Computer Information Systems, Automation, and Security Review

Comprehensive vocabulary flashcards covering Computer Information Systems, decision support hierarchies, business intelligence, prompt engineering, systems thinking, software development lifecycles, and cybersecurity principles.

Seamless Integration

The process of connecting disparate software applications, systems, and data sources so they can communicate across business processes.

Enterprise Systems

Unified platforms—such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Supply Chain Management (SCM)—that allow departments to align goals, share documents, and monitor progress in real time.

Kaizen

A continuous improvement mindset rooted in Japanese philosophy that emphasizes systematic, incremental enhancements to processes and products rather than waiting for failure.

Basic/Task Automation

The automation of simple, routine, and often repetitive tasks to reduce manual errors, accelerate transactional work, and free employees for higher-value activities.

Robotic Process Automation (RPA)

A form of automation that uses software robots to mimic human interactions with software to automate repetitive, rule-based tasks such as data entry and invoice processing.

Business Process Automation (BPA)

A form of automation that orchestrates more complex and repeatable multi-step processes, often involving multiple integrated systems.

Intelligent Automation

An advanced automation approach combining Artificial Intelligence, business process automation, and RPA to automate and scale decision-making across organizations.

Descriptive Analytics

An analytics discipline that examines historical data through visualizations, reports, and dashboards to understand past performance ('what happened'), providing hindsight.

Diagnostic Analytics

An analytics approach that drills down into descriptive data using discovery and data mining to identify the root causes of outcomes ('why it happened'), delivering insight.

Predictive Analytics

An analytics discipline that applies statistical algorithms and machine learning to historical data to forecast future outcomes ('what could happen'), providing foresight.

Prescriptive Analytics

An analytics discipline that utilizes optimization, simulation, and decision modeling to recommend specific actions and analyze the implications of each option ('what should be done').

Prompt Engineering

The practice of crafting LLM inputs to obtain reliable, relevant, and appropriately formatted outputs.

RICCE Framework

A structured prompt engineering framework comprising Role, Instructions, Context, Constraints, and Expectations/Examples designed to improve the quality and utility of generative AI outputs.

Role (RICCE)

The component of the RICCE framework that defines the persona, tone, perspective, or expertise level the AI model should adopt.

Instructions (RICCE)

The component of the RICCE framework that states precisely the specific task, command, or action the model must perform.

Context (RICCE)

The background information, dataset, prior conversation, or scenario details supplied to guide the model's response beyond its training data alone.

Constraints (RICCE)

The boundaries, limitations, or 'rules of the road' specified in a prompt that dictate what the model should not do (e.g., word count limits or restrictions on speculation).

Expectations/Examples (RICCE)

The component specifying the desired format, length, and style of the output, often including sample input-output pairs to improve consistency.

Business Workflows

The operational implementation of a process or subprocess, specifically describing the sequence, tasks, decisions, and handoffs that convert raw inputs into outputs.

Structured Decisions

Decisions with immediate operational impact that involve well-defined parameters and follow a set methodology to produce fast, predictable outcomes.

Semi-Structured Decisions

Decisions with near-term or mid-term impact that combine known data and procedures with human insight, intuition, and experience.

Unstructured Decisions

Open-ended decisions with long-term, multi-year impact that lack clear guidelines or predefined procedures, characterized by ambiguity, uncertainty, and reliance on tacit knowledge.

Operational Support Systems (OSS)

Information systems that primarily support day-to-day business execution for operational-level employees and first-line supervisors, focusing on transactions and process controls.

Transaction Processing Systems (TPS)

Operational systems that record and process routine daily transactions (such as retail POS systems, sales, payments, and inventory adjustments) and form the basis of modern accounting.

Process Control Systems (PCS)

Systems that perform supervisory control and data acquisition to monitor and adjust physical manufacturing conditions, such as temperature and pressure.

Management Information Systems (MIS)

Information systems used by mid-level management that aggregate and analyze operational TPS data to produce routine structured reports (sales analysis, project management, CRM) to monitor operations.

Decision Support Systems (DSS)

Analytical systems used by mid-to-senior level managers that feature modeling, simulation tools, and 'what-if' analysis to support semi-structured and unstructured decision-making.

Executive Information Systems (EIS)

High-level systems that provide senior executives with graphical summaries of organizational performance, key performance indicators (KPIs), and competitive intelligence for strategic long-term planning.

Business Intelligence (BI) Dashboard

A visual interface that consolidates and displays key metrics, trends, and data insights to help users make informed decisions quickly.

Consolidation (Dashboard Feature)

The aggregation of data from various sources (such as databases, APIs, and spreadsheets) into a unified visual display for cross-functional analysis.

Pivot (Dashboard Feature)

The dynamic restructuring of data by rotating or swapping rows and columns to view metrics from multiple perspectives without modifying the underlying dataset.

Drill-Down (Dashboard Feature)

Hierarchical navigation that enables users to move from high-level summary metrics down to granular details within the exact same dataset.

Drill-Through (Dashboard Feature)

A navigation feature that jumps from a data point in one report to a completely separate, related report or dashboard for broader context.

Multimodal AI

Artificial intelligence models engineered to process, interpret, and generate multiple types of data modalities (such as text, images, audio, and video) simultaneously within a unified architecture.

Crossmodal AI

The specific capability of an AI model to map, translate, or retrieve information from one data modality using another through a shared understanding space.

Systems Thinking

A holistic problem-solving framework that analyzes issues by examining the interconnectedness, feedback loops, and dynamic relationships among components within a broader system.

