Using AI Responsibly: A Practical Guide to Ethics, Security and Privacy
By admin · September 1, 2026
Large language models such as ChatGPT and Claude can make knowledge work faster and more accessible. But their convenience can also tempt us to share too much, trust too quickly or hand over decisions that still require human judgment.
Generative AI has moved rapidly from novelty to everyday tool. People now use systems such as ChatGPT and Claude to draft emails, analyse documents, write code, study unfamiliar topics and automate business processes. Used well, these tools can improve productivity and help people turn ideas into useful work. Used carelessly, they can expose confidential information, reinforce unfair assumptions and produce convincing but incorrect results.
Ethical AI use is therefore not only a matter for developers or regulators. Every person who enters a prompt, uploads a document or acts on an AI-generated answer makes choices that affect privacy, security and other people.
Treat an AI conversation as data sharing
An LLM interface feels like a private conversation, but it is still an online service. A prompt may contain far more information than the user realizes: names, health details, customer records, internal plans, source code, passwords, financial figures or clues that identify a person when combined.
A simple rule is useful: do not put information into a public AI service if its accidental disclosure would harm you, another person or your organisation. This includes information protected by confidentiality agreements, personal data received from customers, unpublished business strategies and authentication secrets.
Before submitting a document or prompt, ask:
Do I have the right and permission to share this information?
Does the task require the real names, account numbers or exact figures?
Could I replace sensitive details with placeholders or synthetic examples?
What would happen if this prompt became public?
Data minimisation is often enough. “Customer A,” approximate values and a short relevant extract may achieve the same result as uploading an entire unredacted file. Remove names, email addresses, signatures, IDs, metadata, API keys and other identifiers before uploading material.
Platform settings matter too. In ChatGPT, users can turn off Improve the model for everyone under Data Controls. OpenAI states that chats then remain in history but are not used to train its models. ChatGPT also offers Temporary Chat; unsaved temporary conversations do not appear in history, do not create memories and are not used for model improvement, although a copy may be retained for up to 30 days for safety purposes. Claude users should review their privacy settings as well. Anthropic states that deleted consumer conversations are removed from back-end storage within 30 days, while chats permitted for model improvement may be retained in de-identified form for longer. Claude’s Incognito chats are not used for model improvement.
These controls reduce exposure, but they do not turn a consumer chatbot into an approved vault for secrets. For sensitive organisational work, use a service and account type formally approved by the organisation, with suitable contractual, retention and access controls.
AI can sound certain and still be wrong
LLMs generate likely sequences of words; they do not guarantee truth. They may invent facts, citations, legal provisions, software functions or statistics. Their confident tone can make these errors difficult to notice.
Ethical use requires matching the level of verification to the possible harm. A brainstorming suggestion may need only a quick sense-check. Medical, legal, financial, security or employment advice demands qualified human review and authoritative sources. Code should be reviewed and tested before deployment. Citations should be opened and checked rather than trusted because they look plausible.
AI should support judgment, not replace accountability. If an AI-assisted decision affects someone’s job, access to a service, credit, education, health or legal position, a responsible person must understand the basis of the decision and be able to challenge or correct it.
Bias, dignity and fairness
LLMs learn patterns from large datasets containing social biases and historical inequalities. Outputs can reproduce stereotypes or work less reliably for underrepresented languages, cultures and communities.
Users should check whether an output makes unsupported assumptions about nationality, ethnicity, gender, disability, religion, age or socioeconomic status. When AI is used to compare people—such as screening job candidates—organisations should test for unequal outcomes, document the criteria, allow human review and provide a path for appeal. Asking an AI to rank people does not make the underlying criteria objective.
Responsible use also means avoiding deceptive or harmful content. Do not use AI to impersonate real people, fabricate evidence, create targeted harassment or present synthetic material as authentic. When AI has materially shaped content or a decision, disclose that involvement where a reasonable audience would expect to know.
Security threats are not limited to stolen passwords
LLM systems introduce several less familiar risks.
Prompt injection occurs when malicious instructions hidden in a webpage, email or uploaded document try to override the user’s request. This becomes especially dangerous when an AI assistant can browse, read private files, send messages or execute actions. Text retrieved from outside sources should be treated as untrusted data—not as authority to reveal information or take action.
Sensitive-information disclosure can occur when users place secrets in prompts, when an AI-connected application has excessive access, or when generated outputs reveal material to the wrong person. Insecure output handling arises when generated code or commands are executed without validation. The OWASP guidance for LLM applications also warns about excessive agency: an AI system given broad autonomy can take unintended actions even without malicious intent.
