Responsible AI
Deliver secure and safe AI with our responsible AI advisory services.
23% of organisations using AI have experienced negative consequences due to inaccurate outputs.
Flame Tree helps you avoid those negative consequences with AI governance frameworks to manage risk, ensure regulatory compliance, and strengthen trust.
Responsible AI includes fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness.
Stay ahead of risk with our tailored monthly plan
Flame Tree provides tailored monthly plans to suit your organisation’s unique needs. Talk to us about our full range of AI services and cybersecurity solutions that ensure your systems are always prepared, always protected, and always resilient.
Secure and safe AI advisory tailored to your organisation
We help organisations set guardrails for AI use across the full lifecycle. This includes intake, design, build, deployment, and change control. Controls are aligned to risk appetite and operating context.
We develop AI strategy and a practical roadmap for delivery. We also support use case selection, so effort goes to work with clear benefit. It keeps teams aligned.
We assess and manage threats and risks across AI and data. This includes common issues such as bias, data leakage, prompt injection, misinformation, and misuse. Risks are translated into controls and ownership.
What We Do
Position your organisation for AI success
Flame Tree provides responsible AI advisory services that support safe delivery. We review your environment, capability, and decision pathways. The output is a plan that can be delivered.
Our work connects governance to delivery, so controls are built in from the start. We identify threats using MITRE ATLAS and risks using the MIT AI Risk Repository. Controls are selected from ISO 42001, OWASP, and related libraries.
We work with leadership and delivery teams to keep accountability clear. We align AI goals with business priorities and risk appetite. This supports trust, auditability, and safer scaling.
We partner with local universities to deliver research into generative AI, including quantification of error rates.
Use case identification and policy
We work with you to identify where AI can create advantage and which uses are appropriate. We define permitted use cases, decision owners, and approval steps. This gives teams clarity on what is in scope.
We deliver an AI policy that sets allowed use, prohibited use, and required controls. It covers data handling, tool access, and escalation paths. Your team can use AI with clear boundaries.
Threat and risk identification
We identify AI threats using MITRE ATLAS and map realistic attack paths. We identify and prioritise risks using the MIT AI Risk Repository, aligned to your context and risk appetite. Outputs are clear, owned, and ready for delivery.
Controls and governance design
We design governance and controls using ISO 42001, OWASP, and related libraries. We map controls to ISO 27001 where it supports your existing security environment. Documentation and evidence requirements are defined so assurance is practical.
Implementation support and coordination
We support implementation of the agreed controls and initiatives across teams and vendors. This includes delivery planning, sequencing, project management, and coordination for tools and process change. Progress is tracked against outcomes and evidence, not activity.
AI education, assurance, and continual improvement
Our post-implementation AI assessment and performance reviews We deliver AI education for executives, risk teams, and delivery teams. Education covers permitted use, safe handling of data, and how to apply controls in daily work. We run assurance reviews to confirm control efficacy, then support ongoing uplift.
Our Accreditations and Certifications
Unlock the power of responsible AI
Partner with Flame Tree to build safer, smarter systems through responsible AI, comprehensive AI governance, and readiness planning.
Contact us today to strengthen your AI strategy and achieve full AI readiness.
FAQs
What are responsible AI advisory services?
Responsible AI advisory services support secure and safe AI adoption through governance, risk assessment, policy, and assurance. They also support delivery coordination for priority use cases.
Do we need an AI acceptable use policy?
Yes, because teams will use AI with or without guidance. A policy sets what is allowed, what is not allowed, and what must be approved. It also sets data handling rules and tool settings.
How do you help with AI governance in practice?
We define the roles, approvals, and evidence needed to control AI use. We also set a register for use cases and vendors, then align it to risk. That reduces ad hoc decisions.
How do you identify AI threats in practice?
We start with the use case, data flows, and system boundaries. Then we map threats using MITRE ATLAS. That gives a repeatable way to cover common AI attack paths.
How do you identify and prioritise AI risks?
We use the MIT AI Risk Repository to structure risk identification. Risks are mapped to impact, likelihood, and control coverage. Priorities reflect business context and risk appetite.
What control libraries do you use?
We use OWASP guidance for application and AI security controls. We use ISO 42001 for AI management system controls. We also map to ISO 27001 where security controls already exist.
How does this work for a real project?
We define the scope and success measures first. Then we run threat and risk identification, and select controls from agreed libraries. Work is delivered through a clear plan with owners and evidence.
How do you handle third party and vendor AI?
We assess vendor claims against your control needs and evidence requirements. Risks are captured in the same register as internal AI use. Contract and assurance needs are made explicit.