Transforming Technology Risk Management with AI (Demo Room #11)

Welcome to The Demo Room – your front-row seat to the future of RegTech, RiskTech, and AI innovation. 

In this series, we document our research interviews with the most forward-thinking vendors tackling the industry's biggest challenges. Each blog is built around a comprehensive product demo, providing clear insights into how these innovations address industry challenges.

On this occasion, we met with Kathryn Carlson, Chief Product Officer, and Kim Alderman, Director of the AI Innovation Lab, at Riskonnect to discuss how the firm is combining generative and predictive AI with its mature risk architecture to redefine technology risk management.

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As digital transformation accelerates, the systems that drive performance also amplify exposure. Organisations today depend on an expanding network of interconnected IT assets, cloud services, and AI applications. The scale of digital dependency is growing faster than most risk frameworks can keep up.

Cyber incidents, operational outages, and third-party failures have become routine. Between late 2022 and 2023, more than 30,000 cybercrime incidents were reported globally, including over 12,000 attacks in public administration and 3,000 in finance. In the UK, 74% of large enterprises reported security breaches in 2024 (up from 50%), reflecting the vulnerability created by complex, high-value data environments.

The impact of these incidents extends beyond IT. The average cost of a data breach rose to $4.88 million in 2024, while listed firms suffered average share price declines of 7.5% following major cyber events. Reputational damage and customer attrition compound these financial losses, making technology risk a strategic and enterprise-wide issue.

The Problem for Firms

Firms are under immense pressure to strengthen technology risk management, yet most still operate within fragmented, opaque environments. Digital ecosystems have grown faster than governance frameworks can adapt. Critical assets span legacy infrastructure, SaaS platforms, and AI applications – each managed by different teams using different tools, with little integration or oversight.

This fragmentation makes it almost impossible to gain a single, connected view of risk. Controls are duplicated or missing, dependencies between systems go unmapped, and ownership of remediation efforts is unclear. When incidents occur, firms scramble to piece together incomplete data from multiple sources, slowing response and obscuring root causes.

Manual processes compound the problem. Spreadsheets, static risk registers, and disconnected GRC tools force teams to re-enter information, reconcile inconsistencies, and justify decisions retrospectively. As regulatory expectations tighten under DORA, NIS2, and the EU AI Act, the administrative burden continues to increase.

In short, firms are being asked to manage more risk, in more areas, with less visibility and fewer resources. Without connected data and automation, even mature organisations struggle to identify where their real exposure lies, let alone build resilience against the next disruption.

A Solution: AI For Technology Risk Management

Riskonnect approaches technology risk through an integrated lens, connecting cyber, operational, and third-party risks within a unified platform. This foundation eliminates silos and provides the contextual understanding needed to manage complex technology ecosystems. Its Intelligent Risk framework underpins the platform, embedding AI into resilience workflows across three dimensions: guide, predict, and assist. Riskonnect leverage a combination of predictive, generative and agentic AI to improve the efficiency and efficacy of technology risk management. 

Guide capabilities enhance decision-making. Riskonnect’s generative AI reduces manual effort by summarising lengthy artefacts, drafting policies or business impact assessments, and surfacing issues from integrated risk data. These capabilities already support tabletop exercises and reusable scenario libraries, with continuous monitoring on the horizon as new data streams trigger fresh risk suggestions. 

Planned retrieval-augmented generation (RAG) over client data (such as resilience frameworks, risk artefacts, and incident logs) will enable more context-aware insights and dynamic risk suggestions as new information becomes available.

Predict draws on Riskonnect’s broader analytics foundation, using decades of standardised risk data to forecast incident severity, duration, and control effectiveness. These predictive models quantify exposure and highlight emerging vulnerabilities before they escalate, complementing the generative layer by providing a data-driven basis for forward-looking risk decisions.

Assist functions represent the next phase: agentic execution. Modular AI agents handle specific resilience tasks such as policy mapping, risk-control alignment, and incident triage, seamlessly orchestrated across workflows. The result is a scalable system that not only identifies and analyses risk but can also act on it.

This AI-driven approach is anchored in responsible design. Every feature includes human-in-the-loop review and transparent disclosures, while Riskonnect’s AI Lab applies rigorous model validation and produces model cards aligned with regulatory expectations.

The outcome is a resilient, expert-enriched risk environment – one that combines structured data, predictive insight, and agentic execution to help firms manage the complexity of modern technology risk. By embedding intelligence directly into workflows, Riskonnect transforms TRM from reactive oversight into a proactive, automated discipline.

Parker & Lawrence’s view

Riskonnect is advancing toward a more autonomous, expert-enriched platform for business resilience, ultimately helping technology risk teams to understand and manage AI applications while accelerating their responsible rollout. Its contextualised risk data provides a strong foundation for generative AI at scale, while the key challenge will be realising the full potential of agentic systems, a frontier that remains unproven but promises significant rewards for those who succeed.

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