DEV Community: Instinctools

Computer Vision in Manufacturing: Why Pilots Fail and What A Production-Grade Defect Detection System Requires

Instinctools — Wed, 30 Sep 2026 09:50:16 +0000

For decades, Automated Optical Inspection (AOI) served as the primary bridge between error-prone manual checks and high-speed production.

Yet, as component densities increase and assembly tolerances shrink, classic rule-based AOI systems hit an operational wall. When engineering leaders attempt to stretch these legacy platforms to detect micro-scale anomalies or handle rapid product changeovers, the architecture fractures under high false-positive rates and constant recalibration needs.

This friction has driven manufacturers toward computer vision. Yet a significant share of AI visual inspection projects stall at the proof-of-concept stage, often because of poor engineering decisions. What does it take to get them right and build a production-grade computer vision system for defect detection in manufacturing? This article covers that, along with the benefits of having one.

Limitations of traditional AOI that modern AI visual inspection overcomes

To understand why computer vision is replacing machine vision, it helps to examine where rule-based AOI fails at scale.

Classic AOI relies on explicit, hard-coded geometric and pixel-intensity rules like edge detection, color thresholding, template matching, etc. While effective for standard, highly predictable assemblies under fixed parameters, these systems carry fundamental vulnerabilities:

Overdependence on consistent imaging: Rule-based algorithms expect absolute photometric stability. Slight shifts in ambient factory lighting, lens dust, or minor camera mount vibrations distort threshold calculations, causing false alarm rates to skyrocket.
Rigid inspection logic: Operating on strict if-else logic, AOI cannot analyze statistical visual trends or self-adjust parameters over time. It evaluates every frame in complete isolation.
Slow changeover adaptability: Any change in component placement, board color, or physical dimensions requires manual parameter retuning, baseline re-establishment, or completely rebuilding static templates.
Low scalability across facilities: A rule-based calibration tuned for Line A cannot be copied directly to Line B. Differences in lens wear, lighting angles, and mounting tolerances require manual engineering on every single line.
High maintenance overhead: Quality engineers waste billable hours manually overriding false alarms, adjusting sensitivity parameters, and maintaining static rule sets, effectively turning automated systems back into semi-manual workflows.

Done right, computer vision quality inspection pays off on two fronts

The payoff of AI visual inspection in manufacturing starts on the factory floor and works its way up to the bottom line.

On the production side, the gains show up in the way defects are caught, handled, and kept from moving further down the line:

Inspection accuracy improves as computer vision becomes better at recognizing small, irregular, and visually variable defects that fixed rules can struggle to capture.
Production throughput increases when defects are identified at the inspection point rather than discovered further downstream, where diagnosis and rework can slow the line.
False positives decrease when the model and its inspection thresholds are calibrated to distinguish genuine defects from acceptable product variation.
Scrap and rework fall as defects are caught before additional materials, labor, and processing are invested in a defective unit.
Fewer defective products reach customers, reducing the downstream burden of defect-related warranty cases.

The same operational gains eventually find their way into the P&L, showing up as lower costs and better economics across the production lifecycle:

Waste-related costs decline as fewer units, materials, and hours of production capacity are lost to defects and rework.
Quality-control labor becomes less costly as automated inspection takes over repetitive checks and quality engineers spend more time on exceptions and process improvement.
Warranty costs come down when more defects are intercepted before products leave the factory and turn into repairs, replacements, or service cases.

We saw this impact firsthand after replacing a Saudi-based electronics manufacturer’s legacy rule-based machine vision system with a YOLO-based computer vision solution for early-stage defect detection on the production line.

The new system boosted inspection accuracy by 27,4%, while immediate and precise defect detection helped increase production throughput by 24%. Additionally, the false positive rate fell below 2% and the rate of discarded items dropped below 0,5%, resulting in $1,2 million in waste-related cost savings. Furthermore, a 67% reduction in defect-related warranty claims saved an additional $2,4 million. Finally, quality control labor costs were reduced by 26%.

Build vs. buy: how to choose between turnkey AI vision platforms and custom CV engineering

Manufacturing leaders have two broad ways to implement computer vision quality control:
1) buy a ready-made AI vision inspection platform and configure it for the line,
2) develop a custom CV-based defect detection solution around their particular products, defects, cameras/edge hardware, data, and factory integrations.

Which approach makes sense depends on five practical questions:

How stable is the production line?

A commercial platform can work well when the product, camera setup, lighting, and inspection conditions remain largely unchanged. A food manufacturer running the same packaging format for long production runs is a different case from an automotive plant switching frequently between models, components, and inspection configurations.

How specific are the defects?

Off-the-shelf platforms are easier to apply when defects are visually distinct and well defined, such as a missing cap, damaged package, or obvious surface crack. Custom computer vision services become more relevant when the system must distinguish subtle, product-specific defects, such as minor surface imperfections on a painted car part that are acceptable in one area but rejectable in another.

How deeply must the system integrate with production?

A ready-made platform may be sufficient when inspection ends with a pass/fail decision or a signal to reject the product. More extensive requirements, such as linking defects to a batch and production stage, writing results to the MES, or triggering different rework, quarantine, and scrap workflows, can call for a more customized architecture.

Who will maintain and retrain it?

Every vision model needs care after launch: new product variants, new suppliers, and gradual line drift all mean retraining. So the real question is who does that work, and how often.
If your quality engineers will handle it and the changes are routine, such as adding fresh images, relabeling, or adjusting thresholds, buy. Platforms are built for exactly this, and no ML background is needed.
If retraining goes beyond that, build. Examples are new defect classes, small-defect accuracy problems, or performance that needs architecture-level changes. A platform only lets you adjust what its tooling exposes, so you need people who can go deeper.

How quickly does it need to reach production?

Buying a platform can shorten deployment when the inspection task is straightforward and fits its existing capabilities. Building becomes a larger upfront commitment when the project requires custom models, multiple inspection stages, edge optimization, or deep factory-system integration, but it also gives the team control over how those components work together.

