Risks for RLB

We spent time gathering insight from colleagues and customers to help inform and guide RLB AI principles, and ensuring all potential risks and dependencies are taken into consideration.

RLB AI opportunities have been prioritised based on (actual or perceived) associated risk. We explored risks through the lens of the company, colleagues, customers, with the following risk areas emerging:

Customer trust & confidence
Unintentional data and intellectual property leakage
Large market disruption

Our AI Principles have been prioritised in accordance with the level of perceived risk, and have been used to inform subsequent recommended AI guardrails.

A cross-business AI Council should be formed to own the AI Principles, as well as maintaining and upholding the relevant AI Guardrails

Risk Lens	Company	Colleagues	Customer
Risk Groupings	Reputational Confidential Data leakage Confidentiality + NDAs	Employee Trust Lack of AI awareness Manual checking overhead Inconsistent results Mistrust in the value of roles	Erosion of Trust Intrinsic bias in historical data Accuracy of output Explainability of output
	Operational Data integrity IP leakage Lack of internal traceability Unsafe instruction Liability & premiums	Job Security Automation anxiety Reliance on software	Brand perception Data integrity Carbon copy output Poor value outcomes
	Legal (negligence) Customer Data (PII) leakage Corporate governance breach Inadvertent negligence Plagiarism	Talent Retention Perceived de-skilling Reduced employee motivation Employee disempowerment	Lack of Innovation Product stagnation Market disruption Service commoditisation

Safe

Our AI should be built with safety, security and privacy by design.

To ensure AI services do not compromise on the confidentiality, integrity or availability of sensitive data or systems. Our AI tooling aligns with industry best practice.

Accountable

Our AI should be used as a tool to support and augment intelligence, with humans ultimately accountable.

To ensure users are responsible and answerable for all resulting AI output.

Ethical

Our usage will be assessed to ensure that we publish accurate, unbiased AI content that treats everyone fairly.

To ensure AI services align to fair and safe values, principles and policies for the benefit of our stakeholders and society.

Transparent

Our AI usage will maximise clarity and explainability around how we use it, its input and output.

To ensure AI services are trustworthy they must be, open and understandable to all stakeholders, with as much disclosure as possible.

Responsible

All individuals and stakeholders have a right to manage their data used by well governed AI services.

To ensure our AI systems adhere to compliance guidelines, organizational operating models and a defined code of conduct.

Innovation

Explore and leverage the use of AI services to innovate, disrupt the market and deliver better customer experience.

To ensure the use of AI services provides a credible business benefit to internal and external stakeholders.

The value of an AI council

A cross-business AI Council should be formed to own the AI Principles, as well as maintaining and upholding the relevant AI Guardrails.

An AI council provides a practical implementation for how to avoid and manage escalations, where colleagues have chosen to circumvent AI Guardrails.

To avoid

Block corporate devices on unauthorised tools
Training & Awareness
Sign-post AI policy in current relevant documentation, processes and policies
Mechanism to hold people to account

Escalations

Revisit and tweak existing escalation processes
Revisit current escalation mechanism to ensure AI scenarios are considered,
e.g. quarterly Crisis & Issues group, Incident Response team

Privacy & Cookie Policy