What AI Security Engineers Build and Why Engineering Teams Need Them Now
An AI security engineer protects the software a company builds and the AI systems it now ships inside it. The role spans application security (flaws in code and APIs), cloud security (identity, network and data controls across AWS, Azure and Kubernetes) and security engineering (the tooling and pipelines that make secure delivery repeatable). What has changed is the workload: product teams are embedding LLMs and agents into customer journeys, and those systems create attack paths traditional controls do not cover.
The AI-specific problem is measured, not theoretical. In IBM's 2025 Cost of a Data Breach Report, 13% of surveyed organisations had already experienced an attack that affected their AI models or applications, and 97% of those organisations said they lacked proper AI access controls. The same report found 63% had no AI governance policy at all, and that a high level of shadow AI added USD 670,000 to a breach against a global average cost of USD 4.44 million. An AI security engineer closes those gaps: who can call which model, with which data, and what it may do in return.
The skills are scarce. The ISC2 2025 Cybersecurity Workforce Study found 95% of security teams report at least one skills gap, with AI cited by 41% of respondents as the most pressing need and cloud security by 36%.
What the role delivers. Threat models for new features, including agent tool calls and retrieval pipelines. Guardrails against prompt injection and excessive agency, mapped to the OWASP Top 10 for LLM Applications. Signed build and model provenance. Least-privilege cloud identity. Security tests in CI rather than a quarterly penetration test. Scrums.com forward-deploys AI-certified security engineers into your repositories and cloud accounts, managed through the Scrums.com Enterprise AI Platform for Software Engineering, with a shortlist in 48 hours and a first commit inside three weeks.
Essential Skills to Look For in an AI Security Engineer
Screen for the mix below and weight it towards the systems you run.
Application security fundamentals. Threat modelling against real architectures, secure code review in the languages your teams use (Python, Go, Rust, TypeScript, Java), and fluency with the OWASP Application Security Verification Standard as a checklist for what "done" means. Candidates should tune SAST, DAST and software-composition tools so developers get a short, accurate finding list, and write the fix, not just the ticket.
Cloud and platform security. IAM design across AWS and Azure, including short-lived credentials and permission boundaries. Kubernetes hardening: admission control, network policies, pod security standards and workload identity. Infrastructure as code in Terraform with policy-as-code checks using Open Policy Agent or equivalent, benchmarked against CIS Benchmarks. Detection engineering that turns cloud audit logs into alerts someone will act on.
Secure delivery and supply chain. Secrets management with rotation, signed commits and artefacts using Sigstore, build provenance to SLSA levels, and SBOM generation. AI supply chain lives here too: pinning model versions, verifying third-party model and dataset provenance, and scanning fine-tuning data.
The AI-era additions. Working knowledge of the OWASP Top 10 for LLM Applications (2025): prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption. Practical defences: input and output validation, tool allow-lists and per-agent identities, human approval for high-impact actions, rate and cost limits, retrieval-store access control. Familiarity with MITRE ATLAS for adversarial ML techniques and the NIST AI Risk Management Framework for governance mapping.
Working with AI, not only securing it. Good candidates use AI-assisted tooling for triage and test generation, and can explain where they verify by hand.
Where AI Security Engineers Deliver Measurable ROI
Security spend is easiest to justify where a control removes a concrete cost: a failed audit, a delayed launch, a breach, or a team blocked behind manual review.
FinTech and banking. Regulated institutions must show operational resilience and third-party risk control under regimes such as DORA and PCI DSS while shipping AI features for onboarding, fraud detection and customer service. An AI security engineer builds the evidence into the pipeline: every release carries signed provenance, every model in production is inventoried, and every agent action is logged with the identity that authorised it. Launches that waited on a security sign-off ship on the sprint cadence because the controls are automated. The IBM 2025 report puts the global average breach at USD 4.44 million; avoiding a single incident in a payments system covers the engagement many times over.
Insurance. Claims and underwriting teams are deploying LLM agents that read documents and draft decisions. The exposure is excessive agency and data leakage: an agent that can read any claim file, or one that can be steered by text inside a submitted document. Scoped retrieval, output validation and approval gates on any action that changes a policy or a payment are the difference between a pilot and a production system.
SaaS. Enterprise buyers ask for SOC 2 or ISO 27001 and, increasingly, for an AI usage policy and a tenant-isolation story for LLM features. Engineers who can answer security questionnaires with working controls rather than roadmap promises shorten sales cycles.
Public sector. Government and public bodies face zero-trust mandates, strict data-residency rules and procurement requirements for AI transparency. Security engineers who can implement identity-aware access, document AI risk under the NIST AI RMF and produce audit-ready evidence make digital-service teams eligible for work they would otherwise fail on compliance grounds.
AppSec Engineer vs Cloud Security Engineer vs Security Engineer vs DevSecOps: Which One Do You Need?
These four titles overlap. Pick the emphasis from the primary risk you want to reduce, then hire adjacent skills as secondary.
Application security engineer. Focused on the code and APIs your teams write. Threat models features, reviews pull requests, tunes static and dynamic analysis and coaches developers on secure patterns. In the AI era this role owns prompt-injection defences, output handling and the security of any LLM feature in the product. Choose this when your main exposure is a large or fast-moving codebase.
Cloud security engineer. Focused on the platform the code runs on. Designs IAM, network segmentation, encryption and key management across AWS, Azure and Kubernetes, and codifies guardrails in Terraform. In the AI era this role governs where models run, which data stores they reach and how agent workloads authenticate. Choose this for a multi-account estate, a Kubernetes platform, or a cloud migration in progress.
