to determine if a machine’s intelligence was equivalent to a human’s.

Early successes: Many AI laboratories were established, and AI received significant financial backing. Research focused on teaching AI natural languages like English. Key achievements include Japan’s first human-like robot, “WABOT,” the introduction of the conversational AI model “ELIZA,” and the creation of the first expert system, “Dendral.”

First AI Winter: Due to slow progress in AI research, partly because of limited computing power, the field experienced a “winter” period that significantly hindered its advancement.

Boom: During this phase, the first expert systems with knowledge in specific fields were designed. AI researchers realized the importance of extensive knowledge bases for AI.

Second AI Winter: Funding for AI research was cut again, largely because AI systems were considered too expensive to maintain.

AI Implementation: AI began to be integrated into various industries. Due to its tarnished reputation from the 20th century, it was often rebranded under different names.

Deep Learning and Big Data: AI interest surged to a “frenzy” level, as described by the New York Times. Significant advancements were made thanks to the development of Deep Learning and Big Data technologies.

AI Era (or Second AI boom): We are currently in this period. Large databases and language models enable the creation of highly proficient AI systems. AI automation is widely used, and generative AI has captivated millions and is now accessible to the general public.

Diving deeper, after analyzing open online sources, we traced the milestones of AI evolution by emergence of popular platforms, text and language models starting from the year 2020. Here are the most significant events that became the cornerstones for the era of AI.

2020

January: Google presents MEENA, a conversational model.
June: OpenAI introduces GPT-3
April: Blenderbot, a chatbot by Meta, is released


2021

June: GPT-J is released.
June: LaMDA, a conversational AI from Google, is released.
December: Gopher, a large language model, is introduced.


2022

May: LaMDA 2 is released by Google.
July: Midjourney enters open-beta
August: Stable Diffusion is released
September: DALL·E 2 was opened to everyone
September: CharacterAI is released
September: Make-A-Video is released by Meta.
November: ChatGPT by OpenAI debuts.

2023

February: LLaMA, a collection of language models, is released by Meta.
March: OpenAI‘s GPT-4 model is released.
March: Google releases Google Bard chatbot in a limited capacity
May: Statement on AI Risk is signed by AI researchers and tech leaders, including Geoffrey Hinton, Sam Altman, Bill Gates
December: Google releases Gemini 1.0 Ultra


2024

January: Stable LM 2 is released by StabilityAI.
February: Google releases Gemini 1.5 in limited beta.
February: OpenAI announces Sora, a text-to-video model
April: Apple unveils OpenELM, open-source language models
May: Red Hat launches RHEL for AI

As we now have a broader picture of AI evolving, we can delve into exploration of AI classifications reflecting how these advancements are reshaping industries and driving innovation.

Decoding the classifications of AI

Artificial Intelligence (AI) is transforming many facets of our lives, from virtual assistants to intricate problem-solving applications. However, AI systems vary greatly in their levels of operation and functions, and therefore, its application in the business and tech sphere. Let’s explore the most vivid of them.

AI classifications by capability

According to capability, AI can be divided into three types: Artificial Narrow Intelligence (ANI), or weak AI, Artificial General Intelligence (AGI), Artificial Superintelligence (ASI).

1) Artificial Narrow Intelligence (ANI), or weak AI
Artificial Narrow Intelligence (ANI), also known as Weak AI, is the only type of AI that exists today. ANI can be trained to execute a specific or limited task, often more efficiently and accurately than a human. Yet, it cannot function beyond its assigned role. It is like a calculator designed to perform complex mathematical operations, but it cannot perform tasks outside of calculations. Instead, it specializes in a single subset of cognitive abilities. Some examples of Narrow AI are Siri, Amazon’s Alexa, and IBM Watson. OpenAI by ChatGPT is also considered a form of Narrow AI because it is limited to the task of text-based interaction.