Taken together, these factors make commercial platforms such as Cognex, Keyence, LandingAI, and Roboflow a practical choice when the inspection task is well defined, production conditions are relatively stable, and the platform’s configuration and integration options cover what the factory needs.

Custom computer vision solutions for manufacturing make more sense when several of those conditions work against a ready-made platform: products and defects change frequently, inspection requires product-specific logic, the system must integrate deeply with factory software, or the manufacturer needs total control over model training, deployment, and ongoing optimization.

Engineering non-negotiables: 8 steps to a production-grade computer vision defect detection system with near-perfect accuracy

Scaling a visual inspection system from a laboratory demo to an inline production environment requires a systematic engineering pipeline focused on model topology, dataset quality, edge optimization, and enterprise integration.

1. Choosing an object detection and image classification model

First of all, engineering teams must decide on the underlying learning paradigm based on data availability, defect predictability, and inspection goals:

Pre-trained transfer learning: Starts with backbone networks trained on millions of generic images, leveraging learned visual primitives (edges, gradients, textures) to quickly fine-tune on target manufacturing defects.
Unsupervised anomaly detection: Models like Autoencoders or PatchCore train exclusively on defect-free parts. This approach is ideal when defects are extremely rare, unpredictable, or take infinite visual forms, though it flags anomalies without explicitly categorizing them.
Pixel-level segmentation: Architectures like U-Net or Mask R-CNN isolate defects at the exact pixel level rather than drawing rectangular bounding boxes. This is essential when exact geometric properties (such as solder paste volume or scratch surface area) dictate part quality, albeit at higher computational latency.

For most high-speed inline conveyor setups where defects are discrete and well-categorized, fine-tuning a pre-trained object detection framework provides the optimal trade-off between development speed and edge performance.

2. Selecting a type of detector to perform object detection

Selecting the right model architecture determines whether an inspection pipeline can run at line speed without sacrificing feature resolution.

Two-stage detectors (e.g., faster R-CNN): Isolate region proposals in stage one, then classify and refine bounding boxes in stage two. While precise for dense, low-speed visual tasks, their multi-pass architecture introduces computational latency that creates bottlenecks on fast-moving conveyors.
Single-stage detectors (e.g., modern YOLO variants, SSD): Predict bounding boxes and class probabilities simultaneously in a single forward pass.

For real-time inline defect detection, modern single-stage architectures like YOLO offer the most practical balance: ultra-low inference latency, reduced edge hardware footprint, and real-time processing capabilities.

3. Optimizing the training dataset

Computer vision pilots frequently stall because training sets reflect real-world distributions where defect-free images outnumber defective samples by orders of magnitude. Furthermore, specific defect types (such as cold solder joints or micro-bridges) occur far less often than simple placement errors.

To resolve this imbalance without waiting months to collect rare real-world failures, production pipelines rely on targeted synthetic augmentation:

Data augmentation: Applying controlled geometric transforms, photometric jittering, synthetic noise injection, and background variations multiplies training samples (often up to 10x) while balancing class distributions.
High-precision labeling: Annotation noise severely degrades model accuracy. Bounding boxes must strictly enclose target features without grouping adjacent components into a single box.
Negative sampling: Explicitly including defect-free images conditions the model to ignore background noise and component variations, keeping false positive rates low.

4. Additional model training on the updated dataset

General-purpose models are trained on very large volumes of everyday images. They understand edges, textures, and shapes, but not your solder joints, scratches, or misaligned parts. Transfer learning keeps that general knowledge and retrains the model on your task-specific data, usually by replacing the classification head and fine-tuning on your labeled defects.

Compared with training from zero, it cuts training time and compute significantly and works with less data.

5. Calibrating the defect inspection threshold

A model’s raw output is a set of spatial candidate boxes and probability scores. Turning these into reliable pass/fail decisions requires post-processing calibration:

Confidence threshold tuning: Elevating confidence ratios filters out low-certainty detections, keeping low-confidence anomalies from causing false line stops.
Non-maximum suppression (NMS): Tuning NMS parameters ensures overlapping candidate boxes are suppressed, leaving a single high-confidence bounding box per defect.
Defect severity scoring: Classifying defects by severity allows non-functional cosmetic imperfections to be handled differently than critical structural flaws, preventing unnecessary scrapping.

6. Applying techniques to maximize the model accuracy

Once the fundamentals are in place, architecture-level improvements can close the remaining gap, especially for small or subtle defects. Common examples in single-stage detectors include:

Depthwise Separable Convolution (DSConv) to accelerate inference.
The Cross-Stage Partial Network (C3 module), which combines low-level detail with high-level semantic information and helps the model handle targets of varying scale.
The Bidirectional Feature Pyramid Network (BiFPN), which improves recognition of the fine features of small targets.
The DySample upsampling operator, which reduces detail loss and boosts accuracy on small targets.

Mind that these are refinements. They pay off when the basics (data, labels, thresholds) are already sound, and they can’t make up for weak foundations.

7. Integrating with the MES

A model only creates value when its output changes what happens on the line. That means connecting detection results to the Manufacturing Execution Systems (MES), so each finding is recorded by type and severity, and linked to the batch, station, and shift.

Placement matters too. Inspection points typically sit at the process steps where defects are cheapest to catch. In electronics assembly, for example, that’s after solder paste application, after component placement, and after reflow.

The equivalents differ by industry, but the principle holds: inspect where a defect can still be caught before more value is added.

8. Ongoing model updates, configuration changes, and CV algorithm optimizations

Products change, suppliers change, and lines drift. A production system needs a path for continuous improvement: collecting operational data and outputs, retraining on new defect types and variants, adjusting configuration, and optimizing algorithms. Cloud connectivity makes much of this possible remotely for MLOps teams. Without it, the system’s accuracy decays and you’re back to the maintenance burden that made automated optical inspection expensive.