Emergence (Systems Thinking)

The principle that the interactions and connections among system components produce novel properties, behaviors, or outcomes that cannot be observed from examining individual parts in isolation.

Leverage Points

Specific places within an interconnected system where small, targeted modifications can generate disproportionately large shifts in overall behavior.

Computational Thinking

A structured problem-solving approach that formulates problems and solutions into step-by-step algorithms that can be effectively executed by an information-processing agent.

Decomposition (Computational Thinking)

The method of breaking down a large, complex problem into smaller, more manageable sub-problems.

Pattern Recognition (Computational Thinking)

The identification of commonalities, trends, and recurring structures within and across different problems.

Abstraction (Computational Thinking)

The process of filtering out irrelevant noise and focusing strictly on the essential characteristics needed to solve a problem.

Algorithmic Thinking

The development of clear, ordered, step-by-step instructions or rules designed to execute a task or solve a problem.

Software Development Lifecycle (SDLC)

A structured roadmap of sequential and iterative phases—planning, design, implementation, testing, deployment, and maintenance—used to build and support software applications.

Waterfall Model

A traditional, linear software development methodology where progress flows sequentially through requirements, design, implementation, testing, deployment, and maintenance, with each stage requiring completion and approval before the next begins.

Agile Model

A flexible, iterative development methodology that delivers functional software in short cycles called sprints (typically lasting $2\text{--}4$ weeks), prioritizing customer feedback and rapid adaptability.

Spiral Model

A risk-driven development methodology that combines iterative prototyping with systematic risk analysis through recurring loops of planning, risk assessment, engineering, and evaluation.

Blue-Green Deployment

A software release technique utilizing two identical production environments (one active and one idle); updates are applied and tested in the idle environment before routing live user traffic to it.

Rolling Deployment

A deployment method that minimizes downtime by incrementally upgrading software instances one by one across a system until all nodes run the updated version.

Flash Deployment

A deployment strategy that completely shuts down the existing software version before launching the updated one, causing temporary downtime but guaranteeing a clean start.

Shadow Deployment

A deployment approach that runs a new software version alongside the current active version to test performance on real traffic without exposing any outputs to live users.

Token (AI)

The fundamental unit of data processed by an AI model, consisting of a short text segment roughly four characters or approximately three-quarters of an English word.

Tokenization

The process of splitting raw unstructured data (such as text, images, or audio) into smaller units called tokens for numerical embedding and model interpretation.

Context Window

The maximum capacity of combined input and output tokens that an AI model can process in a single exchange.

Graphics Processing Unit (GPU)

A massively parallel processor originally engineered for computer graphics that is widely used to accelerate matrix calculations in AI training and inference.

Tensor Processing Unit (TPU)

An application-specific integrated circuit engineered by Google specifically to optimize tensor calculations in machine learning workflows.

CIA Triad

A foundational information security model consisting of three core pillars—Confidentiality, Integrity, and Availability—used to protect systems, networks, and data.

Confidentiality (CIA Triad)

The principle of restricting access and disclosure to ensure that proprietary and sensitive information can only be viewed by authorized users and processes.

Access Control

The security protocols and permissions implemented to govern who can view, execute, or modify computing resources, files, and networks.

Data Masking

The security technique of replacing sensitive records with fictitious but structurally realistic values (e.g., substitution, shuffling, number variance) for development or testing.

Anonymization

A privacy technique that permanently removes all personally identifiable information from datasets, making re-identification impossible.

Pseudonymization

A data protection technique that replaces personal identifiers with artificial aliases, preserving the potential to re-identify data under designated circumstances.

Integrity (CIA Triad)

The security principle dedicated to maintaining the accuracy, completeness, and trustworthiness of data across its lifecycle by guarding against unauthorized modification or deletion.

Availability (CIA Triad)

The principle guaranteeing that systems, resources, and data remain reliably and timely accessible to authorized personnel whenever required.

Symmetric Encryption

A cryptographic method that uses a single shared secret key to both encrypt plaintext and decrypt ciphertext, providing fast performance ideal for bulk storage at rest.

Asymmetric Encryption

A cryptographic system utilizing a public key for encryption and a distinct private key for decryption, supporting secure transmission in motion, authentication, and non-repudiation.

Non-Repudiation

A security property ensuring that an actor cannot deny sending a message or executing an action, and the recipient cannot deny receiving it, proven via private key cryptography.

Hashing

A one-way cryptographic mathematical function that converts arbitrary data into a fixed-length string (message digest), serving as an irreversible digital fingerprint to verify integrity.

Filter Bubble

An algorithmic state in which personalization engines repeatedly expose individuals to information matching their past behavior, isolating them from opposing views and amplifying echo chambers.

Deepfake

Synthetic media created or modified via artificial intelligence techniques to generate hyper-realistic depictions of events, audio, or people that never actually occurred.

Model Fairness

The ethical design standard requiring AI algorithms to generate unbiased, equitable outputs across demographic and social groups to avoid discrimination.

Explainability (AI)

The characteristic of an AI system that allows human stakeholders to understand and trace the underlying reasoning and logic driving its decisions.

Human-in-the-Loop (HITL)

An operational design framework that integrates human oversight, review, and judgment at critical stages of automated AI workflows to balance speed with accountability.

Agentic AI

Autonomous AI systems that independently plan and carry out complex multi-step actions toward specified goals, requiring robust guardrails to avoid unintended side effects.

Edge AI

The deployment of artificial intelligence algorithms directly on local hardware devices rather than cloud servers, facilitating real-time latency and enhanced data privacy.