Chapter 2: AI as a Force Multiplier and Why Cyber Risk Has Changed

AI is changing the nature of cyber risk in ways that are both structural and irreversible

While organizations have long faced digital threats, AI alters the balance of power, the speed of escalation, and the scale of harm. For boards and executive leaders, this is not merely a technological shift—it is a change in the conditions under which leadership and governance operate. 

The central implication is simple but profound: cyber risk in an AI-driven organization can no longer be understood as a linear extension of traditional cybersecurity. It is a distinct category of risk, with distinct failure modes, dynamics of escalation, and demands on leadership capability. 

From Tools to Force Multipliers

Historically, technology has served as a tool that amplifies human capacity. AI functions as a force multiplier. It does not just make existing processes faster; it changes who can act, how quickly they can act, and at what scale. 

This applies equally to organizations and to adversaries. 

On the defensive side, AI enables: 

• Faster detection of anomalies and intrusions 
• Pattern recognition across vast, complex data environments 
• Automated responses that operate beyond human reaction speed 

On the offensive side, AI enables: 

• Scalable and adaptive attack methods 
• Highly convincing social engineering and deepfake-based deception 
• Rapid mutation of attack vectors that outpaces traditional controls 

The asymmetry this creates is a leadership challenge. Boards must assume that any technological advantage they create for their own organization will, over time, become available to those seeking to exploit it. Cyber resilience, therefore, cannot be built on the assumption of permanent technological superiority. It must be built on organizational adaptability and leadership judgment. 

The New Attack Surface: Data, Models, and Ecosystems

 AI expands the cyber-attack surface beyond infrastructure and applications into three new domains that are less visible to traditional governance frameworks: 

1. Data as a strategic vulnerability

AI systems depend on vast amounts of data. Data quality, provenance, bias, and integrity are no longer solely operational concerns; they now shape decision-making at scale. Data poisoning, leakage, or manipulation can distort outcomes without triggering traditional security alarms. Leaders may be making “rational” decisions on corrupted foundations. 

2. Models as assets—and liabilities

AI models embody intellectual property, strategic intent, and embedded assumptions. They can be stolen, manipulated, or subtly degraded. Unlike traditional systems, model failure does not always manifest as a crash; it may present as quietly as deteriorating judgement across the organization. 

3. Ecosystems as hidden dependencies

AI-driven organizations increasingly depend on external platforms, cloud providers, open-source components, and data partnerships. Each dependency introduces a governance question: who is accountable when risk is distributed across organizational boundaries? Boards must oversee not only internal controls, but also the resilience of the ecosystem on which their strategy now rests. 

These new attack surfaces challenge traditional lines of responsibility. They require leaders who can think in systems rather than silos. 

Speed, Ambiguity, and the Compression of Decision Time

AI compresses time. Threats evolve faster, signals appear earlier but are less certain, and the window for effective intervention shrinks. For leadership teams, this creates a persistent tension between speed and judgement. 

Boards face a parallel tension in governance. Traditional oversight rhythms—quarterly reviews, annual audits, periodic risk assessments—are poorly suited to environments where risk profiles can change in weeks or days. This does not mean boards must govern at operational speed. It does mean boards must ensure that leadership teams are equipped to operate responsibly under compressed decision cycles. 

This is where leadership capability becomes decisive: 

• Can executives recognize when automated systems require human intervention? 
• Can they slow down decisions when speed creates ethical or strategic risk? 
• Can they act decisively when ambiguity is high and information is incomplete? 

AI does not remove human responsibility; it concentrates it. 

New Failure Modes: When Things Go Wrong, They Go Wrong Differently

AI introduces new forms of failure that boards and leaders are often not accustomed to managing: 

• Silent failure: Systems continue to operate, but outputs degrade over time due to biased data, model drift, or adversarial manipulation. 
• Scaled failure: Errors propagate across processes simultaneously, amplifying impact. 
• Reputational shock: AI-driven incidents can trigger immediate public scrutiny, regulatory response, and stakeholder backlash, even when technical damage is limited. 

These failure modes test leadership under conditions of uncertainty and scrutiny. The question for boards is not whether failures will occur, but whether leadership teams have the composure, credibility, and governance frameworks to respond in ways that preserve trust. 

Leadership Implications for the Board

 AI-driven cyber risk reframes what boards should look for in leadership: 

• Translational capability: Leaders who can translate technical risk into strategic and reputational implications. 
• Judgement under uncertainty: The ability to make responsible decisions when data is imperfect and time is compressed. 
• Ethical anchoring: The capacity to hold boundaries when technological possibility run ahead of governance frameworks. 
• System thinking: Comfort with interdependencies across data, technology, partners, and regulation.

These are not niche skills. They are becoming core leadership capabilities in organizations where AI is central to value creation. 

For boards, this has practical consequences for how executive roles are defined, how leadership teams are composed, and how succession is planned. Cyber resilience in an AI era is not only about how systems are built, but about who is trusted to lead when systems behave unpredictably. 

From Technological Readiness to Leadership Readiness

The first chapter argued that cyber resilience is a governance and leadership mandate. This chapter extends that argument: AI changes the nature of cyber risk in ways that make leadership readiness the critical differentiator between organizations that absorb disruption and those that are destabilized by it. 

Boards that focus primarily on technological readiness may feel reassured—until an incident tests decision-making, accountability, and trust. Organizations that invest in leadership readiness—clear ownership, strong judgment, and the ability to navigate speed and ambiguity—are better positioned to translate technological ambition into sustainable resilience. 

The next chapter will examine what this means in practice for leadership roles, profiles, and organizational design—and how boards can ensure that their leadership architecture is fit for an AI-shaped risk landscape. 

About the Author

Jan-Bart Smits is a Managing Partner at Stanton Chase Amsterdam. He began his career in executive search in 1990. At Stanton Chase, he has held several leadership roles, including Chair of the Board, Global Sector Leader for Technology, and Global Sector Leader for Professional Services. He currently serves as Stanton Chase’s Global Subsector Leader for the Semiconductor industry. He holds an M.Sc. in Astrophysics from Leiden University in the Netherlands.