Who is responsible for the use of AI in production? The mechanisms for managing AI following its implementation remain rather vague. This creates risks. CHI Software’s AI governance consulting services bridge that gap for businesses running AI at scale. We align policy with engineering realities, and ensure that the responsible implementation of AI can be effectively demonstrated during an audit.
Governance works when it lives inside the systems it governs. While market adoption of Chief AI Officer (CAIO) roles has surged from 26% to 76% in just one year, leadership visibility has outpaced operational readiness: only 21% of C-suite executives responsible for AI deployment report having mature agent governance in place. We provide consulting services for AI governance and tie each policy to a technical control, an owner, and a monitoring signal, drawing on CHI’s IT consulting, AI and generative AI development, security, data engineering, and MLOps practices. Each AI governance consulting service covers the ground most enterprises need.
Where is AI used, and where exactly does it pose risks? We need to start by answering these questions. Our AI advisory services and AI readiness assessment services can help with this. We carry out an inventory of the models, data flows, suppliers and decisions made by each system, and then classify the risks according to their level of impact. The output is a prioritised roadmap: what needs to be addressed first, who is responsible for it, and what control measures will minimise risk for every dollar invested.
If people do not understand where they fit within the AI governance framework, they cannot be expected to implement the technology effectively. Firstly, we define the operational model for your AI governance. We identify decision-making authorities, establish a procedure for approving new use cases, and set out escalation pathways. Our consultants work with your team to ensure that responsibility is assigned to specific individuals.
If only lawyers can understand the policies, no changes will ever happen. We develop standards that any engineer can understand. They are practical to implement because they are already integrated into your SDLC via our AI-powered SDLC services. Each policy corresponds to a specific control measure in the code, infrastructure or process.
Risk management in IT is not the same as risk management in the field of AI. Models change, training data becomes outdated – information is fleeting. The CHI Software team has implemented a system for the continuous monitoring of compliance. We incorporate testing of control measures, the collection of evidence and risk mitigation plans, all linked to specific systems and designated individuals.
Governance is not just about deployment. We are about more than that. CHI Software implements monitoring tools in the production environment to ensure that model behaviour, data quality and access remain under control even after launch.
Most engagements stall because nobody can say what AI is actually running. This five-day review answers that. We find the sanctioned platforms, the AI features hiding inside SaaS you already pay for, and the tools your teams adopted on their own, which is usually the largest group and almost always a surprise to the executive. Each system is classified for your role under the EU AI Act, provider, deployer, importer or distributor, and triaged by risk tier. You walk away with a short report of prioritised actions and a usage policy you can adopt the same week. Nothing is installed.
This is a different question for a different buyer. It asks whether you can survive an environment where a disclosed vulnerability gets exploited within a day. We measure how fast you actually patch across the whole estate, how complete your asset and attack-surface picture is, whether detection covers that compressed window, and how AI is used inside your own development pipeline. You get a scored control assessment, a risk register, and a board readout. Because it describes a threat rather than an obligation, it often sells faster than a compliance review, and it reaches the security budget the rest of the page does not.
Directors are being asked to attest to AI matters they were never briefed on, and under NIS2 national transpositions their personal exposure keeps rising. This half-day session covers what the regulation now demands of them, what your firm’s exposure looks like in plain terms, and the five questions they should be putting to management. It is the lowest-friction way in, and the most reliable route to a full readiness assessment.
The page already warns that too many vendors raise third-party risk. This closes that gap. Either we review one critical supplier’s AI practice against the CSA AI-CAIQ and hand you an assurance opinion you can put in front of a regulator, or we take your existing vendor register and flag which suppliers quietly introduced AI into your service path, typically with no notice and no contract change. Under NIS2 and the amended national rules this is now a supervisory expectation, not good practice. It attaches to the third-party risk programme you already run.
Governance tools do plenty, but a platform only helps if someone operates it, and most enterprises have nobody to spare for that. We run the governance platform on your behalf: continuous discovery of new AI across cloud, code and SaaS, a maintained inventory of models, agents and datasets, automated testing, runtime enforcement, evidence generation, and framework assessments. Above the platform sits retained senior oversight in three tiers, from regulatory horizon-scanning and a quarterly board note, to reviewing every new use case before it goes live, to chairing your AI governance forum outright. It is the honest answer to the question every buyer asks. Does it keep working after you leave? It keeps working because we keep running it.
Generative AI and autonomous agents are completely reshaping what risk looks like. Once an agent starts calling APIs, making edits in your systems, and taking action without a person double-checking every move, it needs a whole different level of guardrails than a simple Q&A chatbot. If you’re planning to ship these agents to production, your governance strategy must clearly define what they’re allowed to access – and what kicks in when they inevitably make a mistake.
If you ask a company who’s actually responsible for AI risk, you usually get crickets—or four different departments pointing fingers at each other. Without a dedicated leader or team driving policy, sign-offs, and incident response, every group ends up making up their own rules. That confusion is exactly how shadow AI, rogue data usage, and conflicting choices start sneaking into your organization.
Regulators require clear documentation on how you classify risks associated with artificial intelligence. Auditors are asking questions. If you feel you cannot answer them comprehensively, the pressure from regulators will intensify. Furthermore, corporate clients expect you to complete detailed questionnaires regarding your use of artificial intelligence.
It is still possible to manage a single model. However, twenty models from six suppliers across a dozen use cases present a management challenge. An excessive number of suppliers increases the risks associated with third-party companies and, without a well-thought-out structure, makes it virtually impossible to establish a unified view of artificial intelligence across the entire organization.