2) Artificial General Intelligence (AGI)
Artificial General Intelligence, also known as Strong AI, is currently only a theoretical concept. In the ideal world, AGI would be able to use past knowledge and skills to accomplish new tasks in different contexts without requiring human intervention for training. This adaptability and wide-ranging skill set would distinguish AGI from the more specialized AI we see today.

3) Artificial Superintelligence (ASI)
Artificial Superintelligence (ASI), or Super AI, is likewise only theoretical. If brought to reality, ASI is supposed to exceed human intelligence, capable of thinking, reasoning, learning, and making decisions. Moreover, to experience emotions, have needs, and hold beliefs and desires of their own. Luckily, we still have some time before the real rise of Super AI.

AI classifications by functionality

According to functionality, AI can be divided into: Reactive Machine AI, Limited Memory AI, Theory of Mind AI, and Self-Aware AI.

1) Reactive Machine AI. It consists of systems without memory, designed for executing specific tasks and operate only with currently available data. Reactive Machine AI originates from statistical mathematics and can analyze extensive amounts of data to generate seemingly intelligent outputs. Examples include IBM’s Deep Blue and the Netflix Recommendation Engine.

2) Limited Memory AI. Unlike Reactive Machine AI, this form of AI can recall past events and outcomes and monitor specific objects or situations over time. Limited Memory AI can use past- and present-moment data to decide on a course of action most likely to help achieve a desired outcome. However, while Limited Memory AI can use past data for a specific amount of time, it can’t retain that data in a library of past experiences to use over a long-term period. As it’s trained on more data over time, Limited Memory AI can improve in performance.

In contrast to Reactive Machine AI, Limited Memory AI can remember past events and outcomes and track specific objects or scenarios over time. This type of AI can utilize both historical and current data to determine the most effective course of action to achieve a desired result. Yet, Limited Memory AI cannot store this data in a long-term repository, but can enhance its performance thanks to being trained on more data over time.Here are the most vivid examples of Limited Memory AI.

Generative AI like ChatGPT, Bard and DeepAI rely on limited memory AI capabilities to generate the next word, phrase or visual element within the content they generate.

Virtual assistants and chatbots such as Alexa, Siri, Google Assistant, IBM Watson Assistant, and Cortana utilize natural language processing (NLP) and Limited Memory AI to comprehend questions and requests, take appropriate actions, and formulate responses.

Self-driving cars employ Limited Memory AI to perceive their surroundings in real time and make informed decisions on when to accelerate, brake, or turn.

3) Theory of Mind AI. One of the General AI categories, although not yet created, Theory of Mind AI is predicted to understand the thoughts and emotions of others. Potentially this understanding would enable the AI to form human-like relationships. By inferring human motives and reasoning, Theory of Mind AI could personalize its interactions based on individual emotional needs and intentions. Also, Theory of Mind AI would be able to comprehend and contextualize artwork and essays, a capability that today’s generative AI tools lack.

4) Self-Aware AI. It is a type of functional AI for applications that would have super intelligent capabilities. Similar to Theory of Mind AI, Self-Aware AI remains purely theoretical. If it were realized, it would be able to comprehend its own internal states and characteristics, as well as human thoughts and emotions. Additionally, it would possess its own emotions, needs, and beliefs.

AI classifications by technology

The diverse technologies within AI offer a spectrum of capabilities to meet specific business needs. By integrating these technologies, companies can uncover new avenues for innovation.

Let’s dive into types of AI according to technology.

References

1. Understanding the different types of artificial intelligence. IBM, 2023.
2. Types of Artificial Intelligence That You Should Know in 2024. Simplilearn, 2024.
3. Generative AI vs. other types of AI. Adobe, 2024.
4. The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. McKinsey, 2024
5. History of artificial intelligence. Wikipedia, 2024. 
6. A history of AI. Oxford, 2024.
7. Top 10 Most Popular AI Tools That You Need to Use in 2024. Technopedia, 2024.
8. Our 10 biggest AI moments so far. Google, 2023.
9. AI timelines: What do experts in artificial intelligence expect for the future? Our World in Data, 2023

Developing software is a costly business, and there is always a temptation to cut expenses by taking on some team functions internally. Testing often seems to be the simplest part.

