The value and risk of Generative AI (GenAI) depends on two key controls: inputs and outputs. These controls determine what AI systems produce and how useful, secure and valuable those outputs are in practice.
When people feed confidential, sensitive or proprietary data or poorly framed prompts into GenAI systems, the tools can amplify flaws. This exposes organisations to legal and regulatory exposure, reputation and trust erosion, and business disruptions. Similarly, accepting AI-generated answers to critical questions at face-value can lead to the same three categories of risk. On the other hand, when inputs are carefully curated and outputs thoughtfully managed, GenAI can accelerate insight, improve service delivery, and generate real competitive advantage.
Mastering these controls is now more urgent than ever. Stanford University data shows AI adoption in business operations surged from 55% to 78% of organisations in just one year. Yet this rapid deployment often outpaces the governance frameworks needed to manage the associated risks. This creates a dangerous gap between AI capability and organisational readiness.
This article identifies two pressure points every leadership team must manage. These help minimise risks and maximise opportunities of GenAI systems:
- Input risk – the information supplied to the model.
- Output risk – the content the model provides in return.
In this article, we examine these risks in detail. This helps leaders understand the consequences and develop appropriate controls and mitigations to maximise the value of GenAI use across the organisation. We start by looking at the inputs. This includes how information provided to GenAI systems can create hidden vulnerabilities.
Understanding GenAI input risk
The principle of "quality in, quality out" gains critical importance with GenAI. The quality, nature and source of information users provide to these models can open significant vulnerabilities. This applies whether the information is provided directly or indirectly. These input risks primarily stem from people's actions and the underlying systems connected to the GenAI.
Confidentiality and IP leakage: the unintended disclosure
One of the most significant risks of using GenAI is inadvertently inputting confidential, sensitive or proprietary data. Users may unknowingly share confidential data, unpatented IP, strategic plans or personal information with public-facing AI tools. The 2024 Work Trend Index Annual Report from Microsoft found that 78% of AI users bring their own AI tools to work (BYOAI). These tools often lack enterprise security features and usage cannot be monitored. Even when AI providers claim not to use inputs for training, risks remain due to logging, accidental sharing or internal vulnerabilities. Such leaks can compromise competitive advantage, breach privacy laws, damage client trust and trigger costly remediation or legal action.
Amplifying bias and reputational risk: reinforcing flawed perspectives
Users may unknowingly introduce flawed assumptions, outdated information or personal biases through their prompts or uploaded content. GenAI is designed to build upon these prompts and context which can amplify issues in their outputs that appear authoritative but are misleading, offensive or discriminatory. This creates a dual threat: flawed internal decision-making and the risk of publishing harmful or unethical content. Without clear guidelines or ethical training, even well-intentioned users may generate harmful material. This material can perpetuate stereotypes, alienate audiences or expose the organisation to legal and reputational consequences. Most organisations express concern about bias in GenAI and admit they are not adequately addressing it. The need for robust safeguards and training has never been more urgent.
System-generated input risks via RAG and connected systems
Many enterprises use GenAI systems with retrieval-augmented generation (RAG) capabilities (for example, Microsoft Copilot) to pull and synthesise information from corporate data. That seamless access to information introduces three principal risks:
- Trust Trap (RAG Data)
- Users assume retrieved information is accurate and complete. In reality, stale or partial documents feed flawed data into GenAI outputs.
- Unintended exposure
- Even without direct user input, broad RAG permissions can expose sensitive materials in GenAI responses. These include HR records or financial forecasts.
- Context loss
- RAG often returns discrete text snippets. These lack surrounding caveats or nuance. The AI then recombines them into superficially coherent answers that may lack depth or misstate limitations.
Understanding GenAI output risks
The creative nature of GenAI's outputs presents a distinct set of challenges that organisations must critically evaluate. Beyond input considerations, these models are designed to generate plausible and coherent content, not necessarily factual truth, creating specific and evolving risks.
