Trusted AI Safety and cyber for organisations that cannot afford to get it wrong.
I am Arinze Okosieme, a senior cyber and AI safety professional. I help organisations adopt AI securely, responsibly and in line with emerging UK and international expectations.
What I help you do
I turn high level AI safety principles into practical controls, patterns and assurance that work in your environment without blocking delivery.
AI safety and risk governance
Frameworks and guardrails so your AI portfolio is safe, controllable and aligned with organisational risk appetite.
- AI risk frameworks tailored to your business
- Risk cards and safety cases for high impact use cases
- Human in the loop oversight models
- Alignment with recognised AI governance standards
Secure AI and Zero Trust architectures
Design and review of AI enabled systems that respect Zero Trust principles and modern security practice.
- Threat models and attack paths for AI services
- Identity, access and segmentation for AI workloads
- Secure integration of LLMs and AI APIs
- Hardening of data pipelines and inference environments
AI system assurance and testing
Independent assessment of AI models and applications so that boards, regulators and customers can trust the outcome.
- Safety and security reviews for AI use cases
- Data, training and access control assessments
- Adversarial and misuse scenarios for LLMs
- Pre deployment and change impact assurance
What makes this approach different
Many AI specialists lack security depth. Many security teams are new to AI. I live in the overlap so you do not have to choose.
Dual focus on AI safety and cyber security
I combine formal AI safety training with decades of security architecture and assurance work. This allows me to connect model behaviour, data, infrastructure and governance in one view.
Zero Trust and unstructured data expertise
My Zero Trust and cloud experience, alongside deep work with unstructured data, is particularly relevant for modern AI, which often relies on documents, messages and files.
Practical and regulator friendly
My work is grounded in what risk teams, auditors and regulators actually expect to see. You receive artefacts that stand up to scrutiny and support real decisions.
Credentials at a glance
I am a practitioner first. Certifications support the experience rather than replace it.
Where I add immediate value
These examples show the type of work I do. Each engagement is tailored to the organisation, its risk appetite and its stage of AI adoption.
AI risk framework for a regulated enterprise
A large organisation needed a coherent way to understand and govern AI risk across multiple business units.
- Defined an AI risk taxonomy and governance model
- Mapped AI controls into existing security and risk frameworks
- Created AI risk cards for priority use cases
- Designed human oversight and escalation paths
Secure LLM adoption for sensitive data
A team wanted to use large language models with internal documents without creating a data breach risk.
- Assessed data, threat and regulatory constraints
- Designed safe patterns for retrieval augmented generation
- Recommended encryption and access controls around knowledge stores
- Defined operational monitoring and incident paths
Zero Trust architecture for AI driven services
An organisation wanted to align its AI platform with Zero Trust principles without blocking delivery.
- Mapped current environment and trust boundaries
- Defined a target architecture for AI services
- Integrated identity, device, network and data controls
- Created a staged roadmap that matched delivery cadence
AI safety training for security and data teams
Security teams and data scientists needed a common language for AI risk and safety so they could work together.
- Delivered workshops on AI threats, controls and failure modes
- Introduced a shared set of patterns and anti patterns
- Embedded AI aware checks into existing pipelines and processes
- Provided reference materials and templates for ongoing use
Who I work with and how
I typically support organisations that treat AI as safety and mission critical. That often means regulated or high impact environments.
Sectors that benefit most
- Critical national infrastructure and utilities
- Government and public sector bodies
- Health and life sciences
- Financial services, insurance and fintech
- Technology vendors and AI start ups
Ways of working
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1Discovery and strategyShort, focused work to understand your AI landscape, risk drivers and constraints.
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2Architecture and designCo designing secure and safe AI patterns and reference architectures with your teams.
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3Assurance and reviewIndependent assessment of AI systems, suppliers and changes at key decision points.
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4Ongoing advisoryRetained support for boards, CISOs, AI leads and risk teams as your AI portfolio grows.
AI and unstructured data safety self assessment
In a few minutes you can get an independent view of how well your organisation is protecting unstructured data and AI enabled workflows. The assessment runs in the browser and produces a report you can share with security, risk and leadership teams.
What you will get
The Unstructured Data Security and AI Safety Assessment (UDSA) provides a structured view of your current posture and highlights where to focus next.
- Coverage across data discovery, access control, protection and governance
- Specific focus on how AI and unstructured data interact in your environment
- Domain scores and clear explanations of key risks
- Prioritised recommendations you can act on quickly
Run the assessment
The assessment normally takes around 10 to 15 minutes to complete. It is designed for security, data and risk leaders who need a practical starting point.
I created the UDSA engine to help organisations understand how AI and unstructured data introduce new safety, security and compliance risks before they become incidents.
Start a confidential conversation
If you are planning a significant AI initiative or need independent assurance on existing systems, I am happy to explore how I can help. There is no obligation.