However, this is a significant misconception: proper QA services are just as crucial as professional coding. Consider high-profile cases like Toyota’s unintended acceleration (2009-2010), where software flaws led to multiple accidents and a massive recall, or the bug in the Windows 10 October update (2018), which caused users’ files to be deleted resulting in significant data loss and widespread frustration.

At the same time, it’s essential to complement the internal QA team with independent software testing services. In-house QA can face challenges such as bias and limited perspective, while independent testing brings an objective viewpoint, free from internal pressures, with broader expertise from various projects and industries. Overall, professional testing is crucial for building a strong culture of software quality, ensuring the reliability, security, and usability of products.

The integral role of QA team

Although it may seem easy, software testing is more than just clicking buttons. The quality assurance process entails multiple steps, with significant emphasis on preparation and documentation: crafting a test plan, developing test cases, scrutinizing software requirements and UI design, generating test data, and configuring necessary environments. Choosing the appropriate tools to track identified issues (such as Atlassian Jira or JetBrains YouTrack) and ensuring the maturity of quality assurance procedures are critical. They eliminate wasting time resources and offer prompt feedback on the build’s suitability for further testing. Robust QA processes mitigate scenarios where rectifying one bug introduces new ones or the implementation of a new feature destabilizes the entire application. And this is just the tip of the iceberg.

Beyond functional testing, QA shoulders the responsibility for software security, performance, and usability. Although it might seem that involving the QA team at the final stage of development for acceptance testing is sufficient, this is a misconception. Quality assurance specialists should be actively involved throughout all stages of software development, starting with validating UI design and requirements. The earlier a problem is detected, the cheaper it is to fix. By the time the software reaches the acceptance testing stage, it should have already been thoroughly examined and be operating according to specifications.

A valid reason behind having a dedicated QA team over programmers verifying their own code is that QA engineers and software developers typically have slightly different goals: programmers aim to make the code functional, while testers strive to uncover defects with negative scenario testing. This collaboration, where programmers create and QA specialists try to break, offers significantly more advantages than relying solely on developers, regardless of their skill level.

What does the customer receive from the QA team’s work? Primarily, a report on the quality of the developed system. Detailed reports show not only the current state of the software being developed but also trends in quality changes, using historical data and various QA metrics (such as the percentage of reopened bugs, the percentage of passed test cases, etc.). Additionally, a proficient QA department offers recommendations on priorities for the next build release. While there may not always be an obvious issue, they can identify indicators that suggest a potential problem under certain conditions.

To ensure high-quality software, particularly when the cost of errors is significant, it is wise to involve an independent testing team. Companies that provide software testing as a service can deliver the best results thanks to certified professionals who adhere to international testing standards such as ISO/IEC/IEEE 29119 and ISO/IEC 25010:2023. Additionally, these independent teams prioritize the software customer’s interests, unlike internal QA specialists who are closely aligned with the development team.

Independent software testing: benefits and risks

Engaging independent testing services is like hiring a professional taster for your cooking – they provide candid feedback, but they might also uncover some unexpected ingredients. Meanwhile, an in-house QA team is familiar with your recipe, but may miss out on fresh perspectives.

The primary benefits of involving an independent software testing team are:

• Objective perspective: A third-party QA team offers an unbiased viewpoint, unaffected by internal company dynamics or pressures. 
• Specialized expertise: Outsourced testing teams often bring a wide range of competencies and insights from working across different sectors and projects. 
• Cost-effectiveness: Hiring external QA specialists can be more cost-effective as it eliminates the need for investing in infrastructure, training, and ongoing employment costs associated with an in-house team. 
• Scalability: Outsourced teams offer the advantage of seamless scalability according to project needs, delivering unparalleled adaptability and responsiveness.
• Compliance and standards: Third-party software testing teams are often well-versed in industry standards and regulatory requirements, helping ensure compliance. 