Hallucination and systemic bias
A primary challenge lies in the inherent unreliability of GenAI outputs. This is a multifaceted issue stemming from how these models learn and create. At its core, AI can hallucinate. It generates entirely fabricated information with convincing authority. This is a critical risk, especially for junior staff or those less familiar with a topic. They might struggle to discern these well-crafted falsehoods. Worryingly, as AI advances, we anticipate fewer but higher-quality hallucinations. This makes detection even more difficult. Compounding this, the vast datasets AI trains on can embed and amplify existing biases. These biases are inherent in real-world data. This leads to outputs that are not only inaccurate but also discriminatory. Whether it's a fabricated fact or a biased recommendation, both outcomes undermine trustworthiness. They can lead to flawed decisions, reputational damage and significant legal exposure.
Plagiarism and Intellectual Property infringement
The original source of AI-generated content raises significant issues around plagiarism and intellectual property (IP) infringement. While AI doesn't 'copy-paste', its outputs can inadvertently mimic patterns, styles or even specific phrases from copyrighted training data. The legal landscape is rapidly evolving and uncertain. This poses direct IP infringement risks for professional service providers. It also complicates the ownership and commercialisation of AI-created works. Using AI-generated content without proper verification can lead to costly legal battles, injunctions and a loss of client trust. This happens if their work product is found to be unoriginal or infringing existing IP. Furthermore, it complicates the ownership and commercialisation of AI-generated content. This requires organisations to carefully consider their IP strategies and contractual agreements related to AI tool usage.
The peril of unchecked outputs: over-reliance and nuance loss
Perhaps the most insidious behavioural risk is over-reliance. This is where users accept AI-generated responses without sufficient critical review or independent verification. This is compounded by the fact that AI often struggles with nuance and contextual interpretation. It provides outputs that may be technically correct but overly simplistic or decontextualised. Alarming statistics show a significant number of employees rely on AI output without evaluating accuracy. This leads directly to mistakes in their work. For tasks requiring deep strategic insight, empathetic communication or highly contextualised advice, this automated acceptance can lead to superficial analysis, miscommunication and a failure to address the true complexities of a situation. Mitigating these output risks requires not just technological safeguards but also robust internal protocols. It also requires a culture of critical engagement where human oversight remains paramount.
Charting a safer course: mitigating user-centric AI risks
While the risks associated with user interaction with GenAI are significant, they are not insurmountable. Proactive management requires a holistic, multi-faceted approach. This extends beyond mere technical safeguards. For organisations, mitigating these risks is not just about compliance. It is about unlocking the true, responsible potential of AI while safeguarding core business assets and reputation. This necessitates a robust AI governance framework and a fundamental shift in organisational culture. In this culture, every individual actively contributes to responsible AI practices.
Key strategies for managing these user-centric risks include:
- Comprehensive user training and awareness: Empowering every user with a clear understanding of GenAI's capabilities and limitations, potential risks, and best practices for responsible interaction. This includes developing interactive workshops focusing on data classification and prompt engineering best practices, alongside training on data privacy, IP, bias detection, and critical evaluation of AI outputs.
- Robust governance and policy frameworks: Establishing clear, actionable policies on acceptable use of GenAI, data handling protocols, confidentiality guidelines, and mandatory review processes for AI-generated content. These policies must be regularly updated and communicated, drawing on industry best practices and legal counsel, forming part of a broader AI Governance Framework.
- Implementing technical controls and safeguards: Deploying data loss prevention (DLP) tools, granular access controls for RAG systems, model monitoring for anomalous behaviour, and secure enterprise-grade AI platforms that do not use user data for training.
- Emphasising human validation: Reinforcing the critical role of human oversight, expertise, and critical thinking in reviewing, verifying, and refining AI outputs.
- Establishing clear accountability structures: Defining roles and responsibilities for AI usage, data input, output validation, and incident response. Clear accountability ensures risks are owned and managed effectively throughout the organisation.
GenAI offers an unparalleled opportunity for innovation and efficiency
However, its true value can only be realised when organisations proactively address the inherent risks. This particularly applies to risks arising from the everyday interactions of their users. By understanding the vulnerabilities associated with both inputs and outputs, businesses can transform potential pitfalls into pathways for responsible growth. This requires implementing a robust AI governance framework, training and technical controls. Organisations that take this proactive approach will be best positioned to unlock AI's transformative potential safely and responsibly. Success also depends on fostering a culture of critical human oversight.
