Control Risks supported a private equity fund to equip Chief Technology Officers (CTOs) with practical tools to identify threats, assess risk and embed a converged, decision-led approach to security.
Aligning risk practices across a fast-growing portfolio
A private equity fund with a diverse portfolio of software-as-a-service companies wanted to encourage innovation and adoption of AI across its investments. At the same time, the fund recognised that portfolio CTOs were being asked to navigate an increasingly complex and fast-evolving threat landscape, often without a consistent framework for identifying AI-related threats, assessing risk holistically or translating technical concerns into business-relevant decisions for management and investors.
Risk was often considered in silos – separating cyber, operational and geopolitical factors – limiting visibility of how threats could impact critical assets and investment value. The fund needed a practical, scalable approach to build risk awareness and capability across its portfolio without slowing growth or innovation.
Embedding converged risk thinking
Control Risks delivered a structured education and enablement programme to help portfolio CTOs understand the converged threat landscape created by AI adoption, and apply a consistent approach to AI risk identification, assessment and governance:
- Designing and delivering interactive workshops with CTOs across the portfolio, focused on identifying AI-specific and AI-enabled threats relevant to their products, data, operating models and customer environments.
- Introducing a converged risk methodology, bringing together cyber, operational, data, third-party and emerging technology threat perspectives into a single enterprise view.
- Training participants on practical threat identification techniques, including adversary-led thinking, misuse-case analysis and scenario-based assessment.
- Exploring AI-specific risk themes, including model misuse, data leakage, prompt injection, autonomous agent behaviour, model drift, misalignment risk, third-party model dependency and weak human oversight.
- Supporting CTOs in mapping AI-related threats to critical assets, systems, data flows, decision points and business processes.
- Guiding teams through risk assessment methodologies to translate technical AI risks into business impact, governance priorities, remediation needs and investment considerations.
- Helping participants assess where existing cyber, data and technology governance controls were sufficient and where AI adoption required additional guardrails, escalation routes, monitoring or accountability.
- Facilitating peer discussion and cross-portfolio knowledge sharing, building consistency in how CTOs identify, prioritise and govern AI-related risk.
- Providing repeatable tools and templates to embed risk-led decision-making into day-to-day technology, product and investment activities.
Clearer risk insight that supports investment decisions
The programme significantly improved the ability of portfolio CTOs to identify and assess risk in a structured and business-relevant way.
Participants developed a clearer understanding of how different threat vectors intersect and impact operations, assets and growth objectives. Risk assessments became more consistent across the portfolio, enabling better comparison and prioritisation of exposures.
CTOs were able to translate complex technical risks into clear, actionable insights for senior leadership and investors, improving the quality of decision-making at both company and fund level.
Empowering CTOs with a blueprint for resilient growth
Technology leaders are now better equipped to anticipate threats, assess their impact and make informed decisions aligned to business priorities. The introduction of practical frameworks and shared language is part of the fund’s journey to shape governance and improve collaboration between portfolio companies and the fund.
This has enhanced resilience across operations and assets, increased confidence in managing risk during growth and transformation, and embedded a repeatable model for integrating threat-informed decision-making into future investments.