🤖〽️Prompt Engineering: Copilot Unabridged (Part 183: The Impact of AI Invisible Prompt Injection)

Prompt Engineering Serie

Prompt: "write a post of 600 words on the impact of AI invisible prompt injection"

Introduction

Invisible prompt injection is emerging as one of the most consequential vulnerabilities in modern AI systems. Unlike traditional cybersecurity threats, which target code, networks, or infrastructure, invisible prompt injection targets the interpretive layer of large language models - the part that decides what the system should pay attention to and how it should respond. Because these attacks operate beneath the surface of normal interaction, their impact is both subtle and far‑reaching. Understanding this impact is essential for anyone building, deploying, or relying on AI systems.

The first major impact is the erosion of user control. When hidden instructions are embedded in text, images, or metadata, the AI may follow those instructions instead of the user’s explicit request. This creates a dangerous inversion of agency. The user believes they are in control, but the model is being quietly steered by an unseen actor. In practical terms, this means an AI assistant could ignore a user’s question, alter its tone, or provide misleading information - all without the user realizing why. The loss of control is not just technical; it undermines trust in the entire interaction.

A second impact is the corruption of outputs, which can occur without any visible sign of manipulation. Invisible prompt injection can cause an AI system to hallucinate, fabricate citations, or generate biased or harmful content. Because the injected instructions are hidden, the resulting output appears to be the model’s natural response. This makes the attack difficult to detect and even harder to attribute. In environments where accuracy matters - healthcare, legal analysis, scientific research - the consequences can be severe. A single hidden instruction can distort an entire chain of reasoning.

Another significant impact is the exploitation of contextual blind spots. AI systems treat all input as potentially meaningful context. They do not inherently distinguish between user intent and hidden instructions. Attackers can exploit this by embedding malicious prompts in places users rarely inspect: alt‑text, HTML comments, zero‑width characters, or even the metadata of uploaded files. Because the AI reads these hidden elements but the user does not, the attacker gains asymmetric influence. This asymmetry is what makes invisible prompt injection so powerful: the attacker sees the whole picture, while the user sees only the surface.

Invisible prompt injection also has a profound impact on the reliability of AI‑mediated workflows. As AI becomes integrated into business processes - summarizing documents, drafting emails, generating reports - hidden instructions can quietly alter outcomes. A malicious prompt embedded in a shared document could cause an AI system to misclassify data, rewrite content, or leak sensitive information. These failures are not obvious bugs; they are subtle distortions that propagate through automated pipelines. The more organizations rely on AI for routine tasks, the more vulnerable they become to these invisible manipulations.

A further impact is the amplification of social engineering risks. Traditional phishing relies on deceiving humans. Invisible prompt injection extends this deception to machines. An attacker can craft content that appears harmless to a human reader but contains hidden instructions that cause the AI to behave in ways that benefit the attacker. This creates a new hybrid threat: social engineering that targets both the human and the AI simultaneously. As AI systems increasingly mediate communication, this dual‑layer manipulation becomes a powerful tool for misinformation, fraud, and influence operations.

Finally, invisible prompt injection impacts the broader trust ecosystem surrounding AI. Trust in AI depends on predictability, transparency, and alignment with user intent. Invisible prompt injection undermines all three. It exposes the fragility of systems that rely on natural language as both input and instruction. It reveals how easily AI can be manipulated without detection. And it highlights the need for new forms of input sanitization, context isolation, and architectural safeguards.

Invisible prompt injection is not just a technical curiosity. It is a structural vulnerability that reshapes how we think about AI safety, reliability, and trust. Recognizing its impact is the first step toward building systems that are resilient, transparent, and aligned with the people who rely on them.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 157: The Transformative Power of High‑Quality Training Data in AI)

Prompt Engineering Series

Prompt: "write a post of 600 words on the impact of consistent and high‑quality training data on AI"

Introduction

Artificial Intelligence (AI) has advanced at a breathtaking pace, but beneath every impressive model - whether it’s diagnosing diseases, generating natural language, or predicting customer behavior - lies a simple truth: AI is only as good as the data it learns from. While algorithms often get the spotlight, the real engine of progress is the quality and consistency of the training data that shapes them. When data is clean, representative, and reliable, AI systems flourish. When it’s inconsistent or flawed, even the most sophisticated models struggle.

