AI Risk Assessment Services
Your production AI works. But when an auditor, an enterprise customer, or a regulator asks for a defensible record of what could go wrong, no one on your team can produce one. That's what enterprise AI risk assessment services are for.
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TRUSTED BY ENTERPRISES
AI Risk Assessment Services give organizations a defensible, board-ready view of what could go wrong across their production AI, benchmarked against the NIST AI RMF, the US government's standard for managing AI risk, plus the OWASP Top 10 for LLMs and ISO/IEC 42001. Kodexo Labs built these systems first, shipping HIPAA-compliant and self-hosted production AI, then assesses them with that same engineering discipline.
Our Core Capabilities
Trace how sensitive data moves through every model, with self-hosted options.
Test each model for bias and explain, in plain language, the decisions it makes.
Threat-model every system and stress-test it against real production load.
Map each model to NIST AI RMF, the OWASP LLM Top 10, and ISO/IEC 42001, so the answer is already documented when your auditor asks.
Monitor production drift and hand your board a living risk register.
Get findings from the engineers who ship production AI, not checklist auditors.
IN THE NEWS
AI-powered products across 25+ industries
Earned Top-Rated Reviews on Clutch
Client retention across long-term engagements
Expert Team global offices across the US, UK, Canada
The Risk Domains We Assess Across Your Production AI
5 risk domains, one standard: an assessment run by engineers who have shipped production AI, not read about it.
Data Privacy & Security
Sensitive data flows into your models every day, and most teams can't show where it goes once it does. We map the full data lineage first. Then we verify controls against the NIST AI RMF, the US standard for managing AI risk, and where data can't leave your network, we assess inside it.
Data lineage mapping across every model and pipeline.
Self-hosted, VPC-scoped assessment when data can't leave your environment.
Do you actually know everything running in your AI estate right now?
Most teams don't, and that gap is the first thing an assessment surfaces. Before the auditor asks, let's map what's really in production and where the risk sits.
Roadmaps That Shipped Results
Diesel Laptops
Fleet technicians lost minutes on every job hunting through 160,000 parts records. Solution: Kodexo Labs built an AI parts-lookup running inside their own self-hosted AWS VPC, keeping all data on their private cloud. Outcome: lookup time fell 85%, so a technician finds the right part in seconds, not minutes, on every daily job.
85%
Faster Lookup
160,000
Records Searched
Inc. 5000
Client
Extensiv
This $130M-funded logistics platform had operations staff waiting on engineers for every data question spread across 4 databases. Solution: Kodexo Labs built a LangGraph agentic system that reads plain-English questions and answers them directly. Outcome: staff now query 207 tables themselves, at over 90% accuracy, with answers arriving in seconds instead of days.
90%+
Accuracy
207
Tables
$130M+
Funded (Hg Capital)
Therapy Talk
Therapy Talk needed GDPR built into a mental-health platform, not patched in after launch when the data is already exposed. We architected it in from day one. The privacy controls a risk assessment looks for were part of the design, not a later fix. Today the platform serves 1,923 users at 93% accuracy, GDPR-compliant throughout.
1,923
Users
93%
Accuracy
99.9%
uptime
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- HIPAA-compliant AI auditPatient-data risk mappingClinical model bias reviewProven on SmartMedHx
AI Risk Assessment Across 7 Industries
Every industry carries its own regulator, its own data it can't afford to leak, and its own way an AI system fails quietly. Here is where our assessments already hold up.
When your board asks for a risk register, will a checkbox PDF hold up?
By the time directors ask, a document that was accurate for one afternoon won't survive the questions. You need a living record your board can approve and your engineers can act on. That's what we hand you. Let's start with what's actually running in production today.
Frameworks and Standards We Assess Against
Your auditor and your enterprise customer already have a list of standards they expect answers on. Guessing which ones apply is how a contract stalls. We assess each production system against the frameworks below and document exactly where it stands, so the answer exists before anyone asks for it.
EU AI Act
HIPAA
PCI-DSS
GDPR
CCPA
COPPA
SOC TYPE 2
ISO 27001
NIST AI RMF
FERPA
EU AI Act
HIPAA
PCI-DSS
GDPR
CCPA
COPPA
SOC TYPE 2
ISO 27001
NIST AI RMF
FERPA
Why Enterprises Trust Kodexo Labs to Assess Their AI
Most firms selling AI risk assessments have never shipped a production AI system themselves. We have. That's the difference between an assessor flagging theoretical risks and one who has already had to engineer around them.