Best practices and tools: By outsourcing QA services, you typically gain access to the most up-to-date industry standards and advanced testing technologies, enhancing the overall quality assurance process.

Despite the considerable benefits, engaging a third-party team in the testing process comes with certain risks:

• Lack of context: Independent QA specialists may not fully grasp the company’s internal processes, culture, and specific project requirements, potentially leading to misaligned testing priorities.
• Communication barriers: Interaction between an outsourced quality control engineers and internal developers or stakeholders may be less streamlined compared to in-house teams, resulting in delays or misunderstandings.
• Limited integration: External QA specialists may face challenges integrating with internal systems, tools, or processes, potentially hindering collaboration and efficiency.
• Confidentiality concerns: Sharing sensitive project information with third-party software testing teams may raise concerns about data privacy, confidentiality, and intellectual property protection.

While there are cons mentioned above, the pros of engaging an external team for quality assurance far outweighs them. With effective management, these drawbacks can be completely mitigated.

Independent testing as part of quality-centric culture

To avoid watching competitors swiftly depart on a train of progress while you stay behind, it’s wise to nurture a quality-driven approach. No matter how compelling a product idea may be, poor implementation can sabotage its success. Culture of quality is a business-centric approach that fosters a mindset and practices focused on achieving high-quality outcomes in every aspect of software development.  This includes:

• Continuous improvement: Emphasizing ongoing refinement of processes and practices to enhance quality.
• Collaboration: Encouraging teamwork and communication across departments to ensure alignment and shared goals.
• Customer focus: Putting the needs and satisfaction of end-users at the forefront of decision-making and product development.
• Accountability: Holding individuals and teams responsible for the quality of their work and outcomes.
• Automation: Harnessing modern tools to automate repetitive activities and optimize workflows, minimizing the potential for human mistakes.
• Data-driven decision-making: Employing data analytics to guide choices and enhance quality and efficiency.

All of these aspects can be effectively managed through QA testing as a service. It’s also worth noting that companies specializing in outsourcing testing services are increasingly leveraging AI tools to bolster task automation and data analysis.

However, creating a quality-driven culture requires more than just hiring seasoned developers and QA specialists. It entails ensuring that all processes work seamlessly together. This is where DevOps comes into play by implementing continuous integration of changes and timely quality checks. Skilled DevOps specialists can help mitigate the inevitable risks associated with outsourcing or other activities, even when the DevOps service itself is third-party. Furthermore, DevOps plays a crucial role in controlling technical debt by ensuring code quality and performance.

Trends in QA testing services

Considering recent advancements in software development—such as AI evolution, virtual and augmented realities, digital assets, virtual payments, and smart devices—the demand for high-quality software is more critical than ever. The cost of errors is growing exponentially, affecting users of all ages, from infants to the elderly. Key trends in QA services include:

• Shift-left testing: This proactive approach integrates testing at the initial stages of design and development, allowing defects to be identified and corrected earlier, thus reducing costs and time-to-market. 
• Shift-right and Chaos Engineering: This type of testing evaluates software in real-world production environments. A well-known example is Netflix, where engineers intentionally create glitches during real-world performance testing. Tools like Chaos Monkey randomly shut down service instances in production, and Chaos Kong simulates large-scale outages. Latency Monkey introduces artificial delays into the network, and Doctor Monkey checks and terminates unhealthy instances. These techniques help Netflix identify weaknesses, improve system resiliency, and maintain service reliability during unexpected failures. 
• Automation testing: Introducing automated validation throughout all aspects of the software system minimizes manual effort, accelerates the testing process, and maintains consistency. 
• Security testing: As smart gadgets become integral to our daily lives, security is more crucial than ever. Cybercriminals now use AI and social engineering to launch sophisticated attacks. Identifying vulnerabilities and ensuring software protection against potential threats can safeguard both data and user trust.
• Performance engineering: Beyond simple performance testing, performance engineering focuses on designing systems for optimal performance from the ground up. This is where the QA team works closely with solution architects.
• Leveraging AI and machine learning in testing: AI and ML can predict potential defects, optimize test cases, and automate complex testing scenarios.
• Ethical and usability testing to cover DEI principles: Ensuring that software is inclusive and accessible is becoming a critical aspect of quality assurance. Ethical and usability testing focuses on delivering software that adheres to diversity, equity, and inclusion (DEI) principles, providing a better user experience for all demographics.