Why Data Quality Matters More Than Model Complexity

At its core, machine learning is pattern recognition. Models learn by identifying relationships in the data they’re fed. If that data is noisy, biased, or incomplete, the patterns the model learns will be distorted. This leads to:

• Lower accuracy
• Unpredictable behavior
• Poor generalization to real‑world scenarios

High‑quality data, on the other hand, gives models a clear, stable foundation. It reduces ambiguity, sharpens decision boundaries, and allows the model to focus on meaningful signals rather than statistical 'static'. In many cases, improving data quality yields bigger performance gains than tweaking model architecture.

Consistency: The Unsung Hero of Reliable AI

Consistency in training data is just as important as quality. When data is collected or labeled using different standards, the model receives mixed messages. Imagine teaching a child math using three different definitions of multiplication - they’d be confused, and so is your model.

• Consistent data ensures:
• Uniform labeling practices
• Aligned definitions and categories
• Stable distributions across time

This is especially crucial in domains like healthcare, finance, and autonomous systems, where inconsistent data can lead to dangerous or costly errors.

Better Data = Better Learning

When training data is both high‑quality and consistent, AI models learn faster and more effectively. They require fewer training cycles, less computational power, and less manual intervention. The model’s internal representations become more coherent, which improves:

• Accuracy
• Robustness
• Explainability

This is why organizations that invest in data governance, annotation standards, and quality control often outperform those that focus solely on model development.

Reducing Bias and Increasing Fairness

Bias in AI is almost always a data problem. If certain groups or scenarios are underrepresented - or represented inaccurately - the model will inherit those imbalances. High‑quality data practices help mitigate this by ensuring:

• Diverse and representative samples
• Balanced class distributions
• Transparent labeling criteria

Fairness isn’t just a moral imperative; it’s a performance issue. Models trained on biased data are less reliable and more prone to failure when deployed in diverse environments.

The Competitive Advantage of Data Excellence

In today’s AI‑driven landscape, companies that treat data as a strategic asset gain a significant edge. High‑quality training data leads to:

• Faster deployment cycles
• Lower maintenance costs
• More trustworthy AI systems
• Better user experiences

It also enables continuous improvement. When new data is collected with the same standards as the old, models can be retrained seamlessly, keeping them aligned with evolving real‑world conditions.

The Bottom Line

Consistent, high‑quality training data isn’t just a technical requirement - it’s the foundation of responsible, effective, and scalable AI. As models grow more powerful, the importance of data quality grows with them. Organizations that invest in rigorous data practices today are building the AI systems that will lead tomorrow.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 127: Understanding Facts in Modern AI)

Prompt Engineering Series

Prompt: "write a post of 600 words on what is meant by facts in nowadays AI and include an introduction, respectively a closing statement"

Introduction

As Artificial Intelligence (AI) becomes more deeply integrated into communication, research, and decision‑making, the question of what AI considers a fact has become increasingly important. People often assume that if an AI states something confidently, it must be true. But AI systems do not possess a stable internal database of verified truths. Instead, they generate responses by predicting what information is most likely to follow from patterns in the data they were trained on. Understanding how facts function in modern AI helps clarify why these systems can be powerful tools - and why they sometimes produce errors or fabrications.

What a 'Fact' Means for Humans

For humans, a fact is a statement that can be verified through observation, evidence, or reliable sources. Facts are:

• Stable: they do not change depending on context.
• Grounded: they refer to real‑world states or events.
• Verifiable: they can be checked against evidence.
• Independent: they exist whether or not someone remembers them.

Human understanding of facts is tied to reasoning, experience, and shared standards of truth.

How AI Models Handle Facts

AI systems do not have beliefs, memories, or understanding. They work by identifying statistical patterns in massive datasets. This leads to a different relationship with facts:

• Facts are patterns: not stored entries but tendencies in the data.
• Facts are probabilistic: the model generates what seems likely, not what is verified.
• Facts are context‑sensitive: the same question phrased differently may yield different answers.
• Facts are not inherently distinguished from non‑facts: the model does not “know” what is true; it only predicts what fits the pattern.

This is why AI can produce accurate information in one moment and incorrect information in another.