Assessed By Production Builders
Your posture is judged by firms who never shipped AI. We did. We built SmartMedHx, a HIPAA-compliant system in production with 42+ providers and 493 patient interviews. We check for those same risks.
Self-Hosted Inside Your Network
Your data should not leave your network just to be assessed. We built Diesel Laptops' AI search to run self-hosted inside their AWS VPC, at Inc. 5000 scale. We assess the same way.
We Build Agentic Systems
Auditors can flag agentic risk but not fix it. We build these systems. For IFPG, we shipped a reasoning agent across 1,000+ franchise listings, lifting accuracy 85%. We audit what we have built.
Two-Week Sprints, Live Register
A generic checklist tells you what a template fears, not what your systems do. We scope in two-week sprints, each one ending with a working, updated risk register. You see real findings early.
Most AI risk reports end at a list of findings nobody can act on.
Ours doesn't. Before we assess a single model, we plan for what happens after: a prioritized, buildable roadmap your engineers can start on the day we hand it over.
Recognised By The Platforms That Vet AI Companies
Kodexo Labs is reviewed where technical buyers do their diligence: Clutch and Upwork. Every badge below links to the live profile.
Overcoming AI Risk Assessment Challenges
Most AI risk assessments fail in one of 4 predictable ways: they capture a single moment and call it done, they tick framework boxes without testing anything, they come from people who have never shipped the systems they are judging, or they only cover the AI someone remembered to declare. Here is how we close each gap.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
Our 5-Phase AI Risk Assessment Methodology
Every engagement runs in two-week sprints, so you see a working deliverable at each phase rather than waiting months for one report.
AI Inventory & Classification
You cannot assess what you cannot see, and most teams have more models running than they can name. We catalog every model, agent, and data pipeline in production, then rank each by risk tier, so the highest-stakes systems get looked at first.
Threat Modeling
Once we know what is running, we map how each system could be attacked or fail. We build a threat model per system, mapped to the OWASP Top 10 for LLMs, so the ways your models could be manipulated are documented before an attacker finds them.
Algorithmic & Bias Impact Assessment
A model can discriminate or make unexplainable calls long before anyone notices. We test each one for bias across protected groups and run an explainability review, so you can show a regulator exactly why a given decision was made, or fix it.
Compliance Checking
Now we score each system, framework by framework, against the NIST AI RMF, the OWASP Top 10 for LLMs, and ISO/IEC 42001, the international management standard for AI systems. You get a gap analysis with audit-ready documentation, not a verbal assurance.
Remediation & Continuous Monitoring
Findings mean little without a fix. We hand engineering a prioritized remediation roadmap and stand up a drift-monitoring dashboard, so the living risk register your board approves keeps reflecting what your models actually do in production.
AI Risk Reading Worth Your Time Before You Start
Top 15 Artificial Intelligence Applications List 2026
June 2026 · By Kodexo Labs
A guide to the top 15 AI applications of 2026, covering AI industrial applications and the best open-source artificial intelligence tools across industries.
What is Neural Network – The Future of AI in Businesses Defined
January 2024 · By Kodexo Labs
Neural networks are a core component of AI and deep learning, enabling machines to process data, recognize patterns and make decisions.
Agentic AI Applications, Benefits and Challenges in Healthcare
August 2025 · By Kodexo Labs
A comprehensive guide to agentic AI applications in healthcare for 2025, covering benefits, challenges, technical infrastructure, leading platforms, and implementation best practices.
AI Risk Assessment Services: Frequently Asked Questions
Still Weighing Your AI Risk Exposure? Tell us what's running in production and we'll show you where the real risk sits.
An AI risk assessment is a structured review of everything that could go wrong across an organization's production AI, from data leakage and biased outputs to model drift and regulatory gaps. Kodexo Labs benchmarks each model and agent against the NIST AI RMF, the US government's standard for managing AI risk, then documents exactly where every system stands. The result is a defensible, board-ready record that auditors, enterprise customers, and regulators recognize.