AI has moved beyond the experimental. Today, businesses are embedding it into everyday processes, with LLM-powered chatbots handling customer queries, intelligent tools supporting decision-making and automation boosting operational efficiency. The potential – for faster service, smarter insights, greater scalability – is mind-blowing.
But while the rewards are significant, so too are the risks. Without strict safeguards, not only can AI expose sensitive data – it can introduce bias, misinformation or compliance breaches that damage customer trust. In the second instalment of our security and compliance series, we explore how to mitigate those risks.
What is an LLM?
A large language model (LLM) is AI that’s trained on massive datasets to understand natural language and generate human-like responses across a wide range of tasks, even reasoning through complex problems. Models including GPT, Claude and Llama can:
- Answer questions in plain language
- Generate fluent, contextually aware responses
- Summarise lengthy documents or conversations
- Highlight insights to support decision-making
Versatile and smart, LLMs are increasingly used as virtual assistants, customer service agents or research partners. Yet they’re not flawless. LLMs can hallucinate, producing convincing but incorrect information. They can inherit biases from their training data. And they can expose sensitive information.
However, with the right controls, LLMs have incredible potential to unlock innovations, drive growth and boost efficiencies – without compromising security, compliance or trust.
6 golden rules for safeguarding AI deployments
Follow these 6 golden rules to safeguard your AI deployments, including LLMs:
1. Prioritise data governance and security
LLMs often handle sensitive customer or business data, making strong governance crucial. Treat your model as part of your data infrastructure, ensuring it’s subject to the same safeguards as your CRM or payment system. Make sure you:
- Restrict access – ensure that only authorised users and applications can interact with it
- Encrypt data – in transit and at rest
- Mask personal information – names, addresses, account numbers – before feeding prompts into models
2. Understand and manage bias
LLMs are trained on vast datasets drawn from public and proprietary sources. This makes bias an inherent risk, with the potential to skew outputs, reinforce stereotypes or create compliance issues. To manage this:
- Test regularly – monitor outputs for patterns of bias, such as gendered or cultural stereotypes
- Fine-tune carefully – use diverse, representative datasets when adapting models for your business
- Keep humans involved – always add review processes for high-stakes applications like recruitment screening or lending decisions
Remember, bias can’t be eliminated entirely, but it can be identified, managed and mitigated. With the right safeguards, LLMs can deliver value without embedding hidden risks.
3. Set boundaries to keep AI safe
Without guardrails, LLMs can cause compliance breaches, expose sensitive data or generate inappropriate content. Setting boundaries for how they’re used is, therefore, essential. Here’s how:
- Define usage policies – make it clear to employees what is and isn’t acceptable (for example, never paste customer PII into a public LLM)
- Set limits on automation – let the model support workflows (such as drafting emails) but ensure that humans have the final sign off
- Filter outputs – apply content and compliance filters to block toxic, unsafe or non-compliant responses
Guardrails reinforce the role of AI as a trusted assistant – one that helps your teams work faster without putting your business, customers or reputation at risk.
4. Monitor and audit continuously
Unlike traditional software, LLMs don’t always behave predictably – with outputs varying depending on prompts or context. As a result, it’s important to monitor and audit them on an ongoing basis to keep them safe.
- Monitor in real-time – track and flag unusual or unsafe responses as they happen
- Keep audit logs – record prompts and outputs to support compliance and reviews
- Check for model drift – keep an eye on accuracy over time, retraining when customer behaviour or context changes
5. Build trust through transparency
Trust forms the basis of valuable customer interactions – and transparency is crucial to maintaining it. So, when deploying LLMs, make sure customers know when they’re engaging with AI – and be up front about how their data is handled. To do this effectively:
- Label interactions clearly – for example, “This response was generated by AI”
- Provide escalation paths – so customers can connect with a human agent should they wish
- Share data practices – explain how information is stored, protected and used
6. Plan for incident response
Even the best-governed AI systems can go wrong. What matters is how quickly and effectively you respond. Preparing in advance reduces the risk and reassures customers when issues arise. Make sure you:
- Develop playbooks – create incident response plans that align with AI and LLM-specific risks
- Train your teams – ensure relevant staff know how to spot problems and intervene fast
- Communicate clearly – establish channels to update and reassure customers if something fails
An effective response plan doesn’t just limit damage, it shows accountability and builds long-term trust. By treating AI incidents with the same seriousness as other security breaches or events, you’ll benefit from the innovation that LLMs provide without exposing your business – or customers – to unnecessary risk.
Strike the right balance between innovation and responsibility
LLMs are powerful tools, but they’re not “plug and play.” To harness their full potential safely, you need strong guardrails, robust governance and ongoing oversight.
When implemented responsibly, LLMs don’t just boost productivity across teams, they enable you to personalise customer interactions at scale while reducing operational costs and freeing up agents to focus on higher-value tasks.
However, without the right safeguards, AI deployments risk damaging customer trust and breaching regulatory requirements. The key is balance – it’s all about embracing innovation while protecting the process, your data and your reputation.
Ready to learn more about how to deploy LLMs securely and responsibly? Get in touch to explore best practices and practical support for your organisation.