To stay ahead, QA engineers must refine their skills, gain certifications, and collaborate closely with developers, designers, and business analysts. Adapting to these trends and leveraging independent testing services can provide an objective perspective, specialized skills, and cost efficiencies. This ensures software meets the highest standards, giving businesses a competitive edge in today’s fast-paced market.

Independent testing services by Timspark

References

1. Toyota’s acceleration testing still inadequate, House members say. Los Angeles Times, 2010. 
2. Microsoft Confirms Windows 10 Update Mistake — Was Your Data Lost? Forbes, 2018. 
3. Chaos Engineering Saved Your Netflix Extreme stress testing of online platforms has become its own science. IEEE Spectrum, 2021. 
4. ISO/IEC/IEEE 29119-1:2022 Software and systems engineering — Software testing. ISO, 2022. 
5. ISO/IEC 25010:2023 Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Product quality model. ISO, 2023.

Have you ever wondered how information gets exchanged over the internet? The magic happens thanks to APIs – Application Programming Interfaces. APIs enable communication between your browser and the server-side of an application. They are like the glue holding the digital world together, making apps talk to each other seamlessly. But just like you wouldn’t leave your front door wide open, you shouldn’t leave your APIs unsecured. Imagine if hacking your internet banking was as easy as guessing a Wi-Fi password – scary, right? Unsecured APIs can lead to data leaks, breaches, and more headaches than forgetting your own Wi-Fi password. API security threats are real, and addressing them early is essential for maintaining a smooth and secure digital experience. In our article, you can find valuable insights on how to secure APIs.

Why API security is important

Modern software can be roughly divided into three main components: the client (or frontend), the server (or backend), and the database. In a well-designed system, the client doesn’t interact directly with the database but communicates with the server through an API. The server handles significant business logic, ensuring data completeness, accuracy, and integrity, often managing sensitive information.

Databases may store both confidential (personal) and public data, and depending on the implementation, this data can be encrypted or unencrypted. Importantly, the client side rarely holds sensitive data, containing at most the personal information of the current user. This makes the client side less attractive to hackers, as breaching a single client provides limited valuable information.

Protecting the database is far more critical since hackers may attempt to steal the entire database. However, such attacks are increasingly challenging due to the need to bypass infrastructure security measures, particularly those implemented within a DevSecOps framework. 

Typically, there is no direct access to the database to retrieve data via SQL queries. Therefore, hackers often focus their efforts on the server (or backend). The API is the most accessible point of attack because it is inherently a public interface for interacting with the server. An inadequately secured or improperly implemented API is essentially a red carpet for hackers. Ensuring robust API protection while developing on backend is crucial to prevent unauthorized access and potential data breaches.

However, securing APIs is particularly difficult due to their dynamic nature and extensive integration. Key API security challenges include managing diverse endpoints, implementing robust authentication and authorization, dealing with external integrations that increase the attack surface, balancing rate limiting and throttling, and keeping pace with evolving threats like scraping and injection attacks.

Meanwhile, API breaches can have significant consequences, and no one is completely immune to them. Over the years, major players like Facebook, GitHub, Twitter, Peloton, and Experian have been targeted by hackers. Among the most recent high-profile cases are the breaches of T-Mobile and Duolingo:

1. T-Mobile has experienced eight major security breaches since 2018, with the latest incident in November 2022 exposing the personal data of about 37 million customers. The attackers accessed names, birthdates, billing addresses, phone numbers, and account details through a company API, likely exploiting an unpatched authorization vulnerability; although sensitive data like passwords and social security numbers were not leaked, the exposed information still poses significant risks for phishing and identity fraud.
2. In January 2023, data from 2.6 million Duolingo users, including email addresses and usernames, was scraped from the company’s API and appeared on a dark web forum. The API vulnerability, due to inadequate authentication and authorization, allowed access to user information without proper verification, leading to significant privacy risks and potential misuse for phishing and social engineering attacks.

