Software development and delivery
Modern software is built and shipped through a set of practices and tools that have their own dense shorthand. The terms below cover how code moves from a developer's laptop into a running production system, and the interfaces that let separate pieces of software talk to each other.
Understanding this vocabulary matters beyond engineering teams: procurement staff, auditors and managers increasingly need to read a technical proposal or vendor contract that assumes fluency with these terms.
- API (Application Programming Interface) — A defined set of rules that lets one piece of software request services or data from another without knowing how it works internally.
- CI/CD (Continuous Integration / Continuous Delivery) — Automatically testing and merging code changes frequently, then automatically preparing or releasing that code to production, reducing manual error and delay.
- IaC (Infrastructure as Code) — Managing servers, networks and cloud resources by writing and version-controlling text files that describe the desired setup, rather than configuring systems by hand.
- Microservices — An architecture where an application is built as a set of small, independently deployable services rather than one large program, so teams can update parts separately.
- Version control — A system, most commonly Git, for tracking every change made to source code over time so teams can collaborate and revert mistakes.
- Technical debt — The implied future cost of choosing a quick solution now instead of a better, more time-consuming one, typically repaid later through rework.
Cloud and infrastructure
Cloud computing changed how organizations buy and run computing power, moving from owned hardware to rented capacity from providers such as Amazon Web Services, Microsoft Azure and Google Cloud. These terms describe how that capacity is packaged and managed.
- Cloud computing — Renting computing power, storage and software over the internet instead of owning and running physical servers.
- Container — A lightweight, self-contained package of an application and everything it needs to run, so it behaves the same on any machine; Docker is the most common tool for creating them.
- Kubernetes — An open-source system for automatically deploying, scaling and managing large numbers of containers across many machines.
- Serverless computing — A cloud model where the provider manages the underlying servers and the customer is charged only for the computing time their code actually uses.
- Edge computing — Processing data close to where it is generated, such as on a factory sensor or local device, instead of sending everything to a distant data center, to reduce delay.
- Observability — The ability to understand what is happening inside a running system from the data it emits, typically logs, metrics and traces.
- Latency — The delay between a request being made and a response arriving, usually measured in milliseconds; lower latency means a faster-feeling system.
Data and artificial intelligence
Artificial intelligence has moved from a specialist research topic to a mainstream business concern, bringing a wave of new terminology into everyday management conversation. The definitions below focus on what these systems actually do, not marketing claims about them.
Several of these terms, such as large language models and retrieval-augmented generation, describe techniques released publicly only in the past few years and are still settling into standard usage, so definitions vary slightly between vendors.
- Machine learning — Building software that improves its performance on a task by learning patterns from data, rather than following rules written explicitly by a programmer.
- LLM (Large Language Model) — A model trained on very large amounts of text that can generate, summarize or answer questions about language.
- RAG (Retrieval-Augmented Generation) — A technique where a model looks up relevant documents from a trusted source before generating an answer, reducing the risk of invented information.
- Hallucination — When an AI model produces an answer that sounds plausible and confident but is factually wrong or fabricated.
- Data pipeline — An automated sequence of steps that moves data from its source, cleans and transforms it, and delivers it where it is needed.
- Data lake — A large repository that holds raw data in its original format until it is needed, as opposed to a database that requires data to be structured first.
- Bias (in AI) — A systematic and unfair skew in a model's outputs, usually caused by unrepresentative or historically skewed training data.
Cybersecurity
Security terminology has expanded rapidly as threats have grown more sophisticated and as regulation has increased the expectation that organizations can explain their defenses in plain terms to boards, auditors and the public.
- Zero trust — A security approach that assumes no user or device should be automatically trusted, even inside a company network, and verifies every access request individually.
- Phishing — A fraudulent message, usually email, designed to trick the recipient into revealing credentials or installing malware.
- Ransomware — Malicious software that encrypts an organization's files and demands payment for the key to unlock them.
- Multi-factor authentication (MFA) — A login process requiring two or more separate proofs of identity, such as a password plus a code from a phone.
- Encryption — Converting data into a coded form readable only by someone with the correct key, protecting it if intercepted or stolen.
- Vulnerability — A weakness in software or a system that could be exploited to cause unintended behavior or gain unauthorized access.
- Penetration testing — A controlled, authorized simulated attack carried out to find security weaknesses before real attackers do.
Emerging and specialized computing
A handful of terms describe technologies still maturing outside mainstream commercial deployment but increasingly discussed in strategy and investment conversations. Knowing what these actually mean helps professionals separate realistic near-term applications from hype.
- Quantum computing — A computing approach using quantum physics to process certain problems, such as factoring large numbers or simulating molecules, far faster than classical computers for those specific problems.
- FPGA (Field-Programmable Gate Array) — A chip that can be reprogrammed after manufacture to perform a specific task efficiently, sitting between general-purpose processors and custom-built silicon.
- Digital twin — A virtual model of a physical object, process or system, kept updated with real data, used to simulate and predict real-world behavior.
- IoT (Internet of Things) — The network of physical devices, from thermostats to industrial sensors, that connect to the internet to send and receive data.
- Blockchain — A shared digital record of transactions maintained across many computers at once, designed so past entries cannot be altered without network agreement.
- API economy — The business trend of companies exposing parts of their software as APIs so others can build on top of them, creating new products and revenue streams.
Governance, compliance and ways of working
Not all jargon is purely technical. Some of the most consequential terms describe how technology teams organize their work and how organizations govern technology risk, both of which are increasingly a matter of regulatory and board-level interest.
- Agile — Delivering software in short, iterative cycles with frequent feedback, rather than planning an entire project upfront and delivering it all at once.
- DevOps — Practices that bring software development and IT operations together, using automation to release software faster and more reliably.
- SLA (Service Level Agreement) — A formal commitment, usually contractual, defining the level of service guaranteed, such as uptime or response time.
- Data governance — The policies, roles and processes an organization uses to ensure its data is accurate, secure and used appropriately.
- Legacy system — Older software or hardware still in active use, often because replacing it is costly or risky, even though it may no longer be well supported.
- Digital transformation — Redesigning an organization's processes, culture and customer experience around digital technology rather than simply adding technology to existing ways of working.