Where computer vision earns its place on the floor

While inline quality inspection provides the clearest initial ROI, an integrated computer vision architecture can be extended across the entire manufacturing lifecycle. These are common computer vision applications in manufacturing:

Process monitoring: Tracking assembly steps, material movement, cycle times, and machine behavior to highlight process bottlenecks.
Dimensional measurement: Executing high-precision, non-contact measurements of tolerances, gaps, angles, and physical alignments.
Machine and robot guidance: Sending real-time spatial coordinates to robotic arms for precision assembly, bin picking, and automated part orientation.
Assembly verification: Verifying component presence, placement order, hardware insertion, and final build completeness.
Asset and material tracking: Scanning and tracking Work-in-Progress (WIP) parts, pallets, and material batches across the floor using OCR and barcoding.
Safety monitoring: Monitoring restricted zones, checks worker PPE compliance, and alerts managers to unsafe human-machine proximity.
Equipment condition monitoring: Detecting early visual indications of mechanical wear, fluid leaks, belt misalignments, or thermal anomalies on critical machinery.
Packaging and labeling verification: Checking seal integrity, label placement, print legibility, and pallet stacking structures.
Production optimization: Converting continuous video feeds into actionable metrics to balance line speed, equipment utilization, and material flow.

Explore more computer vision use cases in manufacturing and other industries >>

Zero-defect manufacturing starts with the right engineering foundations

Switching from rule-based AOI to an advanced computer vision solution for early-stage defect detection enables manufacturers to reach near-100% inspection accuracy, saving millions annually through reduced scrap and fewer downstream warranty claims. However, capturing this value requires a production-grade deployment, one built on precise tech stack selection, dataset optimization, targeted transfer learning, and fine-tuned threshold calibration.

Stuck with a stalled pilot or planning a computer vision automation initiative? Instinctools’ computer vision consulting services help you pinpoint exactly what broke down or engineer resilient, production-ready computer vision systems for manufacturing from the ground up.

Healthcare & IT: Medical standards in IT based on HIPAA

Instinctools — Thu, 27 Jul 2023 09:25:26 +0000

Post-COVID-19 reality for healthcare

The COVID-19 pandemic has sparked a wave of innovation and technological advancements in healthcare. As we move forward, several exciting trends will reshape how technology is used in healthcare. These include telehealth (remote medical consultations), artificial intelligence and machine learning (using computers to assist doctors and improve diagnoses), data security (protecting patient information), the Internet of Medical Things (connecting medical devices to share data), blockchain (a secure and transparent way to store and share medical records), and virtual reality and augmented reality (using immersive technology for medical training and patient education).

These trends have the potential to greatly improve patient care and outcomes. For example, telehealth allows patients to see a doctor from the comfort of their own homes, saving time and increasing access to healthcare. Artificial intelligence can help doctors make more accurate diagnoses, leading to better treatment plans. Data security measures are crucial to protect patients’ sensitive information from cyber threats. The Internet of Medical Things connects medical devices to share important data, enabling doctors to monitor patients remotely. Blockchain ensures the privacy and integrity of medical records, while virtual reality and augmented reality enhance medical training and help patients better understand their conditions.

It’s important for healthcare organisations to adapt to these trends and prioritise solutions that put patients at the centre. By embracing new technologies, healthcare providers can deliver better care, improve patient outcomes, and meet the changing needs of our post-pandemic world. As technology continues to advance, it will play an increasingly vital role in shaping the future of healthcare.

IT infrastructure modernization trend in healthcare industry

In 2023, healthcare organisations are adopting new approaches to update their outdated IT systems.
One significant trend is cloud migration, where organisations move their systems to the cloud. This shift helps reduce costs and improves scalability, security, and reliability. The cloud offers a modern infrastructure that can adapt to the changing demands of the digital age, enabling organisations to modernise their legacy systems.

Another approach is the adoption of microservices architecture. This modern software development method breaks down large systems into smaller, independent services. By doing so, healthcare organisations can gradually update their IT infrastructure, improving agility and better responding to evolving needs.

Additionally, the use of low-code and no-code platforms is gaining popularity. These platforms allow non-technical users to build and deploy applications without writing a code. Healthcare organisations can leverage these platforms by quickly creating new applications and integrations, facilitating IT modernization and enhancing the quality of care.

Overall, these trends in 2023 demonstrate the healthcare industry’s commitment to modernise its IT infrastructure, leading to improved efficiency and better patient outcomes.

Why is it important to follow digital health apps development standards?

When implementing apps and IT systems in the healthcare industry, it is crucial to follow standards. Here’s why:

1. Easy and safe communication.

Standards ensure that different systems can talk to each other and share important information. This means that healthcare providers can easily access patient records, test results, and other data when they need it. It helps doctors work together and provide better care for patients.

2. Accuracy and trustworthiness.

Standards provide guidelines for how data is collected, stored, and shared. By following these guidelines, healthcare organisations can make sure that the information they have is accurate and reliable. This helps doctors make the right decisions and ensures that patients receive the best care possible.

3. Protecting privacy and security.

When it comes to healthcare, keeping patient information safe is incredibly important. Standards help healthcare organisations protect patient privacy and secure their data. By following these standards, organisations can prevent unauthorised access to patient records and keep sensitive information secure.

4. Meeting legal requirements.

The healthcare industry is regulated by various laws and regulations. Following standards helps healthcare organisations stay compliant with these rules. This ensures that they avoid legal issues and penalties and maintain a good reputation.

5. Making systems work together.

Standards make it easier for different systems and applications to work together. They define common rules and requirements, allowing healthcare organisations to integrate new technologies smoothly. This means that organisations can adapt to changes and take advantage of new innovations in healthcare.

By following standards, healthcare organisations can ensure that different systems can communicate effectively, protect patient privacy and security, and meet legal requirements. It helps create a more efficient and secure healthcare system, leading to better care and improved patient outcomes.