CHI Software as AI ethics and governance consulting firm introduce governance the way a CIO has to run it: alongside the business, not by pausing it. The approach follows an assess, design, implement flow, and it respects your Run versus Change reality by adding controls incrementally instead of demanding a standalone transformation.
Firstly, an analysis of the current situation. We identify which systems are in operation and what data they process. This gives us a clear and transparent baseline. It also enables us to identify several systems that nobody knew were in production.
We then develop control measures that align with your engineering strategy. High-risk systems are subject to more rigorous oversight. Together, we define the framework, policies and technical control measures to ensure that what is set out in the documentation corresponds to what is actually implemented on the production line.
Design is worthless unless it ships. We implement controls inside your existing tooling, wire up monitoring and audit trails, and hand your teams the runbooks to operate them. Teams that want engineers embedded on-site can add FDE as a service or hire FDE engineers to put governance into the codebase directly. Governance becomes part of how AI gets built and run, and it keeps working after we step back.
Assess, design and implement describe a project. Governance is not a project. As an AI governance consulting firm, once the controls are live, we can operate them for you instead of handing over a binder and walking away. That means continuous discovery as new AI appears, a live inventory rather than a document that goes stale, automated testing and runtime enforcement, and an evidence pack that stays current for the next audit. Senior oversight sits above the platform in three tiers, so a real person owns the regulatory horizon, reviews each new use case, or chairs your governance forum, depending on how much you want to keep in-house. The earlier promise on this page is that governance keeps working after we step back. The stronger version is simpler: it keeps working because we keep running it.
The EU AI Act applies in phases. Its transparency obligations became enforceable across the EU on 2 August 2026, with fines reaching 15 million euros or 3% of worldwide turnover. The Digital Omnibus, in force since 27 July 2026, moved most high-risk obligations to 2 December 2027 for standalone systems and 2 August 2028 for AI built into regulated products. We help you classify systems by risk tier and prepare the documentation each tier demands, before the deadline arrives rather than after.
ISO/IEC 42001, published in 2023, is the first international standard for an AI management system. It sits next to ISO 9001 for quality and ISO 27001 for security, and it gives you an auditable way to govern AI across its lifecycle. We help you align policies, roles, and controls to its structure, whether or not you pursue formal certification.
AI widens your attack surface: model endpoints, training data, prompts, and agent permissions all need protection. NIST’s Cyber AI Profile, released as a preliminary draft in December 2025, extends the NIST Cybersecurity Framework to AI systems. We use it, alongside your existing security program, to strengthen AI-specific controls and keep privacy obligations intact.
We are, first and foremost, engineers. Our governance recommendations have been drawn up by people who are involved in implementing artificial intelligence, so the control measures reflect how your systems actually work, rather than describing an idealised model that has never existed.
We can see the key point. The recommendation system and the credit model do not require the same level of control. Governance only works when it reflects the AI systems your teams actually use. We tailor our control measures to the actual risk posed by each system, so that efforts are directed towards where the risk is highest, whilst low-risk operations continue to run smoothly.
We’re with you every step of the way. From assessment to design, implementation and monitoring — a single team supports you throughout the entire process. This continuity means that the very same people who designed your architecture will also integrate it into the production environment and ensure its stability.The objective is not a static policy library, but repeatable, auditable control across every production AI system.
CHI Software as an AI governance consulting company brings together artificial intelligence development, cloud technologies, data engineering, DevOps, MLOps, security and legacy software modernization under one roof, in accordance with the ISO 9001 and ISO 27001 standards. As a consultancy specializing in ethics and AI governance, with a proven track record in project delivery, we ensure the effective implementation of governance mechanisms without unnecessarily expanding the supplier base.
A Forward Deployed Engineer turned a complex healthcare data challenge into prioritized use cases, a FHIR-based architecture, and an implementation roadmap for 30M clinical records without replacing the existing MIS.
The US-based multinational company with offices in the United States, Poland, Ukraine, and Georgia. The company offers technology consulting, data services, advanced analytics, automated and manual QA, commercial video processing,
We kicked off a project to create an AI assistant to make the client's ERP system even better. Our goal was to develop a text-based Q&A AI assistant that users
AI governance consultancy – services that help organizations determine who is responsible for AI-related risks, what policies are in place, and how oversight is ensured throughout the entire AI lifecycle. High-quality AI governance consultancy services link these solutions to real-world systems, ensuring that governance is put into practice rather than remaining merely on paper.
The working framework covers issues relating to ownership and decision-making rights, policies on acceptable use and data processing, a method for classifying risks by level, rules on human oversight, documentation standards and monitoring in the production environment. It also specifies the individuals responsible for each section. Without designated responsible individuals, the rest is merely a document.
It depends on where you operate, your industry, and what your AI decides. An enterprise AI governance consulting engagement starts by mapping your systems to relevant rules, from the EU AI Act to sector requirements, then flags where you’re exposed. We’re an engineering partner, not your legal counsel, so we work alongside your lawyers on interpretation.
Data governance ensures the quality of data and control over access to it. AI security protects systems from attacks. AI governance oversees both of these components: it determines which uses of AI are acceptable, how models are approved, and who is accountable for the results. These three components overlap, and a consultancy specialising in AI governance must integrate them, rather than treating each one in isolation.
Hire AI governance consultants when AI has spread past what informal oversight can handle: multiple models in production, unclear ownership, or auditors and customers asking questions you can’t yet answer. If you’re weighing whether to hire consultants for AI governance framework development or build the capability in-house, CHI Software can stand up the structure fast and transfer it to your team. Each service we run is built to leave you more self-sufficient, not more dependent.
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