How would you use generative AI at work?
Company: IBM
Role: Software Engineer
Category: Behavioral & Leadership
Difficulty: easy
Interview Round: Onsite
What are your thoughts on using generative AI tools at work? Describe:
- Where you would use them to improve productivity/quality
- Where you would avoid them (risk areas)
- How you would handle confidentiality, security, and correctness
- How you would introduce AI usage to a team (guidelines, reviews, measurement)
Overview: This question evaluates a candidate's judgment and leadership in applying generative AI tools, testing competencies in risk assessment, data confidentiality, security awareness, correctness verification, and team change management for a software engineer role.
Solution
A strong answer balances enthusiasm with risk management and concrete practices.
1) High-value, low-risk use cases
- Drafting and refining: design docs, runbooks, incident postmortems, PR descriptions.
- Code assistance: boilerplate, unit test scaffolding, refactoring suggestions, explaining unfamiliar code.
- Troubleshooting support: summarizing logs, suggesting hypotheses, generating investigation checklists.
- Knowledge work: converting meeting notes to action items, summarizing long specs.
2) Areas to avoid or treat as high risk
- Pasting proprietary code, customer data, credentials, or internal incident details into non-approved tools.
- Using generated output for security-sensitive code (auth, crypto, IAM policies) without deep review.
- Making decisions based solely on AI output for compliance/regulatory work.
3) How to ensure confidentiality and security
- Use only company-approved AI tools with enterprise privacy controls (no training on prompts, data retention controls, audit logs).
- Apply data classification rules: redact/abstract sensitive inputs.
- Never input secrets; rely on secret managers.
4) How to ensure correctness (because models hallucinate)
- Treat AI as a junior assistant: verify with source code, docs, and tests.
- Require tests for generated code; run linters and SAST.
- For factual claims, ask for citations/links and independently confirm.
- Prefer constrained tasks (transformations, summaries) over open-ended “invent a solution”.
5) Team rollout approach
- Start with a lightweight policy:
- approved tools list
- what data is allowed
- required review standards (e.g., “AI-generated code must have tests and peer review”)
- Provide examples/templates for safe prompts.
- Measure impact: cycle time, defect rate, on-call MTTR, documentation coverage.
- Encourage sharing wins and failures to build collective best practices.
6) A good closing statement
- “I’m optimistic about productivity gains, but I’m careful about data handling and correctness. I use it to accelerate routine work and ideation, and I rely on engineering controls—reviews, tests, security scanning, and approved tooling—to make it safe.”