The Fragility of AI Facts

Because AI relies on statistical inference, several factors can distort factual accuracy:

• Training data limitations: if the data is outdated, incomplete, or biased, the model’s 'facts' reflect those flaws.
• Ambiguous prompts: unclear questions can lead to confident but incorrect answers.
• Lack of real‑time grounding: unless connected to external sources, AI cannot update facts after training.
• Hallucinations: the model may generate plausible‑sounding but false statements when patterns are weak or conflicting.

These issues highlight that AI does not know facts; it reconstructs them.

Why AI Can Still Be Factually Useful

Despite these limitations, AI can be highly effective at working with factual information when used appropriately. Its strengths include:

• Synthesizing large volumes of data: AI can integrate information from many sources at once.
• Recognizing factual patterns: it can identify common knowledge across diverse texts.
• Retrieving structured information: when connected to verified databases or tools, it can provide up‑to‑date facts. 
• Supporting human fact‑checking: AI can surface relevant details quickly, which humans can then verify.

In this sense, AI acts as a fact assistant, not a fact authority.

The Human Role in Defining Facts for AI

Because AI cannot distinguish truth from falsehood on its own, humans play a crucial role in shaping factual accuracy:

• Curating training data: selecting high‑quality, diverse, and reliable sources.
• Building guardrails : designing systems that avoid unsupported claims.
• Providing feedback: correcting errors to improve future performance.
• Maintaining oversight: verifying outputs before relying on them for decisions.

AI becomes more reliable when humans treat it as a collaborator rather than an oracle.

Closing Statement

Facts in modern AI are not fixed truths stored inside a machine but statistical echoes of the data used to train it. Understanding this distinction helps set realistic expectations: AI can be a powerful tool for accessing and organizing information, but it cannot replace human judgment, verification, or critical thinking. As AI continues to evolve, the challenge is to build systems that handle facts responsibly - and to ensure that humans remain the final arbiters of truth.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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📦Data Migrations (DM): Quality Assurance (Part IV: Quality Acceptance Criteria IV)

Data Migrations Series

Reliability

Reliability is the degree to which a solution performs its intended functions under stated conditions without failure. In other words, a DM is reliable if it performs what was intended by design. The data should be migrated only when migration’s reliability was confirmed by the users as part of the sign-off process. The dry-runs as well the final iteration for the UAT have the objective of confirming solution’s reliability.

Reversibility

Reversibility is the degree to which a solution can return to a previous state without starting the process from the beginning. For example, it should be possible to reverse the changes made to a table by returning to the previous state. This can involve having a copy of the data stored respectively deleting and reloading the data when necessary. 

Considering that the sequence in which the various activities is fix, in theory it’s possible to address reversibility by design, e.g. by allowing to repeat individual steps or by creating rollback points. Rollback points are especially important when loading the data into the target system. 

Robustness

Robustness is the degree to which the solution can accommodate invalid input or environmental conditions that might affect data’s processing or other requirements (e,g. performance). If the logic can be stabilized over the various iterations, the variance in data quality can have an important impact on a solutions robustness. One can accommodate erroneous input by relaxing schema’s rules and adding further quality checks.

Security 

Security is the degree to which the DM solution protects the data so that only authorized people have access to the respective data to the defined level of authorization as data are moved through the solution. The security provided by a solution needs to be considered against the standards and further requirements defined within the organization. In case no such standards are available, one can in theory consider the industry best practices.

Scalability

Scalability is the degree to which the solution is able to respond to an increased workload.  Given that the number of data considered during the various iterations vary in volume, a solution’s scalability needs to be considered in respect to the volume of data to be migrated.  

Standardization

Standardization is the degree to which technical standards were implemented for a solution to guarantee certain level of performance or other aspects considered as import. There can be standards for data storage, processing, access, transportation, or other aspects associated with the migration processes. Moreover, especially when multiple DMs are in scope, organizations can define a set of standards and guidelines that should be further considered.  

Testability

Testability is the degree to which a solution can be tested in the respect to the set of functional and data-related requirements. Even if for the success of a migration are important the data in their final form, to achieve that is needed to validate the logic and test thoroughly the transformations performed on the data. As the data go trough the data pipelines, they need to be tested in the critical points – points where the data suffer important transformations. Moreover, one can consider record counters for the records processed in each such critical point, to assure that no record was lost in the process.  

