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Knowledge Term

AI Security

AI security is the practice of protecting artificial intelligence systems, models, data, prompts and integrations from unauthorised access, manipulation, misuse and cyber threats while maintaining trust, resilience and business continuity.

Artificial intelligence securityAI cyber securitySecure AIData SecurityAI GovernanceResponsible AIData PrivacyAI Risk Management
Knowledge hub
AI for Ecommerce
Used in
Data Security • AI Governance • Responsible AI • Data Privacy • AI Risk Management
Reading time
6 minutes
Right Partners perspective

Every new AI capability creates new opportunities—but it can also create new attack surfaces. Security must evolve alongside innovation.

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Explanation

What AI Security means

A practical explanation of the concept and how it appears in digital transformation, ecommerce and technology decision-making.

AI security is the discipline of protecting artificial intelligence systems throughout their lifecycle. It includes securing AI models, training data, prompts, APIs, integrations and supporting infrastructure while ensuring AI outputs remain trustworthy and resilient against misuse.

Unlike traditional cyber security, AI security must also consider threats such as prompt injection, model manipulation, data poisoning, adversarial attacks and the unauthorised exposure of sensitive business information through AI systems.

Commercial relevance

Why it matters

Definitions are useful. Business context is where the value appears.

As organisations integrate AI into customer service, operations, sales and decision-making, AI becomes part of their critical business infrastructure. Poor AI security can expose confidential information, undermine customer trust and create operational or regulatory risks. Security should therefore be considered from the earliest stages of AI adoption rather than added later.

Clarification

Common misconceptions

A plain-English correction of the misunderstandings that often lead to poor decisions.

01
AI security is just cyber security.
Cyber security is part of AI security, but AI introduces additional risks such as prompt injection, model manipulation and insecure AI integrations.
02
Only public AI tools create security risks.
Private AI systems also require strong governance, access controls and monitoring.
03
AI vendors manage all security.
Organisations remain responsible for how AI is configured, governed and used.
04
Security slows AI innovation.
Well-designed security enables organisations to adopt AI with greater confidence and resilience.
Example

AI Security in practice

A simple example of how this concept might appear in a real ecommerce or transformation environment.

An organisation deploys an internal AI assistant connected to company documentation. Access controls ensure employees only retrieve information appropriate to their role, prompts are logged for governance, sensitive information is masked where appropriate and unusual activity is monitored to identify potential misuse.

FAQ

Common questions

Short answers to common questions about this term and how it applies in practice.

01 of 04

AI security is the practice of protecting AI systems, models, data and integrations from cyber threats, misuse and unauthorised access.

When to seek advice

When this becomes a business issue

These are the situations where a definition usually turns into a decision, risk or opportunity.

01
Employees are using AI tools without governance.
Develop AI security policies and approved usage standards.
02
AI systems access sensitive business information.
Review permissions, access controls and data protection measures.
03
Multiple AI vendors are being introduced.
Assess AI security and governance consistently across the technology landscape.
04
Leadership is concerned about AI risk.
Create an AI security framework that balances innovation with resilience.
Services

Related services

Where this concept connects to practical advisory support.

AI Readiness Assessment

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