harvey Security Engineer Interview: Questions & Prep (2026)

Harvey is an AI company building tools for law firms and corporate legal teams, which means Security Engineers there work at the intersection of AI, sensitive legal data, and enterprise compliance. With 367 open roles at Harvey as of mid-2026, the company is in an active growth phase, and Security Engineer is among the most critical hires they make.

Candidates report a multi-stage process that typically includes a recruiter screen, one or two technical rounds covering threat modelling, secure architecture, and cloud security, and a final round focused on cross-functional collaboration and security leadership. The full process typically spans a few weeks end to end.

Expect questions that go well beyond generic security knowledge. Harvey handles highly sensitive legal documents on behalf of major law firms, so interviewers probe how you think about data governance, access controls, and compliance frameworks like SOC 2 in an AI-first product environment. Knowing the OWASP Top 10 is a baseline; being able to reason about LLM-specific risks will set you apart.

These are the questions Harvey Security Engineer candidates most commonly report, based on shared interview experiences:

1. How would you approach threat modelling for an AI application that processes confidential legal documents?
2. Walk us through how you would design an access control system for a multi-tenant SaaS platform serving law firms.
3. Describe how you handled a security incident from detection through to post-mortem.
4. How do you think about securing LLM pipelines and protecting against prompt injection or data leakage?
5. What does a SOC 2 Type II compliance programme look like, and have you helped build or maintain one?
6. How would you identify and remediate a misconfiguration in a cloud environment?
7. Tell me about a time you had to influence an engineering team to adopt a security practice they initially resisted.
8. How do you stay current with the evolving threat landscape, especially for AI and LLM systems?
9. Describe your hands-on experience with security tooling: SIEM, EDR, vulnerability scanners, or similar.
10. How would you handle a situation where a product feature request conflicts with a core security requirement?
11. What is your approach to secrets management in a cloud-native application stack?
12. How have you collaborated with legal, compliance, or executive stakeholders to meet regulatory requirements?

Q: Describe how you handled a security incident from detection through to post-mortem.

*Situation:* Our monitoring system flagged unusual API activity on a service account late one evening.

*Task:* I was the on-call security engineer responsible for triaging and containing the incident before it escalated.

*Action:* I immediately rotated the affected credentials and isolated the service account from production systems. I then pulled the access logs for the key across the prior several hours and identified that a developer had accidentally committed the key to a public repository. I coordinated with the DevOps team to scan for downstream misuse and notified the security lead and relevant stakeholders within the same hour.

*Result:* No customer data was accessed. We shipped a mandatory secrets-scanning pre-commit hook across all repositories within the same week, and the incident became a case study in our internal runbook.

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Q: Tell me about a time you influenced a team to adopt a security practice they pushed back on.

*Situation:* The product team at my previous company wanted to skip a threat model review for a new document upload feature, feeling it would slow their sprint.

*Task:* My job was to ensure the feature launched securely without becoming a blocker.

*Action:* Instead of escalating, I offered to run a focused, time-boxed session with just the lead engineer and the PM. I prepared a one-page risk summary in advance so we could move quickly. We identified one high-severity issue around unrestricted file type uploads that the team had not considered.

*Result:* The team fixed the issue before launch and requested a similar session for the next sprint. That lightweight review became a recurring ritual for new features.

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Q: How would you design access controls for a multi-tenant SaaS platform?

*Situation:* I was asked to redesign the authorisation model for a B2B SaaS product after a penetration test flagged tenant isolation gaps.

*Task:* I needed to propose a new model that prevented cross-tenant data leakage without requiring a full rewrite of the data layer.

*Action:* I evaluated role-based and attribute-based access control patterns, mapped the existing data flows, and proposed row-level security enforced at the database layer combined with strict API-level tenant context validation. I documented the model, reviewed it with the engineering lead, and created a phased migration plan.

*Result:* The new model passed a follow-up penetration test with no tenant isolation findings, and the documentation became the internal reference for all future multi-tenant feature work.

STAR for behavioural questions. Situation, Task, Action, Result. Keep the Situation brief, spend most of your time on Action, and always close with a concrete Result. Harvey interviewers care about what you actually did and what changed because of it.

Structured reasoning for technical design questions. Start by naming the assets you are protecting and the threat actors you are defending against. Then walk through your approach step by step and explicitly call out the trade-offs you considered. Harvey cares about reasoning, not just answers.

Think aloud for 'how would you' scenarios. State your assumptions, ask clarifying questions about scale and constraints, and explain your logic as you go. Jumping to a solution without scoping the problem reads as reactive rather than methodical.

Lead with impact for experience questions. When describing past work, open with what the outcome was, then explain how you got there. This keeps the interviewer anchored on value rather than process details.

Harvey builds AI products for law firms, which creates a specific security profile. Interviewers typically look for candidates who demonstrate the following:

Cloud and infrastructure security depth. Strong command of identity and access management, secrets management, and logging on at least one major cloud platform.

AI and LLM security awareness. Understanding of risks unique to language model applications: prompt injection, data leakage through model outputs, training data exposure, and guardrail design.

Compliance experience. Practical familiarity with SOC 2 or similar frameworks, especially in a SaaS context where customer data is highly regulated.

Communication across functions. Security engineers at Harvey work closely with product, legal, and engineering teams. Candidates who can translate risk into business terms and influence without authority tend to do well.

Ownership mindset. Interviewers want engineers who drive security improvements end to end, not those who flag issues and wait for others to fix them.

Week one: sharpen fundamentals. Refresh your knowledge of the OWASP Top 10, cloud IAM, secrets management, and network security. Go deep on at least one major cloud provider's IAM model, logging services, and security tooling.

Week two: go Harvey-specific. Read everything publicly available about Harvey's product. Think through how you would secure an AI application that ingests confidential legal documents. Map your past experience to the threat model of a legal AI company: sensitive data at rest and in transit, multi-tenant isolation, audit logging, and compliance readiness.

Week three: practise out loud. Do at least one timed mock interview where you walk through a cloud misconfiguration scenario from detection to remediation. Practise your STAR stories until the structure feels natural, not rehearsed.

Day before the interview. Review your top behavioural stories, confirm the interview format with your recruiter, and prepare a few questions that show you have thought about Harvey's specific security challenges.

Knok checks 150+ job sites nightly, applies to roles matching your resume, and messages HR for you, so you do not miss a new Harvey opening while you are heads-down on prep.

1. Being too generic. Saying 'I would use encryption' without specifying what, where, and why is a red flag at a company like Harvey. Specificity signals seniority.

2. Skipping the 'why'. Interviewers want your reasoning, not just your answer. Always explain the trade-offs behind your decisions.

3. Not asking clarifying questions. Jumping straight into a solution on a design problem without understanding scale, threat actors, or constraints makes you look reactive.

4. Underplaying cross-functional work. If all your stories are solo heroics, add examples of collaboration. Security at a product company means influencing without authority.

5. Ignoring AI-specific security. Harvey is an AI company. If you cannot speak to LLM security risks, data pipeline exposure, or model access controls, you will be at a disadvantage versus candidates who have done that homework.

6. Avoiding failure stories. Many candidates hesitate to describe incidents where something went wrong. Harvey values blameless post-mortems. Own the incident, describe what you learned, and explain what changed as a result.