What is HIPAA compliance in software development?

When developing software for healthcare, it’s important to comply with HIPAA regulations. HIPAA, the Health Insurance Portability and Accountability Act, sets rules to protect sensitive patient information in the United States.

HIPAA compliance in software development means following specific guidelines to ensure the privacy and security of patient’s health data. Here’s what it involves:

1. Keeping data safe. Software developers must implement strong security measures to protect patient information from unauthorised access. This includes encrypting data, controlling who can access it, and ensuring secure storage and transmission.

2. Secure user access. The software should have strong authentication methods to make sure only authorised individuals can access patient data. User access privileges should be carefully managed to prevent any unauthorised use.

3. Monitoring and tracking. The software should maintain detailed logs of user activity and system access. This helps to monitor any unusual or unauthorised actions and ensures accountability.

4. Assessing and managing risks. Developers should regularly assess potential vulnerabilities in the software and take steps to minimise risks. This involves conducting security assessments, identifying and fixing vulnerabilities, and keeping the software up to date.

5. Working with Business Associates. If the software is used by a business associate of a healthcare organisation, there should be an agreement in place that outlines their responsibilities for protecting patient information.

6. Training and education. Developers and others involved in the software development process should receive training on HIPAA regulations and best practices to ensure they understand their role in protecting patient privacy.

By following these guidelines, software developers can help safeguard patient information and ensure that healthcare organisations meet HIPAA requirements. This ensures the privacy and security of patient data, promoting trust and confidence in the healthcare system.

How to check the application HIPAA compliance?

Ensuring the compliance of an application with HIPAA requirements involves a series of steps to safeguard patient health information. The following GitHub page has a detailed procedure for creating HIPAA-compliant apps. Here’s a simplified overview of the process:

1. Understanding HIPAA requirements. Gain familiarity with the regulations and guidelines associated with software development and data security under HIPAA. This knowledge will help you identify the specific requirements that must be tested for.

**2. Identifying relevant standards. **Determine which HIPAA standards are applicable to your application, such as those concerning security, privacy, breach notification, and data storage. Understanding these standards will guide your testing approach.

3. Conducting a risk assessment. Perform a comprehensive assessment to identify potential vulnerabilities and risks to patient data. Evaluate the application’s infrastructure, access controls, data storage, and transmission mechanisms to pinpoint weaknesses and areas in need of improvement.

4. Developing test cases. Based on the identified HIPAA requirements and risk assessment findings, create specific test cases that cover each requirement. These test cases should simulate scenarios that validate compliance, such as testing data encryption, user access controls, auditing, and breach notification procedures.

5. Testing data security. Evaluate the application’s data security measures, including encryption, authentication, and access controls. Test different user roles and permissions to ensure the proper protection of sensitive patient information.

6. Testing data transmission. Verify that data transmission between the application and external systems is secure and aligns with HIPAA requirements. Test the encryption and integrity of data during transmission to prevent unauthorised interception or tampering.

7. Assessing auditing and logging. Confirm that the application maintains detailed audit logs and tracks user activities. Ensure that these logs capture relevant information for monitoring and auditing purposes.

8. Testing incident response and breach notification. Evaluate the application’s ability to respond to security incidents and breaches. Test the procedures in place for detecting, reporting, and addressing breaches, including timely notification to affected parties as mandated by HIPAA.

9. Documentation and compliance reporting. Maintain comprehensive documentation of your testing efforts, including test plans, test cases, and results. This documentation serves as evidence of your compliance endeavours and can be valuable during compliance audits.

10. Regular monitoring and updates. HIPAA compliance is an ongoing endeavour. Continuously monitor and update your application’s security measures, policies, and procedures to adapt to evolving threats and changing HIPAA regulations.

It’s crucial to note that testing for HIPAA compliance can be intricate, and it is advisable to involve professionals with expertise in healthcare compliance and data security. Consider consulting with experts or engaging third-party auditors to ensure a thorough and accurate evaluation of your application’s compliance with HIPAA requirements.

Requirement elicitation process for medical applications

When developing a healthcare application, ensuring its security is of paramount importance. To elicit requirements on security for such an application, a systematic approach is necessary. Here are steps to guide you through the process:

1. Identify key stakeholders. Determine the individuals or groups who have a vested interest in the healthcare application’s security. This may include healthcare professionals, patients, administrators, IT staff, and compliance officers.

2. Understand regulatory compliance. Familiarise yourself with the relevant regulations and standards governing healthcare data security, such as HIPAA and GDPR. These regulations provide guidelines for protecting patient information and ensuring privacy.

3. Engage stakeholders. Conduct interviews and workshops to gather insights from stakeholders. Understand their concerns, expectations, and requirements regarding security. Encourage discussions on topics like data protection, access controls, encryption, authentication, and secure communication.

4. Analyse potential threats. Conduct a thorough analysis of potential security threats specific to healthcare applications. Consider risks such as unauthorised access, data breaches, malware attacks, and insider threats. Identify necessary security measures and controls to mitigate these risks.

5. Prioritise privacy. Give due consideration to privacy and confidentiality requirements. Recognize the sensitivity of patient data and assess the need for measures like anonymization, pseudonymization, or encryption to protect patient privacy.

6. Seek expert guidance. Collaborate with cybersecurity experts or consultants specialising in healthcare security. Their expertise can provide valuable insights and ensure adherence to industry best practices.

7. Refer to established standards. Review established security frameworks and standards specific to healthcare, such as NIST guidelines or HITRUST framework. These resources offer comprehensive security controls and practices that can serve as references.

8. Document and validate requirements. Document elicited security requirements in a clear and structured manner. Ensure they are specific, measurable, achievable, relevant, and time-bound (SMART). Validate these requirements with stakeholders to confirm their accuracy and alignment with expectations.

