Managing AI Use in Business:
A Cyber Security Perspective
AI and LLMs bring huge opportunities – but also risks in the form of data leaks, shadow AI, and misinformation. In this blog, we’ll explore how to manage these challenges and harness AI securely.
Artificial Intelligence (AI) is rapidly transforming business operations, from automating workflows to enhancing decision making. However, its adoption introduces new cyber security considerations that must be addressed to protect sensitive data, maintain compliance, and safeguard organisational reputation.
Cyber Security Challenges of Generative AI
Large Language Models (LLMs) like ChatGPT, Copilot, and other generative AI tools are reshaping how organisations work, accelerating content creation, enhancing decision making, and enabling rapid knowledge retrieval. However, their adoption introduces unique cyber security challenges. LLMs process vast amounts of data, can generate convincing but inaccurate content, and may inadvertently expose sensitive information if not governed properly. Secure and responsible use of LLMs is therefore essential to protect intellectual property, maintain compliance, and preserve trust.
Risks and Organisational Considerations
From a cybersecurity perspective, the use of generative AI introduces several high impact risks that demand proactive management. Sensitive data entered into AI tools – whether for drafting documents, analysing trends, or generating code – can be inadvertently stored, processed, or even used to train external models, creating the potential for data leakage and regulatory breaches. The technology’s ability to produce highly convincing but false or biased outputs (“hallucinations”) can also mislead decision making or propagate misinformation. Furthermore, generative AI can be weaponised by threat actors to automate phishing campaigns, craft deepfake content, or accelerate social engineering attacks at scale. The rapid adoption of these tools, often without formal vetting, increases the risk of “shadow AI” use by employees, bypassing corporate security controls and exposing proprietary or client information. These factors combined make it essential for organisations to implement strict usage policies.
Risks & What to Watch Out For:
- Data Leakage: Sensitive or regulated information entered into an LLM may be stored or processed externally, risking exposure.
- Hallucinations & Inaccuracy: LLMs can produce plausible but factually incorrect or outdated information.
- Prompt Injection Attacks: Malicious inputs can manipulate an LLM into revealing restricted data or executing unintended actions.
- Bias & Compliance Risks: Outputs may reflect biases in training data, creating reputational or legal exposure.
- Over Reliance: Excessive dependence on AI outputs without human validation can erode critical thinking and quality control.
- Third Party Risk: Using external LLM providers introduces supply chain vulnerabilities if their security posture is weak.
Recomendations:
- Define an LLM Usage Policy: Specify approved tools, permitted use cases, and prohibited data types.
- Implement Access Controls: Restrict LLM access to authorised personnel and integrate with identity management systems.
- Sanitise Inputs: Train staff to remove sensitive or regulated data before submitting prompts.
- Validate Outputs: Require human review of AI generated content, especially for legal, financial, or compliance critical work.
- Monitor & Log Usage: Track interactions for auditing, anomaly detection, and incident investigation.
- Vendor Due Diligence: Assess LLM providers for security certifications, data handling practices, and contractual safeguards.
- Educate Users: Provide training on prompt hygiene, recognising AI generated misinformation, and avoiding manipulation.
- Integrate into Incident Response: Include LLM related risks in cyber incident playbooks, covering data exposure and malicious prompt scenarios.
LLMs can be transformative when deployed securely, offering speed, scale, and insight that traditional tools cannot match. But without disciplined governance, they can also introduce new and significant cyber risks. By embedding security controls, clear policies, and user education into every stage of LLM adoption, organisations can harness their benefits while protecting data, reputation, and compliance.
Looking to Unlock AI Without the Risk?
At NG-IT, we help organisations embrace innovation securely. We work with leading cybersecurity and infrastructure partners to help you:
- Define clear AI and data usage policies.
- Implement identity, access, and monitoring controls to reduce risk.
- Strengthen defences against phishing, data leakage, and supply chain vulnerabilities.
- Build resilient cloud and security frameworks that support innovation without compromising protection.
If you’re exploring how to adopt AI responsibly while securing your wider IT environment, NG-IT can guide you every step of the way. Contact us to learn more.
