Artificial Intelligence Security

Artificial Intelligence Security (AIS) is the set of practices, controls, and technologies that protect Artificial Intelligence (AI) systems, models, data, and pipelines from threats, vulnerabilities, and misuse across their lifecycle, while preserving confidentiality, integrity, and availability.

Expanded Explanation

1. Technical Function and Core Characteristics

AIS covers risk management for Machine Learning (ML) and other AI systems, including model training, deployment, and monitoring. It addresses threats such as data poisoning, model theft, adversarial examples, prompt injection, and abuse of AI outputs in downstream systems.

It combines security engineering, privacy engineering, and AI-specific assurance techniques to protect training data, model artifacts, inference APIs, and associated infrastructure. It also includes governance processes that define security baselines, testing protocols, and criteria for secure AI deployment and operation.

2. Enterprise Usage and Architectural Context

In enterprises, AIS integrates with existing security architecture, including identity and access management, data security, application security, and cloud security. Organizations apply policies and controls to AI data pipelines, feature stores, model registries, and inference endpoints.

Architectures often incorporate secure data collection, robust training environments, model validation, red-teaming, and runtime monitoring for anomalous behavior or abuse. Enterprises align AI security practices with broader risk management, compliance, and audit frameworks to provide traceability and documented control coverage.

3. Related or Adjacent Technologies

AIS relates to ML security, Model Risk Management (MRM), software supply chain security, and data protection. It intersects with privacy-preserving techniques such as Differential Privacy (DP), federated learning, and secure multiparty computation.

It also connects with zero trust architectures, secure DevOps and Machine Learning Operations (MLOps), and security analytics that use AI for detection and response. Standards and guidance from security and standards bodies provide reference controls, taxonomies of AI threats, and testing methodologies for secure AI systems.

4. Business and Operational Significance

AIS supports reliable operation of AI-enabled products, decision-support systems, and automation in regulated and unregulated sectors. It reduces the likelihood that attacks or misuse of AI will disrupt services, expose data, or corrupt business processes.

Organizations use AI security practices to meet regulatory expectations, contractual obligations, and internal governance requirements related to safety, privacy, resilience, and auditability. It also supports trust in AI deployments by providing documented safeguards and continuous assurance activities over the AI lifecycle.