9. Analyse and prioritise. Analyse the elicited requirements to identify dependencies, conflicts, and potential trade-offs. Prioritise them based on their criticality, impact on security, and feasibility. Consider the associated cost and effort for each requirement.

10. Stay updated and evolve. Acknowledge that security requirements evolve over time. Stay informed about the latest security threats, regulations, and best practices. Continuously communicate and collaborate with stakeholders to update and improve security requirements throughout the development process.

Following these steps will help ensure that your healthcare application is developed with robust security measures, safeguarding patient data, preserving confidentiality, and complying with regulatory standards.

How to build medical applications which correspond to security standards?

Firstly, thorough research must be conducted to understand regulatory compliance requirements. Different regions and functionalities may fall under specific healthcare regulations and standards. Protected health information typically includes insurance-related data, medical information, personal patient data, appointment dates, medical histories, prescription history, and other sensitive information. Compliance research should be done early on to identify applicable regulations.

For the US market, medical apps storing or transmitting protected personal and medical data must comply with HIPAA. Similarly, the European market requires adherence to GDPR, while Canada follows PIPEDA. Understanding legislative norms is crucial to develop and distribute apps that meet security requirements authorised by governmental legislation.

Encryption plays a vital role in ensuring the confidentiality of eHealth information. By encrypting sensitive data using algorithms and encryption keys, the security of a medical mobile app can be safeguarded. Transport Layer Security (TLS) and Secure Socket Layer (SSL) are cryptographic protocols commonly used for secure communication between a server and an application.

Implementing multi-factor authentication (MFA) adds an extra layer of security. Two-factor authentication (2FA) requires users to present separate pieces of evidence to access data, such as a password and a secondary component like fingerprint or voice identification.

Comprehensive testing, including security testing, is crucial before launching a medical mobile app. Vulnerability testing should cover areas such as weak server-side control, insecure data storage, insufficient transport layer protection, unintended data leakage, poor authorization and authentication, broken cryptography, client-side injection, security decisions via untrusted input, improper session handling, and lack of binary protections.

To protect against attacks, developers must be aware of different types of attackers and their methods. This includes hackers who exploit technical vulnerabilities, social engineers who manipulate human psychology, and man-in-the-middle (MITM) attackers who intercept communications. Preventive measures, secure transmission of data, and up-to-date security protocols like TLS are essential.

Long-term support and maintenance are necessary to address future vulnerabilities and technological advancements in the constantly evolving healthcare industry. Post-release support ensures updates and security maintenance.

Aliaksandra Myslivets, BA Specialist *instinctools

Cloud Migration Team: Why Establish It and Who to Involve

Instinctools — Fri, 15 Oct 2021 09:50:17 +0000

Cloud migration is a process where an enterprise transfers its data center capacities to the cloud. The cloud provides online computing resources like data storage and computing power in IT, which the user does not directly manage. As the world adjusts to remote industries, cloud movement has shifted from a far-off possibility to an undeniable truth for almost all enterprises.

The Flexera 2021 State of the Cloud Report deduces that 59% of corporations intend to shift their computing resources to the cloud. Additionally, the Cloud Guru survey in 2020 confirmed that above 80% of cloud administrators speculate a deficiency of competent in-house personnel within the crews to be a significant limitation to cloud advancement. They also point out that it is virtually impossible to successfully transfer cloud services without IT experts to execute the move.

In earlier articles, *instictools has highlighted the perks of cloud migration and explained the latent costs associated with it alongside dividing the different cloud designs available to assist you with selecting the best for your venture. But, understanding the benefits and selecting a suitable model won’t do much to your business without the personnel to execute the work efficiently and ensure you get the best from your cloud experience. Now it is time to address the cloud transfer personnel issue.

Why a Competent Cloud Migration Team is Essential

Every IT-centered venture desires to have its cloud-related initiatives brought to fruition. For this initiative to materialize, you require a dedicated team of cloud professionals with vast experience. From this team, your business will undoubtedly encounter a myriad of benefits. They encompass:

Saving on Expenditure, Pace, and Strain

You employ an architectural crew’s services to design a structure for you though you may as well do it yourself. Why do you decide to engage the seasoned masons? The answer is transparent; you recognize that specialists who know their craft will undoubtedly do a better job than you. This principle still connects to cloud movement since there are various gradations your native crew may be unfamiliar with.

For instance, are you familiar with the Virtual Machine (VM) size that is excellent for your tasks at hand? Choosing the VM whose power is more than you require is replete with redundant costs. Doing this will make you spend far too much than you should. As a result, you will conclude that the cloud is “too costly” when the secret is you require a more prudent approach.

A cloud supervisor at *instinctools Petr Spirichkin states that judging cloud shifting to be a simple task is the biggest delusion, whereas, in reality, it is not applicable for individuals with little cloud understanding. Those with less experience in cloud gauging hardly look out for Service Level Agreements (SLA). This document shows that the producer and the client acknowledge the details and settings for supplying and utilizing the cloud utility, each agreeing to their commitments.

SLA interprets the degree of utility the producer conveys and reimbursement if the specs are not satisfied. An amateur may view the cloud utilities to be relatively straightforward, but with slight variations, some areas need detail-negotiation and customization perpetually. They include pricing, data retention, and compensation.

From the data above, 73% of enterprises make it their prime initiative to optimize cloud costs a year earlier. Concurrently, 30% of customers concede to misuse cloud expenses. Undeniably, the cloud provider costs framework may seem too complex to translate, although the vigilant diagnosis of allowances can unearth chances to cut on expenditure.

Establishing Protection for the Enterprise

If failing to deduce chances of expenditure contraction is troublesome yet tolerable, handling surveillance problems is different. Cloud shifting showcases the adjustment to a wholly improved functional model. Therefore, it is customary to diversify the approach to surveillance of the venture’s input and expertise standard.

