AI security consulting

Q: What drives the cost of an AI security consulting engagement?

Scope sets the price, and you can estimate yours from a few variables: how many AI systems and agents fall in scope, whether governance covers one team or several, which compliance regimes apply (EU AI Act, HIPAA, GDPR), and how much hands-on red teaming and in-code remediation the program includes. Our team reviews each variable with you on the first call and itemizes it in the proposal, and most engagements reach a documented strategy and a prioritized roadmap within 14 days.Run a quick estimate today: count the AI features that are live or close to launch and multiply by the teams that own them, because that number, more than any single model, drives the governance effort. If that list is hard to assemble, our team builds it with you as the first step, with nothing to prepare beforehand.

Q: How soon will our engineers have a direction they can act on?

You receive a documented threat model and a target architecture inside the first two weeks, and the governance model and full roadmap follow over the weeks after, scaled to how many systems and teams the program spans. Access sets the pace: the sooner your ML engineers and product owners join the working sessions, the sooner the decisions land.One check you can run this week: ask whether your teams can name every place an LLM or agent touches customer data, and where they can, discovery moves fast. Where that map is still scattered, our team assembles it with you at the start.

Q: How does AI security consulting differ from hands-on AI security delivery?

Consulting sets the direction: the threat model, the target architecture, the governance model, and the order the work runs in. Hands-on delivery is the execution that follows: red teaming your live models and agents, engineering the guardrails, closing findings in your codebase, and wiring evaluation checks into your pipeline. Most programs use both under one owner, and the consulting work scopes what the delivery work then builds.A short way to tell which you need now: if you can already name your top AI risks and want them fixed, delivery leads. If the risks themselves are still unclear, consulting comes first.

Q: We already run LLMs in production. Can you secure them without a rebuild?

Yes, we can secure what you already run. The engagement starts by mapping the live system: the models, the prompts and tools each agent can reach, and the data that moves through them. From there the threat model and the remediation sequence around the systems that stay online, and our engineers deliver the fixes as pull requests your team reviews, so the work reaches production on our side of the line.A rebuild enters the conversation only where an access model makes safe control impossible to express, and that case is rare. If you are not sure what your agents can currently reach, our team traces it with you before any change.

Q: Do we hire individual specialists or a full team for this work?

Both models work, and the scope decides which one earns its cost. For a focused threat model, an architecture review, or a compliance-readiness check, one or two senior AI security consultants cover it alongside your team for a few weeks. For a governance rollout across several teams, or continuous red teaming that runs beside feature delivery, a dedicated team carries the load through the roadmap.A signal for choosing: where a single audit or client deadline drives the work, a scoped engagement fits, and where AI features ship weekly across teams, standing coverage pays off faster. Most engagements start small and grow as the delivery work justifies it.

Q: Which regulations and standards does the work map to?

Your threat model and controls map to the frameworks your auditors and clients ask about: the EU AI Act risk tiers, the NIST AI Risk Management Framework, ISO/IEC 42001 for AI management systems, and the OWASP Top 10 for LLM Applications with MITRE ATLAS for the technical attack surface. Each finding and control arrives mapped to the named requirement it satisfies, so the output drops into an audit or a client questionnaire without translation.If you are unsure which regime applies to your use case, contact us and our team scopes it with you before any commitment.

Prompt injection, data leaking through a model, and agents acting with more access than intended sit outside what your existing security review was built to catch. Relevant Software gives you an AI security direction your engineers own: a threat model for LLMs, a target architecture, and a governance plan for the EU AI Act and NIST AI RMF.

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Our AI security consulting services

AI security strategy and architecture
AI security assessment and posture review
LLM and agent application security
AI governance, risk, and compliance
AI pipeline and MLOps security
AI red teaming and remediation

AI security strategy and architecture
AI security assessment and posture review
LLM and agent application security
AI governance, risk, and compliance
AI pipeline and MLOps security
AI red teaming and remediation

AI security strategy and architecture

Your AI systems earn one security direction before the next model ships. This engagement sets it: which models, datasets, and agents carry which risk, and the architecture your engineers build to across the data, model, and inference layers. For teams still building the models in-house, our AI software development team delivers that work, and this engagement secures what they build.

