decagon Security Engineer Interview: Questions, Experience & Prep (2026)

Decagon is an AI-native company building autonomous agents for enterprise customer support and operations. The company is in an active hiring phase, with 117 open roles listed as of mid-2026, making it one of the more prominent engineering employers in the AI agent space right now. A Security Engineer at Decagon typically works on protecting AI infrastructure, securing customer data pipelines, and ensuring the platform meets the compliance requirements of large enterprise clients.

The broader Security Engineer job market in India is strong. knok's jobradar tracked 628 open Security Engineer positions as of July 2026, with Bangalore leading at 69 openings, followed by Delhi and Pune at 12 each, Hyderabad at 10, Mumbai at 7, and Chennai at 6. Most Decagon engineering positions are remote-first or based out of US offices, so Indian candidates typically interview for globally remote roles.

Candidates report that the interview process typically spans several rounds covering technical depth, system design, and behavioural questions. The company moves relatively quickly by startup standards. Expect interviewers to probe both your security fundamentals and your ability to think about AI-specific threat surfaces from early in the process.

These are the questions candidates report most frequently across Decagon Security Engineer interviews. Prepare specific examples from your own experience for each one.

1. Walk me through how you would secure an AI inference pipeline that handles sensitive customer data.
2. How do you approach threat modelling for a SaaS product used by large enterprise clients?
3. Describe a time you discovered a critical vulnerability in a production system. What did you do?
4. How would you design a zero-trust architecture for a cloud-native product?
5. Decagon's agents handle customer conversations at scale. What are the unique security risks of an LLM-based product and how would you mitigate them?
6. How do you handle secrets management across a microservices environment?
7. Tell me about your experience running or supporting a compliance program like SOC 2. What was your personal contribution and what did the outcome look like?
8. Walk me through a real security incident you handled from detection to resolution.
9. How do you balance the pressure to ship fast as a startup with maintaining a strong security posture?
10. What controls would you put in place to prevent prompt injection attacks in an AI agent system?
11. How do you evaluate the security risk of a third-party vendor or API integration before approving it?
12. What logging and monitoring strategy would you implement to detect anomalous behaviour in a production AI system?

Use the STAR format (Situation, Task, Action, Result) for every experience-based question. Here are three model answers to help you structure your own.

Q: Describe a time you discovered a critical vulnerability in a production system.

*Situation:* During a routine code review at a previous company, I found that an internal API was accepting user-supplied file paths without sanitisation, opening up a directory traversal risk in a system that handled customer data.

*Task:* I needed to assess the blast radius quickly, patch the issue, and verify that no exploitation had already occurred.

*Action:* I escalated immediately to my engineering lead and raised a priority-one ticket. I audited recent access logs for anomalous path patterns, wrote a patch to sanitise inputs using an allowlist, added automated tests to cover the attack vector, and coordinated a hotfix deployment with the on-call team. I also documented the incident in a post-mortem and added the check to our standard code review checklist.

*Result:* No customer data was exposed. The patch shipped the same day and the new checklist item caught similar issues in the weeks that followed.

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Q: Walk me through a real security incident you handled from detection to resolution.

*Situation:* At a previous role, our monitoring system flagged an unusual spike in outbound API calls late at night. Initial investigation suggested a compromised service account.

*Task:* As the on-call security engineer, I needed to contain the breach, identify the full scope, and restore normal operations without disrupting customers.

*Action:* I immediately rotated and revoked the compromised credentials to stop further exposure. I pulled logs from our SIEM to trace any lateral movement, looped in the engineering lead and legal team to assess notification obligations, and once containment was confirmed, conducted a root-cause analysis. The credentials had been inadvertently committed to a public code snippet. I then audited all our repositories for similar leaks using a secret scanning tool and introduced automated pre-commit hooks to prevent recurrence.

*Result:* The incident was contained within hours with no customer data accessed. The changes I introduced reduced credential-exposure risk and became part of our security onboarding for all new engineers.

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Q: How do you balance the pressure to ship fast as a startup with maintaining a strong security posture?

*Situation:* At an early-stage startup where I worked, the engineering team was under pressure to ship features every week. Security reviews were seen as blockers by the product team.

*Task:* My goal was to build a lightweight security process that engineers would actually adopt, without slowing down the release cadence.