To safeguard the security of your cloud, and therefore managing your enterprise’s functionality, hire a crew that has vast knowledge about how the Application Programming Interface (API) and Command-Line Interface (CLI) appliances work. Your team should also have a deep understanding of cloud density fundamentals, container safety, and a clear short and long-term vision of the condition of your cloud, in general.

According to Petr Spirichkin, *instinctools, you or your IT personnel might lack copying and scheme retrieval knowledge. As a result, you may assume all is well until the unexpected occurs and the input conservatory collapses. Input center blackouts manifest complications such as high finances, loss of input, and free time. As bad as it looks, simulating your VM to another region will counter this matter effectively. For instance, Microsoft provides an innate recovery utility - Azure Site Recovery, that helps maintain business stability by safeguarding running tasks at hand and business applications during blackouts.

Always Abiding by your Track

Going off track will easily map you out of your route. It is similar to cloud transfer - lacking a thorough plan will likely get stranded in an entirely advanced functional model. This reason makes a well-deduced cloud shifting approach a prerequisite.

A strategic layout drafts crucial steps of the cloud incorporation procedure and outlines the hazards that could arise later. Establishing a sturdy arrangement is not as fraught as it seems, and you will probably fail to develop one without aid from capable specialists since there is no prevalent remedy. You cannot use a common approach to transfer all your IT fortes to the cloud since business needs are unique, and each needs a scheme that will ensure it remains operational even as the shift proceeds. A coherent arrangement focuses on answering questions concerning the migration method, what to move, and the system it has to shift.

Countering Lags in Business

Diligently watching over your cloud transfer program is beneficial unless it is hazardous for the enterprise that needs you to manage it. Failing to be alert with your immediate obligations will cause significant setbacks in your business. By resorting to a cloud movement expert, you get enough time to target crucial matters and erase the danger of conduction transfer elements alone.

The Cloud Transfer Crew Format

The crew’s organization varies depending on elements like cloud transfer methods, which, consecutively, are contingent on the scale of the venture and the input you want to transfer. For instance, if you move some software to the cloud, employing an entire team is unnecessary - a cloud designer will suffice. But, contrarily, diverting a lot of input and workload to the cloud needs a strong team of specialized professionals.

Besides the individuals on your part responsible for vital answerability, here are professionals that will make your cloud transfer seamless, efficient, optimized, and all these done hustle-free.

Cloud Architect

Cloud planners are the foremost participants in cloud breakthroughs. Stating this fact would not be an overstatement. They are obliged to design settings in the software. They possess detailed aspects of cloud automation and can view the program from any angle to examine a complete visual.

The tasks of the cloud architect entail:

Establishing a cloud arrangement
Developing and managing cloud design
Determining cloud foundation expenses
Managing surveillance in terms of incident return strategy and privacy

Cloud architects are fully conscious of the alternatives and drawbacks of cloud utilities. Therefore, they will assist you with averting unexpected occurrences during your shifting process.

Cloud Engineer

A cloud engineer generally does technical tasks concerning cloud computations. They have various standings with distinct tasks such as systems, software, surveillance, and network engineers.

Their tasks encompass:

Handling the software and hardware making up the cloud-based utilities
Advocating, creating, and looking into technologies that avail surveillance for cloud-based computerized platforms
Establishing and managing software to operate on virtual units
Upgrading existing systems

Smaller companies will probably employ a cloud engineer for generality, while more prominent corporations need one with a specific area of experience.

Project Manager (PM)

Limitations are there to hinder your initiation into the cloud movement scheme timely and force you to exceed the predetermined budget. A PM’s prime responsibility is to overcome these obstacles and guarantee the crew meets the set deadlines and milestones.

What do PM’s do?

They come up with a cloud transfer scheme approach
Delivering cloud movement within capacity, cost, and schedule
Overseeing the procedure of deciphering IT objectives, guidelines, and venture prerequisites into prospective state architectures
Spotting detrimental factors to moving and offering remedies
Ensuring adjustment across squads ##Why Shifting to the Cloud With Your Crew is Necessary Are you fortunate enough to have a capable, dedicated crew of specialists above? If so, then nothing is stopping you. However, you may lack cloud experts or confidence in your in-house team’s abilities to conduct the movement to the cloud. In that case, you should employ a cloud migration professional.

Maintaining the ‘Do It Yourself’ concept will result in severe time wastage. The worst-case scenario is even more gruesome. For instance, your enterprise’s data can be lost, which could seriously damage your business. Additionally, you risk the revenue of your business due to the delays your team may experience, which translates to business downtime.

Lacking a crew of specialists with a qualified plan leads to numerous problems that many enterprises do not anticipate at first. Getting everything well executed at first is always a fluke. However, cloud transfer is fast, safe, and cost-friendly for those with a highly seasoned crew. That is what *instinctools offers; a competent team with diverse IT skills to help your venture successfully move to the cloud and enjoy its benefits.

HOW TO CHOOSE A PRIVATE BLOCKCHAIN… KIND OF THING

Instinctools — Wed, 18 Nov 2020 16:21:41 +0000

As soon as you’ve decided that blockchain or distributed ledger is the technology your business cannot flourish without, the question of choosing the right one between those two comes up. There are plenty of promising projects out there that can be adjusted to your needs. The only way of making up your mind is to take into account all the important criteria.

Structure & Data

If you want to share some data between nodes (connection points) and don’t need to keep an entire history of all the transactions (append-only data structure) you can go for a Distributed Ledger. It is a database that exists across several locations or among different participants. Although it doesn’t hit the headlines as Blockchain does, Blockchain is just one type of a Distributed Ledger. The main difference between them is that, unlike blockchain, a DL does not necessarily need to have a data structure in blocks. Being not dependent on the previous block, DL reaches a very good speed of transactions. The most vivid example of DL is R3 Corda.

Storing big files in the blockchain is not a great idea. Information is added in blocks, limited in size, so it is impossible to add a big package of data at a time. If you are sure you need not just hashes, but also tamper resistance, visibility, decentralization for your files, consider Storj and Sia.