An AI system inventory with a risk rating for each model, dataset, and agent
A target security architecture spanning the data, model, and inference layers
A prioritized backlog your security and engineering leads agree on

AI security assessment and posture review

A posture review shows where your AI stands today against the attacks it will meet in production, and ranks the work so your team knows what to address first.

A gap analysis against the OWASP Top 10 for LLMs and MITRE ATLAS
Findings on data handling, access, and guardrails, ranked by business impact
A remediation plan sequenced against your release calendar

LLM and agent application security

Each LLM and agent feature meets the inputs a real user will send it, so the review covers the paths an attacker takes as well as the ones your product expects.

Prompt-injection, jailbreak, and system-prompt-leakage testing per feature
Tool and function-call permission review for every agent action
Output-handling and rate-limit fixes ranked by impact

AI governance, risk, and compliance

One governance model keeps every team shipping AI to the same standard, mapped to the regulations your industry answers to, from the EU AI Act to ISO/IEC 42001.

Risk classification and a control set mapped to the EU AI Act and NIST AI RMF
Model documentation, review gates, and a use-case approval workflow
Ownership, monitoring, and lifecycle rules for each model in production

AI pipeline and MLOps security

The pipelines your models train and run on carry provenance, validation, and access rules that hold under load. Our MLOps team builds and operates those pipelines, and the controls set here govern what any pipeline may carry.

Training-data provenance, validation, and poisoning-resistance controls
Model registry, artifact signing, and secrets-handling review
Access controls and audit logging across the model lifecycle

AI red teaming and remediation

Findings become working software in this block: our engineers red-team your live models and agents, then close each finding in your codebase. Where the delivery runs wide, our AI security services team carries the hands-on testing, guardrail engineering, and remediation across every model and agent.

An adversarial red-team campaign against your live models and agents
Fixes delivered as reviewed pull requests in your codebase
Guardrail and evaluation checks wired into your CI so the next release inherits them

Where AI security consulting helps

Before an AI feature ships
Your team has an AI feature working in a prototype and wants the security questions settled before real users arrive: how the system trusts prompts, what the model can reach, and where its output travels. AI security consulting answers those questions as a threat model and a control set your engineers approve, and our AI prototype to production services harden the feature so it launches with its guardrails already in place.
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When AI security questionnaires and audits arrive
Enterprise buyers and auditors now ask how your AI systems are secured. AI security consulting produces the threat model, the mapped controls, and the retest that answer them, and your engineers merge the fixes, so your team answers the questionnaire from evidence it can show.
Alongside an MLOps or data program
Models in production depend on pipelines that pull training data, run inference, and store artifacts and secrets. AI security consulting settles the provenance, validation, and access rules those pipelines enforce, so pipeline design starts from a stable specification and holds when a data source changes.

Your AI security posture is only as real as the code running in production. We put the guardrails there and hand you the evidence they hold.

What you walk away with

An AI security strategy document
Your AI system inventory, the risk rating for each model and agent, and the security direction your leads approve.
A threat model for each AI system
Attack paths mapped to the OWASP Top 10 for LLMs and MITRE ATLAS, from prompt injection to model extraction, ranked by impact.
A target security architecture
Controls for the data, model, and inference layers your engineers build to.
A governance and compliance operating model
Risk tiers, review gates, and ownership mapped to the EU AI Act, NIST AI RMF, and ISO/IEC 42001.
A sequenced delivery roadmap
Priorities, dependencies, and milestones your teams build against from week one.