*Action:* I introduced a tiered review system: low-risk changes like UI updates or config tweaks required only a self-checklist, while changes touching authentication, data storage, or external APIs required a brief async security review. I built a shared reference document with common implementation patterns and their secure alternatives so engineers could self-serve. I also set up automated static analysis in CI to catch common issues before they ever reached review.

*Result:* Security review coverage improved across all high-risk changes and engineer complaints about security as a blocker dropped noticeably. The tiered model was formally adopted by team leads as the default process.

STAR for behavioural questions: Structure every experience-based answer as Situation, Task, Action, Result. Keep the Situation brief (one or two sentences), spend most of your time on Action, and always qualify the Result. Even without precise numbers, describe what changed and why it mattered.

Threat modelling with STRIDE: When asked how you assess risk, walk through Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. Apply the framework to the specific system in the question to show structured thinking rather than recited theory.

Design questions with layered security: For any 'how would you secure X' question, think in layers: perimeter controls, authentication and authorisation, data encryption in transit and at rest, logging and alerting, and incident response. Naming all layers signals seniority and end-to-end ownership.

AI-specific security thinking: Because Decagon builds with large language models, interviewers probe AI threat vectors. A strong framework covers input validation and prompt injection prevention, output filtering and content controls, model access controls, and data isolation between tenants. Demonstrating awareness of this distinct threat surface sets you apart from candidates with only traditional security backgrounds.

Candidates report that Decagon values engineers who think like adversaries first and builders second. The company is looking for people who can own security end to end, not act as a gate on other teams.

Hands-on cloud security experience: Practical work with AWS, GCP, or Azure security controls including IAM policies, VPC design, and secrets management is a consistent signal interviewers look for.

Compliance without a checkbox mentality: Familiarity with compliance programs (SOC 2 is commonly cited across enterprise SaaS teams) matters, but interviewers probe whether you understand the underlying risk or just the audit requirements. Expect follow-up questions that go beyond the checklist.

Coding ability: Candidates report that security engineers at Decagon are expected to write code, not just advise. Experience building security automation or tooling is a strong differentiator.

Clear communication of tradeoffs: The ability to explain a security tradeoff to a non-technical product manager or founder, without jargon, is something interviewers assess in behavioural rounds.

Curiosity about AI threats: Given the product is LLM-based, candidates who have thought about prompt injection, model output risks, and multi-tenant data isolation stand out from those with only traditional security experience.

Weeks one and two: core security fundamentals

Review cloud security (IAM, VPC, secrets management), application security (injection attacks, SSRF, broken authentication), and incident response workflows. Focus on areas where you have gaps rather than reviewing what you already know well.

Weeks two and three: AI security and company research

Read up on LLM-specific attack vectors including prompt injection, indirect prompt injection, data leakage through model outputs, and multi-tenant data isolation. Study Decagon's product publicly to understand what their agents do and what enterprise data they touch. Think through what you would secure if you had built the system from scratch.

Final week: mock interviews and practice

Practise threat modelling out loud on paper or a whiteboard. Run at least one mock system design session focused on securing a cloud-native SaaS architecture. Prepare several STAR stories covering incidents you have handled, compliance work you have driven, and security process improvements you have made. Prepare product-level questions to ask the interviewer about Decagon's current security challenges.

knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR on your behalf, so you can spend your energy on preparation rather than applications.

Treating compliance as security: Candidates who frame their experience entirely around passing audits without demonstrating actual threat thinking tend to struggle in Decagon interviews. Interviewers typically follow up with 'what would an attacker do here?' to test whether you understand the underlying risk or just the audit requirement.

Ignoring AI threat vectors: Many experienced security engineers have not thought deeply about LLM-specific risks. Arriving without any preparation on prompt injection or model output risks is a visible gap when the product you are being hired to protect is an AI agent platform.

Being vague about impact: Saying 'I improved the security posture' without any qualifier reads as weak. Describe what changed, what risk was reduced, and how you know. You do not need precise numbers, but you do need a concrete outcome.

Talking only about tools and certifications: Listing tools and frameworks without explaining the thinking behind each choice is a common pattern. Interviewers want to hear how you reason about risk, not just what appears on your resume.

Not preparing product questions: Failing to ask how Decagon's agents handle customer data, what their trust model looks like, or what security challenges the team is currently prioritising signals low engagement. Strong candidates arrive with product-level questions that show genuine research.