It’s important to note that  API breach statistics is often delayed, with system hacks taking up to six months to detect. Therefore, addressing API security threats requires a proactive, comprehensive approach to ensure data integrity and protection.

API security best practices

Securing API endpoints involves methods and technologies to protect the public interface from attacks, safeguard confidential data, ensure authorized access, and prevent data leaks, thus maintaining the integrity and reliability of applications.

OWASP API Security Top 10 lists the most serious vulnerabilities and provides guidelines on how to prevent them. Here is a brief overview along with recommended solutions:

By implementing these best practices, you will address major API security vulnerabilities and protect yourself from data breaches and reputational damage.

API security standards

As digitization expands and systems become more interconnected, the importance of API governance and adherence to standards continues to rise for businesses. ISO/IEC 27001 sets the gold standard for information security in software development, offering a robust framework for organizations to establish and maintain effective information security management systems (ISMS). Additionally, it’s advisable to incorporate the following technologies:

• OAuth 2.0 and OpenID Connect protocols recognized as API authentication best practices. They are used for secure authentication and authorization processes, ensuring trusted access to resources.
• JSON Web Tokens (JWT) facilitate secure data transmission between parties.
• Transport Layer Security (TLS) encrypts communication between clients and servers, safeguarding data integrity.
• Cross-Origin Resource Sharing (CORS) mechanisms specify permitted origins for accessing resources, enhancing web application security.
• Utilizing HTTP security headers like Content Security Policy (CSP) and X-Content-Type-Options helps mitigate various attack vectors.

By properly integrating standards into the application architecture, you can ensure the protection of critical business functions.

Securing the API lifecycle through testing and DevSecOps

Creating a secure API lifecycle demands an integrated approach that addresses security concerns at every stage of development, from initial architectural design to final system deployment. Implementing a robust API security strategy, which involves meticulous assessment of all endpoints and the establishment of stringent security policies, serves as the foundation for ensuring data protection. However, it’s not sufficient to merely set up rules and adhere to API security best practices; it’s essential to validate that these measures function as intended. This necessitates integrating security testing during both the development and operational phases, with particular emphasis on the latter to handle real, potentially confidential data effectively.

API security testing involves vulnerability assessment, penetration testing, authentication and authorization checks, data validation, encryption verification, session management evaluation, and error handling assessment. The tests can be conducted manually or through the use of security scan tools such as OWASP ZAP (Zed Attack Proxy), Burp Suite, Postman, SoapUI, and others. Automating security testing has its own advantages: such tests can be incorporated into CI/CD pipelines. Additionally, you can utilize specialized security frameworks ensuring Interactive Application Security Testing (IAST), such as Checkmarx, and Runtime Application Self-Protection (RASP), such as Fortify.

Incorporating security testing during development and fortifying the infrastructure with various security tools and protocols is crucial for enhancing overall API security management. In addition to deploying the necessary environments, the DevSecOps approach integrates continuous logging and monitoring and enforces security policies from the outset of the development cycle through deployment and maintenance. These measures help identify and address potential API security vulnerabilities before they escalate into serious threats.

Trends for securing APIs

The bottom line

Understanding the latest methodologies and technologies is crucial for knowing how to secure API effectively. However, staying abreast of recent advancements in software development, particularly in cybersecurity, can be challenging. Timspark professionals offer assistance at every stage of the development lifecycle, from architectural design to secure deployment. Together, we can create exceptional software solutions.