What AI security consulting gives you

Enterprise deals clear AI security review
Your team answers client questionnaires about your AI from a report with the fixes already merged, so procurement moves at the pace of the rest of the deal.
Red teaming catches prompt injection early
Independent testing by Kroll found a prompt-injection vulnerability in 92% of the AI assessments it ran [stat source: Kroll, confirm before publish]. A threat model and a red-team pass put that class of issue on your backlog before a user finds it.
Audits arrive with evidence attached
EU AI Act, NIST AI RMF, and ISO/IEC 42001 reviews receive controls mapped to each named requirement, plus a retest that confirms the fix.
Delivery keeps its pace
Our engineers write the fixes as pull requests your team reviews, so the security work lands in code while your roadmap holds its schedule.

How an AI security consulting engagement works

A Scoped Discovery You Approve
Your models, agents, data flows, and compliance drivers go through a short discovery, and you leave it with a shared view of what the program protects.
A Documented AI Threat Model
Each system’s attack paths, from prompt injection to data leakage, land in a threat model your security and engineering leads sign off.
A Target Security Architecture on Paper
Controls for the data, model, and inference layers reach your engineers while changes stay cheap to make.
Merged Fixes and CI Guardrails
Findings close in your codebase as reviewed pull requests, and evaluation checks run in your pipeline on every release that follows.
A Handover Your Team Owns
Every decision, threat model, and control transfers to your team in a package they act on without us in the room.

Why choose Relevant for AI security consulting

Certified security and compliance

ISO 27001, GDPR, and HIPAA-aligned delivery. Security and compliance review runs from the first workshop, so your AI direction clears procurement on the first pass.

Coverage across the frameworks auditors use

EU AI Act, NIST AI RMF, ISO/IEC 42001, OWASP Top 10 for LLMs, and MITRE ATLAS. Your threat model and controls map to the standards your auditors and clients already work from.

One team advises and ships the fix

Findings closed by the engineers who found them. Our team delivers the remediation as pull requests in your codebase while your developers hold their roadmap.

AI-assisted delivery on every engagement

Relevant AI Delivery Framework™: up to 50% faster delivery and up to 20% fewer bugs through AI-assisted development, with every fix still passing senior code review.

A track record clients verify

200+ companies have built and scaled products with Relevant, and the case study below is the checkable subset.

The team that advises stays to ship

96% employee retention. The consultants who write your AI security direction are the same engineers who merge the remediation, so the context holds from the threat model through to the last pull request.

What clients say about Relevant

4.9 is our Clutch
average

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FAQ

What drives the cost of an AI security consulting engagement?

How soon will our engineers have a direction they can act on?

How does AI security consulting differ from hands-on AI security delivery?

We already run LLMs in production. Can you secure them without a rebuild?

Do we hire individual specialists or a full team for this work?

Which regulations and standards does the work map to?

Turn your AI security decisions into guardrails your code carries.

Our AI security consulting services

AI security strategy and architecture
AI security assessment and posture review
LLM and agent application security
AI governance, risk, and compliance
AI pipeline and MLOps security
AI red teaming and remediation

AI security strategy and architecture
AI security assessment and posture review
LLM and agent application security
AI governance, risk, and compliance
AI pipeline and MLOps security
AI red teaming and remediation

Why choose Relevant for AI security consulting

Certified security and compliance

ISO 27001, GDPR, and HIPAA-aligned delivery. Security and compliance review runs from the first workshop, so your AI direction clears procurement on the first pass.

Coverage across the frameworks auditors use

EU AI Act, NIST AI RMF, ISO/IEC 42001, OWASP Top 10 for LLMs, and MITRE ATLAS. Your threat model and controls map to the standards your auditors and clients already work from.

One team advises and ships the fix

Findings closed by the engineers who found them. Our team delivers the remediation as pull requests in your codebase while your developers hold their roadmap.

AI-assisted delivery on every engagement

Relevant AI Delivery Framework™: up to 50% faster delivery and up to 20% fewer bugs through AI-assisted development, with every fix still passing senior code review.

A track record clients verify

200+ companies have built and scaled products with Relevant, and the case study below is the checkable subset.

The team that advises stays to ship

Turn your AI security decisions into guardrails your code carries.

AI security consulting