Security engineer (generalist). Builds and runs the security tooling itself: identity provider integration, secrets management, logging pipelines, detection rules, vulnerability management and incident response automation. Often the first security hire in a scaling company. Choose this when you have no security engineering function and need one person who covers the basics well.
DevSecOps engineer. Sits inside the delivery pipeline. Integrates scanning, signing, provenance and policy checks into CI/CD and measures fix times. Owns model and dataset supply-chain checks for AI features. Choose this when you have security expertise but findings never become production fixes.
Choosing in practice. A team shipping an LLM agent on a shared Kubernetes platform usually needs an AppSec lead first and a cloud security engineer second. A regulated company mid-migration needs the reverse. Scrums.com matches on the mix, not a single title.
What AI Security Engineers Cost: US, UK and Africa Benchmarks
Security engineering pay sits above general software engineering in every market; the AI premium is on top.
United States. ZipRecruiter data as of June 2026 puts the average application security engineer salary at USD 138,117, with the 25th percentile at USD 117,500, the 75th at USD 157,000 and the 90th at USD 184,000. KORE1's 2026 security engineer salary guide reports cloud security engineers at USD 140,000 to 210,000 and application security engineers at USD 135,000 to 200,000. Arc lists freelance security engineer rates of USD 60 to 100 or more per hour.
United Kingdom. Glassdoor data for London (April 2026) shows an average cloud security engineer salary of GBP 69,204, with a typical range of GBP 57,849 to 83,045 and top earners at GBP 98,172. Across the UK, Coursera's 2026 guide, citing Glassdoor, puts the average security engineer at GBP 50,000 base, cloud security engineers at GBP 62,000 and principal security engineers at GBP 87,000.
Africa. PayScale data for South Africa (May 2026) reports an average cyber security engineer salary of ZAR 298,326, with entry-level roles around ZAR 216,000 and late-career roles at ZAR 490,000 or more. PayScale data for Nigeria shows an average of NGN 982,635 from 37 profiles. Senior engineers in Johannesburg, Cape Town, Lagos and Nairobi with cloud and AI security experience earn above those averages, and still cost a fraction of a US or UK hire.
The full cost of hiring directly. Add recruiter fees, a months-long hiring cycle for specialist roles, payroll overhead, and retention risk in a market where the ISC2 study finds 88% of organisations have already suffered a consequence from a skills shortage. Scrums.com offers a managed alternative: an AI-certified security engineer, forward-deployed into your systems and run through the Scrums.com platform with delivery oversight included, on one predictable engagement fee. Start a conversation to scope the engagement.
How AI Security Engineers Work Inside a Forward-Deployed, Platform-Managed Team
A security engineer outside the delivery team produces reports. One inside it produces fixes. The Scrums.com model is built for the second outcome.
Weeks one to three: access, threat model, first commit. The engineer is onboarded into your repositories, CI and cloud accounts under your identity provider, with access scoped by your policy. The first deliverable is a threat model of the system that triggered the hire, followed by a first merged change: a pipeline check, a policy, an IAM fix.
The paved road. Rather than gating every release by hand, the engineer builds secure defaults developers pick up automatically: pipeline templates with scanning and signing, Terraform modules with guardrails baked in, a reference pattern for calling LLMs that includes input validation, output handling, tool allow-lists and cost limits. Teams on the paved road get security by default; deviations are visible and reviewed.
Guardrails for AI agents. Every agent runs under its own identity with an explicit set of tools and data sources. High-impact actions require a human approval step. Egress is controlled so a compromised prompt cannot turn into data exfiltration. Every action is traced to a request and a model version, which is what auditors ask for.
Platform management. The Scrums.com Enterprise AI Platform for Software Engineering tracks the engineer's work as a normal delivery stream: backlog, throughput, findings opened and closed, time to remediate by severity. AI-assisted triage in the platform helps classify scanner output and draft fixes; the engineer is accountable for what merges. You see the same dashboard the delivery lead sees.
Working with your people. The engineer runs a security-champions loop, pairs on fixes rather than handing back tickets, and leaves runbooks and policy code your team owns. Engagements scale from one embedded engineer to a squad covering AppSec, cloud and DevSecOps across US, UK and Africa time zones.
Evaluating AI Security Engineer Talent: Signals, Tasks and Red Flags
Certifications and tool names on a CV are weak evidence. The strongest signal is a candidate describing a system they secured, a decision made under a constraint, and what they measured afterwards.
Interview signals. Ask for a walk-through of a threat model they produced. Ask how they cut false positives from a static-analysis tool and what happened to the fix rate. Ask them to explain prompt injection to a product manager, then why output validation and tool scoping matter more than prompt wording. Ask how they would give an LLM agent access to a ticketing system without access to every ticket. Strong candidates answer with identities, scopes and approval gates; weak ones answer with a system prompt.
Take-home tasks that work. A short review of a Terraform module and a small agent service whose tool calls an internal API. The candidate should find the over-privileged role, the unvalidated model output reaching a shell or SQL call, the injection path through retrieved documents, and the missing rate limit. Ask for fixes as a pull request. Two to three hours is enough.
Red flags to watch for:
- Talks only about scanners and never about fixing code or infrastructure
- Cannot name a single item from the OWASP LLM Top 10 or describe a defence for it
- Treats the system prompt as a security boundary
- No IAM design experience beyond built-in admin roles
Practical interview questions. Design the access model for an agent that reads customer records and drafts replies. How would you detect a poisoned fine-tuning dataset? What goes into CI first for a team with no security tooling and a release next week? Which findings block a merge?
Scrums.com screens every security engineer on these competencies before shortlisting, with a technical assessment matched to your stack. Start a conversation to review profiles.