5 prerequisites to AI-augmented risk management

Risk management is a key pillar of cybersecurity and business growth. While executives sleep soundly believing their traditional risk management systems will protect them, cybercrime cost the global economy over $1 trillion in 2020, with projected costs reaching $10.5 trillion annually by 2025. The brutal truth is that most risk management approaches are not just inadequate, they're dangerously obsolete.

Three key problems

While there are various challenges with risk management to date, over the last two decades, I have seen organisations deal with three key problems, over and over, without much resolution:

1. Data everywhere: Generally, more data would mean better insights. However, it often leads to more unstructured, unclassified, and undocumented data being fed into risk assessment pipelines. Yet for immature companies, there is often data everywhere, but it never makes it to their risk assessment because no one knows where it is, who has ownership of it, how it is being used, how the data workflows are, etc.
2. Lack of business context: Security issues mean nothing without business context. An issue, a finding, or a vulnerability becomes a real risk only when you understand it within the business context. Often, businesses don’t have that business context, leading to false positives, incorrect risk assessment, or a complete lack of risk management. Additionally, the lack of or an incorrect business context can mean incorrect assessment, leading to millions of dollars in losses, fines, operational disruptions, etc.
3. Complexity of third-party relationship: The sheer volume and complexity of third-party relationships mean a lack of due diligence and inadequate or non-existent vendor management lifecycle, leading to extended risks across the supply chain, both known and unknown.

Why use AI in risk management?

AI-augmented risk management isn’t about automation. It’s about leveraging predictive analysis, pattern detection, qualitative analysis, real-time decision making, and anticipating plausible issues, shifting left the entire risk management program from reactive to proactive and predictive.

Given the three key problems highlighted above that most businesses are still dealing with, an AI-augmented cyber risk management isn't a luxury upgrade anymore. It’s one of the key use cases of AI. However, like any powerful weapon, AI-augmented risk management requires the right foundation to be effective and adequate.

Here are the 5 non-negotiable prerequisites that separate organisations thriving in the AI era from those that become tomorrow's breach headlines.

1. Data quality and architecture

Both legacy and new data systems create bottlenecks that prevent AI from processing information in real time. This can make threat detection, pattern matching, or predictive analysis reactive rather than predictive and in real time. AI effectiveness depends on both data velocity and quality. Unclassified, unknown, and unstructured data will lead to garbage in and garbage out.

Having implemented enterprise risk management for large corporations, including financial services, the quality of data and its architecture is a key problem that most companies still struggle with. However, to get the benefit of continuous AI risk monitoring, the underlying data quality, ownership, structure, and architecture must be well-defined. Secondly, this is also crucial because AI systems evolve as they encounter new data and operating conditions. The higher the quality of data, the better the quality of outcome for real-time threat analysis, predictive risk modeling, and automated response capabilities.

The greater the quality and diversity of your data sets, the better the AI and machine learning outcomes.

2. Executive-level AI literacy

Using AI-based risk management also means leaders need to understand AI’s capabilities and limitations. Without that understanding, your risk management is likely to lead to failed implementations, wasted resources, incorrect outcomes, etc. Reality is that AI governance spans the entire AI augmentation lifecycle. Therefore, AI augmentation requires organisational structures that enable AI systems to be used responsibly, ethically, and safely, with an understanding of the underlying AI risks. Without executive understanding of what AI governance looks like vs. what it should look like, AI-augmented risk initiatives will lack proper support and direction. C-suite leaders who take risks and make investment decisions without understanding AI's capabilities or limitations will eventually have to deal with unintended consequences. AI literacy and understanding of its capabilities, limitations, and risks are a must.

Additionally, an AI-literate board and executive team create strategic alignment, appropriate resource allocation, and realistic expectations for AI implementations, in this case for AI-augmented risk management.

When you see examples like JPMorgan Chase's COIN platform, which processes legal documents in seconds rather than the 360,000 hours lawyers previously required, it demonstrates an executive-driven AI transformation success.

In the age of AI, ignorance at the top isn't just expensive—it can be existential. That’s equally true for AI-augmented risk management, especially as it is used for executive decision-making.

3. Cross-functional risk integration

One of the long-term challenges many organisations still experience is that most cyber risk teams operate in silos, missing critical connections between IT risks, operational risks, and business risks that AI could identify.

Even if you care about cyber risk, the entire assessment could lead to different outcomes and, thereby, different decisions when dependency and connectivity to other risks are considered. Modern threats transcend traditional boundaries. A physical supply chain disruption can become a cyber vulnerability, which can trigger financial risk.

A cross-functional risk integration creates holistic risk visibility and enables AI to identify patterns across previously disconnected risk domains.

Maersk's 2017 NotPetya attack started as a cyber incident but cascaded into supply chain disruption, costing $300M because their risk systems couldn't connect operational and cyber risks.

4. Algorithmic transparency and explainability

Most AI models are black boxes. "Black box" AI systems make decisions that risk teams can't explain to stakeholders, creating compliance issues and eroding trust. Explainability is going to be key as we go forward.

Organizations must integrate governance, compliance, and risk management to face the challenges AI poses, including unauthorized access and model tampering. Transparency and explainability ensure regulatory compliance, build stakeholder confidence, and enable continuous improvement of AI models.

For example, HSBC's AI fraud detection system was redesigned to provide clear explanations for every decision after regulators questioned their ability to justify blocking customer transactions. When you are considering a transaction as a risk (e.g., fraud) and you block it, you need to be able to explain how the AI came to that decision of tagging it as fraudulent and blocking it.

An AI system you can't explain will be a liability you can't afford. Going forward, transparency and explainability are not going to be optional.

5. Human-AI collaboration framework

We are talking about AI-augmented, not AI-replaced. Why? Organizations that treat AI as a complete replacement for human judgment and decision-making rather than an augmentation of human expertise will lead to over-reliance, accountability issues, or underutilization.

The most effective risk management combines machine processing power with human contextual understanding and ethical judgment. Based on the level of risk, AI-augmented decision-making combined with human oversight maximizes both AI capabilities and human expertise while maintaining appropriate oversight and control.

An AI pattern recognition with human analyst expertise will serve as the foundation for better risk management and against cyber attacks.

The underlying power lies in a human-AI collaboration framework, not AI replacing human decision-making throughout.

The next steps

These prerequisites aren't just suggestions; they're a must to serve as the foundation for effective risk management in the world of AI. On one hand, every day you operate without AI-augmented risk management, you're barely scratching the surface of what your risk and threat landscape truly looks like. On the other hand, without these prerequisites in place, you are creating more noise, more silos, more incorrect decisions, and amplifying the worst parts of your already existing risk management framework.

Your competitors are reading the same list. The difference between winners and casualties isn't knowledge; it’s the quality of data, execution speed, and human-AI collaboration as fundamentals.

About the authors

Monica Verma is a leading spokesperson on Artificial Intelligence (AI), Cyber Resilience, Leadership, and Cybersecurity. She is a committed and passionate technology and security leader, a Chief Information Security Officer (CISO), recognised and awarded as Top No. 3 CISO in EMEA, and an engaging keynote speaker, copywriter, and founder with 20+ years of experience in the tech and cybersecurity industries. She is an award-winning leader, 2019 Best Security Advisor, 2022 Top 50 Women in Tech, and 2023 Top #3 CISO in EMEA. She is a renowned keynote speaker and a storyteller, as well as a board-certified qualified Technology Expert (QTE), and a former board member of Cloud Security Alliance (CSA).