Traceability

Traceability is the degree to which the changes performed on the data can be traced from the target to the source systems as record, respectively at entity level. In theory, it’s enough to document the changes at attribute level, though upon case it might needed to document also the changes performed on individual values. 

Mappings at attribute level allow tracing the data flow, while mappings at value level allow tracing the changes occurrent within values. 

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🧮ERP: Implementations (Part IV: It’s all about Scope II - Nonfunctional Requirements & MVP)

ERP Implementations Series

Nonfunctional Requirements

In contrast to functional requirements (FRs), nonfunctional requirements (NFRs) have no direct impact on system’s behavior, affecting end-users’ experience with the system, resuming thus to topics like performance, usability, reliability, compatibility, security, monitoring, maintainability, testability, respectively other constraints and quality attributes. Even if these requirements are in general addressed by design, the changes made to the system have the potential of impacting users’ experience negatively.  

Moreover, the NFRs are usually difficult to quantify, and probably that’s why they are seldom made explicit in a formal document or are considered eventually only at high level. However, one can still find a basis for comparison against compliance requirements, general guidelines, standards, best practices or the legacy system(s) (e.g. the performance should not be worse than in the legacy system, the volume of effort for carrying the various activities should not increase). Even if they can’t be adequately described, it’s recommended to list the NFRs in general terms in a formal document (e.g. implementation contract). Failing to do so can open or widen the risk exposure one has, especially when the system lacks important support in the respective areas. In addition, these requirements need to be considered during testing and sign-off as well. 

Minimum Viable Product (MVP)

Besides gaps’ consideration in respect to FRs, it’s important to consider sometimes on whether the whole functionality is mandatory, especially when considering the various activities that need to be carried out (parametrization, Data Migration).

For example, one can target to implement a minimum viable product (MVP) - a version of the product which has just enough features to cover the mandatory or the most important FRs. The MVP is based on the idea that implementing about 80% of the needed functionality has in theory the potential of providing earlier a usable product with a minimum of effort (quick wins), assure that project’s goals and objectives were met, respectively assure a basis for further development. In case of cost overruns, the MVP assures that the business has a workable product and has the opportunity of deciding whether it’s worth of investing more into the project now or later. 

The MVP allows also to get early users’ feedback and integrate it into further enhancements and developments. Often the users understand the capabilities of a system, respectively implementation, only when they are able using the system. As this is a learning process, the learning period can take up to a few months until adequate feedback is available. Therefore, postponing implementation’s continuation with a few months can have in theory a positive impact, however it can come also with drawbacks (e.g. the resources are not available anymore). 

A sketch of the MVP usually results from requirements’ prioritization, however then requirements need to be regarded holistically, as there can be different levels of dependencies existing between them. In addition, different costs can incur if the requirements will be handled later, and other constrains may apply as well. Considering an MVP approach can be a sword with two edges. In the worst-case scenario, the business will get only the MVP, with its good and bad characteristics. The business will be forced then to fill the gaps by working outside the system, which can lead to further effort and, in extremis, with poor acceptance of the system. In general, users expect having their processes fully implemented in the system, expectation which is not always economically grounded.

After establishing an MVP one can consider the further requirements (including improvement suggestions) based on a cost-benefit basis and implement them accordingly as part of a continuous improvement initiative, even if more time will be maybe required for implementing the same.

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🛡️Information Security: Denial of Service [DoS] (Definitions)

"A type of attack on a computer system that ties up critical system resources, making the system temporarily unusable." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"Any attack that affects the availability of a service. Reliability bugs that cause a service to crash or hang are usually potential denial-of-service problems." (Mark S Merkow & Lakshmikanth Raghavan, "Secure and Resilient Software Development", 2010)

"This is a technique for overloading an IT system with a malicious workload, effectively preventing its regular service use." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"Occurs when a server or Web site receives a flood of traffic - much more traffic or requests for service than it can handle, causing it to crash." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"Causing an information resource to be partially or completely unable to process requests. This is usually accomplished by flooding the resource with more requests than it can handle, thereby rendering it incapable of providing normal levels of service." (Mark Rhodes-Ousley, "Information Security: The Complete Reference, Second Edition" 2nd Ed., 2013)

