AI risk management starts with principles, frameworks, and standards. What organisations need next is a practical way to apply them to real AI systems. AIQURIS provides a structured methodology that connects AI use cases to real-world impact, risk, and control. It is designed to support confident deployment decisions, grounded in evidence and aligned with how AI is actually built and used.
AI risk management starts with principles, frameworks, and standards.
What organisations need next is a practical way to apply them to real AI systems. AIQURIS provides a structured methodology that connects AI use cases to real-world impact, risk, and control.
It is designed to support confident deployment decisions, grounded in evidence and aligned with how AI is actually built and used.
The problem isn’t a lack of guidance. The problem is that most approaches don’t translate concrete AI use cases into operational risk and executable controls in a consistent way.
The problem isn’t a lack of guidance.
The problem is that most approaches don’t translate concrete AI use cases into operational risk and executable controls in a consistent way.
AI risk is not theoretical. It comes from real systems, used by real people, in real environments.
The AIQURIS methodology starts with the AI use case and moves through impact, risk, and control. For each use case, we identify concrete risks across safety, security, legal and compliance, performance, ethics, and sustainability. These risks are then linked directly to governance, process, and technical controls.
Regulatory requirements, industry standards, and internal policies are translated into clear, executable requirements that apply inside AI systems. This enables consistent and repeatable AI risk management across real-world deployments, without adding unnecessary overhead.
We define the AI application, its purpose, and the operating conditions that shape its risk profile, such as decision automation, customer-facing use, or regulated environments.
We identify who or what is affected, how they are impacted, and the potential severity of harm, including impact on individuals, business operations, or regulatory exposure.
We determine the risk level for the specific use case based on impact severity and operational context. This results in a clear classification, such as low, medium, or high risk, depending on deployment conditions.
We translate regulatory obligations, standards, and internal policies into clear requirements. These are mapped to governance, process, and technical controls that address the identified risks, such as oversight, monitoring, and access controls.
We assess how well the requirements are implemented, identify gaps, and produce evidence to support deployment and assurance decisions. This gives a clear view of residual risk, including control status, documentation, and audit-ready artefacts.
The result? AI risk becomes concrete, measurable, and controllable, with a clear basis for assurance.
The result? AI risk becomes concrete, measurable, and controllable.
AI risk is not theoretical. It comes from real systems, used by real people, in real environments.
The AIQURIS methodology starts with the AI use case and moves through impact, risk, and control. For each use case, we identify concrete risks across safety, security, legal and compliance, performance, ethics, and sustainability. These risks are then linked directly to governance, process, and technical controls.
Regulatory requirements, industry standards, and internal policies are translated into clear, executable requirements that apply inside AI systems. This enables consistent and repeatable AI risk management across real-world deployments, without adding unnecessary overhead.
We define the AI application, its purpose, and the operating conditions that shape its risk profile, such as decision automation, customer-facing use, or regulated environments.
We identify who or what is affected, how they are impacted, and the potential severity of harm, including impact on individuals, business operations, or regulatory exposure.
We determine the risk level for the specific use case based on impact severity and operational context. This results in a clear classification, such as low, medium, or high risk, depending on deployment conditions.
We translate regulatory obligations, standards, and internal policies into clear requirements. These are mapped to governance, process, and technical controls that address the identified risks, such as oversight, monitoring, and access controls.
We assess how well the requirements are implemented, identify gaps, and produce evidence to support deployment and assurance decisions. This gives a clear view of residual risk, including control status, documentation, and audit-ready artefacts.
The result? AI risk becomes concrete, measurable, and controllable, with a clear basis for assurance.
AI risk management fails when activities are fragmented.
AIQURIS connects the full chain of decision-making into a single operational flow. From defining the use case to producing evidence that supports executive decisions and assurance.
We bring together AI use cases, real-world impacts, risk assessment, technical and organisational controls, and evidence into one coherent process. This allows teams to focus on what matters, close gaps efficiently, and manage residual risk within the organisation’s risk appetite.
Use case, purpose, and operational conditions.
Affected domains and severity of harm.
Use-case specific risk characterisation.
Governance, process, and technical requirements.
Implementation gaps, residual risk, and evidence.
AI risk management fails when activities are fragmented.
AIQURIS connects the full chain of decision-making into a single operational flow. From defining the use case to producing evidence that supports executive decisions and assurance.
We bring together AI use cases, real-world impacts, risk assessment, technical and organisational controls, and evidence into one coherent process. This allows teams to focus on what matters, close gaps efficiently, and manage residual risk within the organisation’s risk appetite.
Use case, purpose, and operational conditions.
Affected domains and severity of harm.
Use-case specific risk characterisation.
Governance, process, and technical requirements.
Implementation gaps, residual risk, and evidence.
You don’t need to replace what you already use.
Our platform integrates your regulatory obligations, standards,
and internal frameworks into one coherent structure, turning guidance
into action at the use-case level.
Testing alone is never enough. It shows what can be measured, but not whether results are acceptable in context. AIQURIS defines what to test, who should run the tests, and how results should be interpreted for each AI use case.
We work with trusted testing partners, so organisations are not left navigating fragmented tools or unclear outcomes. By linking frameworks, testing, and context, we deliver actionable AI assurance.
You don’t need to replace what you already use.
Our platform integrates your regulatory obligations, standards,
and internal frameworks into one coherent structure, turning guidance
into action at the use-case level.
Testing alone is never enough. It shows what can be measured, but not whether results are acceptable in context. AIQURIS defines what to test, who should run the tests, and how results should be interpreted for each AI use case.
We work with trusted testing partners, so organisations are not left navigating fragmented tools or unclear outcomes. By linking frameworks, testing, and context, we deliver actionable AI assurance.
If your AI governance stops at frameworks, this is how to make it operational across real deployments. Ask us how we can assist you.