AI also makes phishing easier to personalise and polish. Treat urgent requests for money, credentials, confidential files or login approval with suspicion, even when the language sounds professional or appears to come from someone familiar. Verify unusual requests through a separate, trusted channel.
A practical safety routine
Users can significantly reduce risk with a few consistent habits:
Use official services and apps. Check the domain and publisher before signing in. Avoid unofficial browser extensions or “free AI” tools that request unnecessary permissions.
Protect the account. Use a unique password, enable multi-factor authentication or a passkey where available, secure the connected email account and review active sessions.
Minimise every prompt. Share only what the task needs. Redact personal data, confidential figures, credentials and identifying metadata.
Review privacy controls. Decide whether conversation history, memory and model-improvement settings fit the task. Use temporary or incognito modes for lower-retention conversations, while remembering that they are not a licence to submit secrets.
Be cautious with files, links and connectors. A connected AI tool may reach cloud drives, email, calendars or business applications. Grant the minimum access required, disconnect unused services and inspect actions before approval.
Treat external content as untrusted. Documents and websites can contain prompt-injection instructions. Do not let their content authorize data disclosure, payments, account changes or outbound messages.
Verify before acting. Check important claims against primary sources. Review and test generated code in an isolated environment. Never paste commands into a production system without understanding them.
Keep humans in control. Require confirmation before an AI sends, publishes, purchases, deletes or changes access. High-impact decisions should always have an accountable human reviewer.
Delete what is no longer needed. Remove old conversations and uploaded files when they no longer serve a purpose, subject to organisational record-keeping requirements.
Report problems quickly. If a secret is entered into an AI tool, treat it as potentially exposed: revoke or rotate the credential, notify the appropriate security or privacy contact and document what happened.
The right question is not simply “Can AI do this?”
The better question is: Can we use AI for this purpose without creating unreasonable risks for the people whose information, rights or opportunities are involved?
Responsible AI use does not require avoiding these tools. It requires using them deliberately. Keep sensitive data out unless an approved environment and legitimate purpose justify it. Verify important outputs. Give AI systems only the access they need. Be transparent when AI materially contributes to content or decisions. Most importantly, keep human responsibility where it belongs—with the people choosing to use the technology.
Further reading
OpenAI: Data Controls FAQ
OpenAI: Temporary Chat FAQ
Anthropic: How long do you store my data?
OWASP: Top 10 for Large Language Model Applications
UK National Cyber Security Centre: ChatGPT and large language models—what’s the risk?
This article provides general information, not legal, medical or cybersecurity advice for a specific situation.
Generative AI has moved rapidly from novelty to everyday tool. People now use systems such as ChatGPT and Claude to draft emails, analyse documents, write code, study unfamiliar topics and automate business processes. Used well, these tools can improve productivity and help people turn ideas into useful work. Used carelessly, they can expose confidential information, reinforce unfair assumptions and produce convincing but incorrect results.
Ethical AI use is therefore not only a matter for developers or regulators. Every person who enters a prompt, uploads a document or acts on an AI-generated answer makes choices that affect privacy, security and other people.
Treat an AI conversation as data sharing
An LLM interface feels like a private conversation, but it is still an online service. A prompt may contain far more information than the user realizes: names, health details, customer records, internal plans, source code, passwords, financial figures or clues that identify a person when combined.
A simple rule is useful: do not put information into a public AI service if its accidental disclosure would harm you, another person or your organisation. This includes information protected by confidentiality agreements, personal data received from customers, unpublished business strategies and authentication secrets.
Before submitting a document or prompt, ask:
Do I have the right and permission to share this information?
Does the task require the real names, account numbers or exact figures?
Could I replace sensitive details with placeholders or synthetic examples?
What would happen if this prompt became public?
Data minimisation is often enough. “Customer A,” approximate values and a short relevant extract may achieve the same result as uploading an entire unredacted file. Remove names, email addresses, signatures, IDs, metadata, API keys and other identifiers before uploading material.
Platform settings matter too. In ChatGPT, users can turn off Improve the model for everyone under Data Controls. OpenAI states that chats then remain in history but are not used to train its models. ChatGPT also offers Temporary Chat; unsaved temporary conversations do not appear in history, do not create memories and are not used for model improvement, although a copy may be retained for up to 30 days for safety purposes. Claude users should review their privacy settings as well. Anthropic states that deleted consumer conversations are removed from back-end storage within 30 days, while chats permitted for model improvement may be retained in de-identified form for longer. Claude’s Incognito chats are not used for model improvement.
These controls reduce exposure, but they do not turn a consumer chatbot into an approved vault for secrets. For sensitive organisational work, use a service and account type formally approved by the organisation, with suitable contractual, retention and access controls.