"Attacks designed to disable a resource such as a server, network, or any other service provided by the company. If the attack is successful, the resource is no longer available to legitimate users." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"An attack from a single attacker designed to disrupt or disable the services provided by an IT system. Compare to distributed denial of service (DDoS)." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

"A coordinated attack in which the target website or service is flooded with requests for access, to the point that it is completely overwhelmed." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"An attack that can result in decreased availability of the targeted system." (Mike Harwood, "Internet Security: How to Defend Against Attackers on the Web" 2nd Ed., 2015)

"An attack that generally floods a network with traffic. A successful DoS attack renders the network unusable and effectively stops the victim organization’s ability to conduct business." (Weiss, "Auditing IT Infrastructures for Compliance" 2nd Ed., 2015)

"A type of cyberattack to degrade the availability of a target system." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"Any action, or series of actions, that prevents a system, or its resources, from functioning in accordance with its intended purpose." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"The prevention of authorized access to resources or the delaying of time-critical operations." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"An attack shutting down running of a service or network in order to render it inaccessible to its users (whether human person or a processing device)." (Wissam Abbass et al, "Internet of Things Application for Intelligent Cities: Security Risk Assessment Challenges", 2021)

"Actions that prevent the NE from functioning in accordance with its intended purpose. A piece of equipment or entity may be rendered inoperable or forced to operate in a degraded state; operations that depend on timeliness may be delayed." (NIST SP 800-13)

"The prevention of authorized access to resources or the delaying of time-critical operations. (Time-critical may be milliseconds or it may be hours, depending upon the service provided)." (NIST SP 800-12 Rev. 1)

"The prevention of authorized access to a system resource or the delaying of system operations and functions." (NIST SP 800-82 Rev. 2)

📉Graphical Representation: Reliability (Just the Quotes)

"Most authors would greatly resent it if they were told that their writings contained great exaggerations, yet many of these same authors permit their work to be illustrated with charts which are so arranged as to cause an erroneous interpretation. If authors and editors will inspect their charts as carefully as they revise their written matter, we shall have, in a very short time, a standard of reliability in charts and illustrations just as high as now found in the average printed page." (Willard C Brinton, "Graphic Methods for Presenting Facts", 1919) 

"Reliability is highly valued by accountants and has been defined as 'the faithfulness with which it (information) represents what it purports to represent'. The reason reliability is so important is that an essential characteristic of an accounting report is its acceptance, and if a report is considered to be misleading or superfluous, it and future reports will be disregarded." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"The scales used are important; contracting or expanding the vertical or horizontal scales will change the visual picture. The trend lines need enough grid lines to obviate difficulty in reading the results properly. One must be careful in the use of cross-hatching and shading, both of which can create illusions. Horizontal rulings tend to reduce the appearance. while vertical lines enlarge it. In summary, graphs must be reliable, and reliability depends not only on what is presented but also on how it is presented." (Anker V Andersen, "Graphing Financial Information: How accountants can use graphs to communicate", 1983)

"In everyday life, 'estimation' means a rough and imprecise procedure leading to a rough and imprecise result. You 'estimate' when you cannot measure exactly. In statistics, on the other hand, 'estimation' is a technical term. It means a precise and accurate procedure, leading to a result which may be imprecise, but where at least the extent of the imprecision is known. It has nothing to do with approximation. You have some data, from which you want to draw conclusions and produce a 'best' value for some particular numerical quantity (or perhaps for several quantities), and you probably also want to know how reliable this value is, i.e. what the error is on your estimate." (Roger J Barlow, "Statistics: A guide to the use of statistical methods in the physical sciences", 1989)

"Pie charts have severe perceptual problems. Experiments in graphical perception have shown that compared with dot charts, they convey information far less reliably. But if you want to display some data, and perceiving the information is not so important, then a pie chart is fine." (Richard Becker & William S Cleveland," S-Plus Trellis Graphics User's Manual", 1996)

"Data visualization is a means to an end, not an end in itself. It's merely a bridge connecting the messenger to the receiver and its limitations are framed by our own inherent irrationalities, prejudices, assumptions, and irrational tastes. All these factors can undermine the consistency and reliability of any predicted reaction to a given visualization, but that is something we can't realistically influence." (Andy Kirk, "Data Visualization: A successful design process", 2012)