Permissions

All enterprise-focused systems assure to be permissioned ones, but in most cases, it’s simple role management. Multichain – an open platform for building blockchains – has the following list of permissions: connect, send, receive, issue, mine, activate, admin. It’s a pretty good and flexible list, but when you create a smart contract with completely different entities and roles, this list won’t help. You won’t be able to specify the existing permissions, for instance, to grant different access according to the hierarchy (admin, super admin, owner, staff, guest) or ban a particular group of people from buying your product (e.g. to forbid those who are underage to buy alcohol), etc… So be ready to implement your own permissions system.

Database

Most chains save data in key-value storages like LevelDB. They are very fast and good at simultaneous writing, but to find and sort the information out you have to duplicate all the data in a separate general-purpose database and keep this copy up to date. However, there are blockchains where you don’t have have to do it. Hyperledger Fabric supports extended queries thanks to CouchDb, EOS, and BigchainDB thanks to MongoDB. Also, Credits has query capabilities with Credits DBMS.

Upgradability

Market conditions are changing and the conditions of deals are changing along with them. In this respect, immutability – one of the main advantages of blockchain – becomes a disadvantage that developers should cope with when they want to upgrade a smart contract. Migrating data to a new contract can take a lot of resources and lead to unwanted issues like errors, stealing the money from the contract account, changing the data by someone who isn’t supposed to do it, and many others. Thus, if these procedures might occur quite often, then it’s important to choose a blockchain that will handle this problem as fast and smoothly as possible.

In Hyperledger Fabric you can change the contract or even write a new one independently on a channel state. As Hyperledger Fabric splits into channels, the modifications you want to make will be applied only to the channel with an existing contract. Similar to this, you can upgrade a contract in your private installation of EOS.

Consensus

The term is self-defining: it’s a mutual agreement among the participants on the state of all data. The type of consensus depends on the way it is reached, for example by a voting requirement of a simple majority or by using some validators, etc. Although you might hear about many types of consensus, in reality, there’re just a few that make sense within a private blockchain. These are PoA, BFT, Raft.

Privacy

Not all blockchains have the same requirements for privacy. In some of them, the process of adding new participants may cause certain inconveniences. E.g. in Hyperledger Fabric, you will have to update a channel block and upgrade a chaincode. Also to make sure a participant has got encrypted data you have to wait for him to be online otherwise the information can be lost. The only chain that natively allows to revoke access to the previously granted data is Hyperledger Iroha. To make a private transaction in Quorum you have to wait for all the participants mentioned in the privateFor field. That’s not a good choice if you want to restrict access to some information within a chain, for example, to interact with a particular participant without other participants seeing it.

Asset

If exchanging the assets is your priority consider financial chains. They are fast and easy to understand. Some of them even support creating your own native assets, e.g. Stellar, Credits, BigchainDB, NEO, NEM, MultiChain, Hyperledger Iroha, Chain Core, Openchain.

Customization

There are three projects that allow you to create your own transaction handler and do absolutely what you want, e.g. work with the state on a low level or run any code, Substrate, Tendermint, Hyperledger Sawtooth. These frameworks allow you to focus on the business logic of your chain, not on consensus, validating blocks, and storing them. If you want to create your own consensus and exchange special signals, instead of blocks, consider libp2p.

Energy consumption

Here’s the inconvenient truth: the amount of energy consumed by blockchain technology is comparable to that of a small country. The good news is that it relates to chains based on Proof-of-Work consensus (e.g. Bitcoin). PoW is the first consensus algorithm for a blockchain to secure data, but, fortunately, not the only one. There are other options such as a Proof-of-Authority consensus which allows pre-selected nodes to run a chain, using about the equivalent energy of a light bulb. Hyperledger is a good example of a private blockchain that uses PoA.

The ability to directly interact with peers like in R3 Corda also significantly decreases energy consumption, because there is no necessity to broadcast and validate blocks.

Speed

Again, it depends on the types of consensus. The only slow one is PoW. The others are fast enough. Of course, it’s hard to compare the speed of a really decentralized system with a highly optimized and scaled database cluster, so keep in mind that interacting within a blockchain still can be a bottleneck in your architecture.

There’s no one-size-fits-all solution. But it doesn’t mean that you have to tolerate the features you don’t really need. Feel free to combine! By adding something you are interested in and cutting off anything useless, you will eventually come to what is best for your business!

The article is originally posted on instinctools.com

You don't need blockchain... until you do!

Instinctools — Tue, 15 Sep 2020 12:32:13 +0000

Being ready for blockchain means being ready for the future.

As this technology is still in its infancy, we twist, whirl and test it in all possible variations to see whether it actually works as billed. And you know what? It does. For many companies, who want to lift their business up to a whole new level of functionality, blockchain – or at least some aspect of it – is a total must.

Blockchain is not your cup of tea if you’re afraid of changes – even the positive ones. Or, if you don’t pursue ambitious goals. Maybe, you’re simply not ready for the great responsibility it entails.

Anyway, you can stop reading this article right now if you don’t want to find out the advantages of blockchain *instinctools clients already enjoy.

Transparency
The problem in many companies, especially those that produce something complicated is that they’re managing different vendors across a horizontal supply chain. All of these people that go into making a product don’t have the same database. They don’t use the same infrastructure, so it becomes really hard to see a product evolve over time. Using blockchain, we can create a shared reality across non-trusting entities. It means all of these nodes in the network have the ability to monitor and validate the chain for themselves.

Ability to create smart contracts
Smart contracts have a number of advantages over traditional ones. These are lower price, efficient implementation, absence of middlemen and automatic payment. All the actions are transparent, there’re no loopholes in contracts, which makes it impossible to interpret them to one’s benefit. A perfect solution indeed, for the financial market (banks, insurance), accounting and auditing, logistics, registration of property rights, etc.