AI can sound certain and still be wrong
LLMs generate likely sequences of words; they do not guarantee truth. They may invent facts, citations, legal provisions, software functions or statistics. Their confident tone can make these errors difficult to notice.
Ethical use requires matching the level of verification to the possible harm. A brainstorming suggestion may need only a quick sense-check. Medical, legal, financial, security or employment advice demands qualified human review and authoritative sources. Code should be reviewed and tested before deployment. Citations should be opened and checked rather than trusted because they look plausible.
AI should support judgment, not replace accountability. If an AI-assisted decision affects someone’s job, access to a service, credit, education, health or legal position, a responsible person must understand the basis of the decision and be able to challenge or correct it.
Bias, dignity and fairness
LLMs learn patterns from large datasets containing social biases and historical inequalities. Outputs can reproduce stereotypes or work less reliably for underrepresented languages, cultures and communities.
Users should check whether an output makes unsupported assumptions about nationality, ethnicity, gender, disability, religion, age or socioeconomic status. When AI is used to compare people—such as screening job candidates—organisations should test for unequal outcomes, document the criteria, allow human review and provide a path for appeal. Asking an AI to rank people does not make the underlying criteria objective.
Responsible use also means avoiding deceptive or harmful content. Do not use AI to impersonate real people, fabricate evidence, create targeted harassment or present synthetic material as authentic. When AI has materially shaped content or a decision, disclose that involvement where a reasonable audience would expect to know.
Security threats are not limited to stolen passwords
LLM systems introduce several less familiar risks.
Prompt injection occurs when malicious instructions hidden in a webpage, email or uploaded document try to override the user’s request. This becomes especially dangerous when an AI assistant can browse, read private files, send messages or execute actions. Text retrieved from outside sources should be treated as untrusted data—not as authority to reveal information or take action.
Sensitive-information disclosure can occur when users place secrets in prompts, when an AI-connected application has excessive access, or when generated outputs reveal material to the wrong person. Insecure output handling arises when generated code or commands are executed without validation. The OWASP guidance for LLM applications also warns about excessive agency: an AI system given broad autonomy can take unintended actions even without malicious intent.
AI also makes phishing easier to personalise and polish. Treat urgent requests for money, credentials, confidential files or login approval with suspicion, even when the language sounds professional or appears to come from someone familiar. Verify unusual requests through a separate, trusted channel.
A practical safety routine
Users can significantly reduce risk with a few consistent habits:
Use official services and apps. Check the domain and publisher before signing in. Avoid unofficial browser extensions or “free AI” tools that request unnecessary permissions.
Protect the account. Use a unique password, enable multi-factor authentication or a passkey where available, secure the connected email account and review active sessions.
Minimise every prompt. Share only what the task needs. Redact personal data, confidential figures, credentials and identifying metadata.
Review privacy controls. Decide whether conversation history, memory and model-improvement settings fit the task. Use temporary or incognito modes for lower-retention conversations, while remembering that they are not a licence to submit secrets.
Be cautious with files, links and connectors. A connected AI tool may reach cloud drives, email, calendars or business applications. Grant the minimum access required, disconnect unused services and inspect actions before approval.
Treat external content as untrusted. Documents and websites can contain prompt-injection instructions. Do not let their content authorize data disclosure, payments, account changes or outbound messages.
Verify before acting. Check important claims against primary sources. Review and test generated code in an isolated environment. Never paste commands into a production system without understanding them.
Keep humans in control. Require confirmation before an AI sends, publishes, purchases, deletes or changes access. High-impact decisions should always have an accountable human reviewer.
Delete what is no longer needed. Remove old conversations and uploaded files when they no longer serve a purpose, subject to organisational record-keeping requirements.
Report problems quickly. If a secret is entered into an AI tool, treat it as potentially exposed: revoke or rotate the credential, notify the appropriate security or privacy contact and document what happened.
The right question is not simply “Can AI do this?”
The better question is: Can we use AI for this purpose without creating unreasonable risks for the people whose information, rights or opportunities are involved?
Responsible AI use does not require avoiding these tools. It requires using them deliberately. Keep sensitive data out unless an approved environment and legitimate purpose justify it. Verify important outputs. Give AI systems only the access they need. Be transparent when AI materially contributes to content or decisions. Most importantly, keep human responsibility where it belongs—with the people choosing to use the technology.
Further reading
OpenAI: Data Controls FAQ
OpenAI: Temporary Chat FAQ
Anthropic: How long do you store my data?
OWASP: Top 10 for Large Language Model Applications
UK National Cyber Security Centre: ChatGPT and large language models—what’s the risk?
This article provides general information, not legal, medical or cybersecurity advice for a specific situation.