"Are your insights based on data that is accurate and reliable? Trustworthy data is correct or valid, free from significant defects and gaps. The trustworthiness of your data begins with the proper collection, processing, and maintenance of the data at its source. However, the reliability of your numbers can also be influenced by how they are handled during the analysis process. Clean data can inadvertently lose its integrity and true meaning depending on how it is analyzed and interpreted." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"Without knowing the source and context, a particular statistic is worth little. Yet numbers and statistics appear rigorous and reliable simply by virtue of being quantitative, and have a tendency to spread." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

🌁Software Engineering: Failure Modes & Effects Analysis [FMEA] (Definitions)

"A risk-analysis technique that identifies and ranks the potential failure modes of a design or process and then prioritizes improvement actions." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"An approach to analyzing the effect of faults on system reliability." (Bruce P Douglass, "Real-Time Agility: The Harmony/ESW Method for Real-Time and Embedded Systems Development", 2009)

"An analytical procedure in which each potential failure mode in every component of a product is analyzed to determine its effect on the reliability of that component and, by itself or in combination with other possible failure modes, on the reliability of the product or system and on the required function of the component; or the examination of a product (at the system and/or lower levels) for all ways that a failure may occur. For each potential failure, an estimate is made of its effect on the total system and of its impact. In addition, a review is undertaken of the action planned to minimize the probability of failure and to minimize its effects." (For Dummies, "PMP Certification All-in-One For Dummies, 2nd Ed.", 2013)

"Approach that dissects a component into its basic functions to identify flaws and those flaw’s effects." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"(1) A process for analysis of potential failure modes within a system for classification by severity or determination of the effect of failures on the system. (2) A structured method for analyzing risk by ranking and documenting potential failure mode in a system, design or process. The analysis includes: identification of potential failures and their effects; ranking of factors (e.g., severity, frequency of occurrence, detectability of the potential failures); and identification and results of actions taken to reduce or eliminate risk." (International Aerospace Quality Group)

"A systematic approach to risk identification and analysis of identifying possible modes of failure and attempting to prevent their occurrence." (SQA)

"FMEA (failure modes effects analysis) is a technique used in product life cycle management activities to predict how a product or process might fail and what the effects of that failure might be." (Gartner)

🌁Software Engineering: Reliability (Definitions)

"[...] the characteristic of an information infrastructure to store and retrieve information in an accessible, secure, maintainable, and fast manner." (Martin J Eppler, "Managing Information Quality 2nd Ed.", 2006)

"The measure of robustness over time. The length of time a product or process performs as intended." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"Reliability describes a product’s ability to maintain its defined functions under defined conditions for a specified period of time." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

"A stochastic measure of the likelihood that a system will be able to deliver a service." (Bruce P Douglass, "Real-Time Agility: The Harmony/ESW Method for Real-Time and Embedded Systems Development", 2009)

"The degree to which the new system is perceived as being better than the system it replaces, often expressed in the economic or social status terms that will result from its adoption." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

"The ability for a component (server, application, database, etc.) or group of components to consistently perform its functions." (Craig S Mullins, "Database Administration", 2012)

"A set of characteristics relating to the ability of the software product to perform its required functions under stated conditions for a specified period of time or for a specified number of operations." (Tilo Linz et al, "Software Testing Foundations" 4th Ed, 2014)

 "It is a characteristic of an item (component or system), expressed by the probability that the item (component/system) will perform its required function under given conditions for a stated time interval." (Harish Garg,  "Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology", 2014)

"A characteristic of an item (component or system), expressed by the probability that the item (component/system) will perform its required function under given conditions for a stated time interval." (Harish Garg, "A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data", 2015)

"A sub-set of statistical engineering methodology that predicts performance of a product over its intended life cycle and understanding of the effects of various failure modes on system performance." (Atila Ertas, "Transdisciplinary Engineering Design Process", 2018)

"The ability of the software product to perform its required functions under stated conditions for a specified period of time, or for a specified number of operations" (ISO 9126)

"The ability of the software product to perform its required functions under stated conditions for a specified period of time, or for a specified number of operations." (ISO/IEC 25000)

"The capability of a system or component to perform its required functions under stated conditions for a specified period of time." (IEEE Std 610.12-1990)