Peer-to-peer connection
Using peer-to-peer networks you kill two birds with one stone. No, wait a sec – even three of the feathered creatures. Let’s see.

Getting rid of the vulnerability of the client-server network, where everything depends on a central agent. In this model when something goes wrong everyone suffers. If the server doesn’t work, no one can gain access to it. And, moreover, the server deals with a great volume of clients’ private information. That’s why companies that depend on this model have to spend A LOT of money to protect themselves from being hacked. But with a peer-to-peer connection that blockchain provides, there’s no need to rely on a central point of storage.
Efficiency. When it comes to the financial sector everyone wants a faster output. You can make a transaction in a few seconds, while traditionally it takes up to a few days.
Cost reduction. How can blockchain help to cut costs? Spoiler alert: by removing intermediaries.

Cryptography
Quite a bonus to everything mentioned above, isn’t it? But if all you need from blockchain is cryptography, why not start things off with the special libraries or hardware wallets that can sign, encrypt, decrypt and verify signatures? It’ll be enough for authentication/authorization users and hiding data.

Transaction register
Details of the transactions are recorded, verified and settled within seconds across all nodes, which, on top of that, have shared write access.

Opportunity to control personal data back to the owner
With blockchain, it has become possible to create “identity in a black box”, which only gives a piece of information that’s required to do something. And, what’s even more important, belongs to the immediate owner.

Simple and clear as it might seem, blockchain is definitely not something you need to dive into without giving it careful thought. There are plenty of specific issues – business and technological ones – you have to deal with beforehand.

“What type of blockchain/distributed ledger should I go for?”

“Is it necessary to write my own chain or had I better choose one out of the existing projects?”

“And if so, HOW can I do it?”…

To be continued...

The article is originally posted on instinctools.com

HAIL OR FAIL: 10 REASONS TO SAY GOODBYE TO LEGACY SOFTWARE

Instinctools — Thu, 20 Aug 2020 08:36:48 +0000

Isn’t this “it’s-not-about-the-age” philosophy just adorable? So affirmative, so promising and yet… so untrue? You know what? Age matters. At least when it comes to technologies.

Here are some points, which clearly show how using outdated software may, someday, lay waste to your projects in an epic display of mass destruction.

DIFFICULTIES WITH INTEGRATION AND COMPATIBILITY
Just take it: running your business efficiently is impossible without the ability to integrate your software with other tools or applications. If your business isn’t flexible enough in technological terms, your customers will probably find another provider, leaving you high and dry.
SLOW AND POOR PERFORMANCE
You see, spending oodles of time to perform a standard task is not okay for both your team and your business. However, it’s inevitable if your software is either outdated or of low-quality. Are you sure you have enough time to waste?
INSECURITY
Outdated systems are vulnerable to malware and breaches, and they’re less resistant to cyber-attacks. What was secure a decade ago, is unlikely to be all that reliable now.
ABSENCE OF VENDOR SUPPORT
Using third-party software implies dependence on the vendor for an update and maintenance.
You’re putting a lot at stake here, as anything can happen to the vendor (bankruptcy? acquisition by another company?..), that could affect your business in a negative way. If the vendor stops the support for the software you are using, you will be neither receiving updates nor getting the assistance fixing any issues. And that’s what will be a technological nail into your business’s coffin. Plus, if the system fails or data is lost, you will definitely want to be able to use your warranty. But that becomes absolutely impossible since your third-party provider no longer exists.
DEVICE-DEPENDENCY
goes together with the point
LOUSY MOBILE USABILITY
Succeeding in today’s market is tough. Constant development – that’s where breakthrough opportunities reside. But a breakthrough is impossible without using up-to-date technologies. Here’s the truth: if your software can only be accessed from office computers, you’re lagging far behind, as it deprives users of the flexibility they’ve got used to. For the past decade, the workplace has been narrowed – or expanded? – to the mobile device. For better or for worse, we take our offices with us while traveling, commuting, or parked at home on the couch.
USER-UNFRIENDLY TECHNOLOGIES
Clarity and simplicity are definitely not the words that can describe legacy software.
Today’s business systems are, on the contrary, intuitive and easy to interact with, which significantly decreases the time spent on training your employees to actually use it.
YOUR BUSINESS REQUIREMENTS ARE NOT BEING MET
Yeah, we get it! People tend to stick to old stuff out of habit. Nevertheless, if you want your company to evolve, you can’t adapt the business to the software. It should be the other way around.
Here’s the shortest checklist ever of the tasks your software must fulfill: 1) increase productivity; 2) have the possibility to expand; 3) be able to scale and change.
MAINTENANCE COSTS ARE HIGHER
Do you think that the modernization of your systems will cost you a fortune? Well, the cost of legacy system maintenance is incomparably higher. And the neglect of it is not an option as it results in outages and, eventually, costs you even more. A vicious cycle indeed.
ONCE AGAIN: IT IS EXPENSIVE. NO, REALLY! IT IS!
Inefficiency, staff training, outages… We’ve just mentioned plenty of obstacles, which get in the way of your business’s success. Yet, there are some hidden costs you might not be aware of. We’d better put them in separate sentences so that you’ll understand all the significance. Employee satisfaction. Customer loyalty. Your brand image, which, by the way, is not “nothing” as Sprite’s slogan claims, but “everything.” Go ask Starbucks.

There are domains (e.g. construction, warehouse control, oil and gas production, logistics, etc.) that can hardly exist without a proper revamp. Even if you think your company is not ready to dive deep into the modernization process, a good idea is to consider a step-by-step approach. Sometimes an all-or-nothing attitude can be really dangerous, as the odds of doing nothing are too high.

Everything changes. Whether we like it or not. Whether we accept it or not.

Transformation of legacy systems is significantly less expensive than losing customers and precious time while dealing with prehistoric software solutions. So wouldn’t it be better to embrace new things instead of resisting them? Just in case: this is a rhetorical question;)