References

1. T-Mobile hacked to steal data of 37 million accounts in API data breach. Bleeping Computer LLC, 2023. 
2. Hackers exposed 2.6 million Duolingo users, more available for scraping. Cybernews, 2023. 
3. OWASP Top 10 API Security Risks – 2023. OWASP, 2023. 
4. ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection — Information security management systems — Requirements. ISO, 2022. 
5. SAST, DAST, IAST, and RASP: Pros, cons and how to choose. Open Text Corporation, 2024. 

In Kubernetes deployments, certain missteps happen frequently. We have asked our in-house DevOps specialist, Mikhail Shayunov, about these typical blunders and their origins. Also, Mikhail has covered possible effective solutions and preventative measures to update your Kubernetes deployment strategy. Delve in and see how you can make deployment a more streamlined process in your company.

What’s the biggest mistake Kubernetes developers make?

In the Kubernetes deployment strategy, insufficient attention is often paid to resource planning. Sometimes, I encounter clusters in which developers have yet to describe the requirements for limits and requests, have not configured horizontal pod autoscaling, or have not correctly calculated the capacities of worker nodes. All this leads to hard-to-diagnose application issues. The reverse side of this problem is that developers request many times more capacities than necessary, which does not negatively affect performance but dramatically increases the difference in the cost of the solution compared to the classic architecture.

Another significant and often overlooked error in Kubernetes development is the failure to specify CPU and memory usage limits and requests. Requests refer to the minimum resources required for an application development, while limits define the maximum resources a container can utilize. Not setting these limits can overload worker nodes, resulting in poor application performance. Conversely, setting too low limits can lead to CPU underperformance and OOMkill errors. Both cases result in Kubernetes becoming ineffective.

A lack of resource control also means the application is not adequately monitored, making it challenging to identify and resolve potential issues or bottlenecks.

What can be a game-changer if the company wishes to update its Kubernetes deployment strategy?

Consider the following rules of thumb to avoid typical pitfalls with Kubernetes.  

Create a test environment to check application launches

Define correct performance parameters, allocate the right amount of CPU and memory, and define metrics for usage in Horizontal Pod Autoscaler (HPA) for resource management and scalability.

Remember to put the correct settings of node pool autoscaling on the cloud side. For instance, HPA is usually implemented as a deployment entity within a cluster. It automatically adjusts the quantity of Kubernetes nodes in your cluster according to your needs. If the number of pending pods increases, indicating resources in the cluster are inadequate, the cluster autoscaler automatically includes additional nodes.

Optimize resource allocation and scalability in Kubernetes

Effective workload management is crucial to prevent burnout and ensure successful project execution, especially for remote development teams. It requires understanding each team member’s strengths, weaknesses, and current capacity. To accurately estimate team workload, it’s crucial to establish initial task estimates, track remaining work for each task, and maintain a regular work log where team members record their work hours. This helps identify who is overloaded or underloaded, allowing you to redistribute tasks and balance the workload.

Conduct stress testing and automated tests

Stress testing on your application allows you to identify the threshold at which the system or software fails. By observing the system’s reactions in different scenarios, you can establish bottlenecks in the defined autoscaling policies and dedicate resources as requests and limits, ensuring consistent service and optimal performance.

What’s the best way to avoid future mistakes?

When starting, never allow manual changes to infrastructure or configurations.
For infrastructure, use Kubernetes deployment tools, for instance, infrastructure-as-code (IaC), and for deploying components within Kubernetes, use templating tools such as Helm or Kustomize. Additionally, applying thinking-forward thinking and using DevSecOps tools might help you significantly in avoiding possible pitfalls related with security.

Set up repositories for this code in any version control system you are comfortable with and CI/CD pipelines for automatic changes. In general, this practice will help identify the causes of bugs and address them more effectively.

In the long run, Kubernetes is not an orchestration tool to be afraid of. It will bolster you with a different level of confidence, beating other technologies by a mile. The flexibility and stability it provides will significantly enhance your overall application performance.

References

1. Resource Management for Pods and Containers
2. What is Infrastructure as Code (IaC)