The illusion of control: How shrinking teams and AI are redefining cyber risk

In theory, it appears to be feasible. In practice, this has created a growing and dangerous gap between what organizations believe they control, and what is actually happening inside their environments. 

This disconnect is what I refer to as the risk gap — and it is progressively expanding.

The quiet shift no one budgeted for 

Canadian organizations are well-equipped with security frameworks, risk registers, and  policies. Most organizations can point to ISO certifications, privacy initiatives, and advanced  technologies. However, beneath those artifacts lies a quieter reality: Security and governance, risk and compliance (GRC) teams are shrinking, even as expectations placed on them are growing exponentially. 

AI systems are being deployed at a pace that outstrips the ability of governance frameworks  to adapt. Cloud environments are generating machine identities at a rate that surpasses  human capacity for monitoring. Regulators are providing guidance that assumes operational  maturity; however, many organizations lack the personnel necessary to maintain such  standards. 

This is not due to negligence. It is due to structural imbalance. 

Boards continue to anticipate that risk will be effectively managed. Executives continue to  anticipate reassurance. Regulators continue to anticipate accountability. However, those  responsible for delivering all three are being tasked with managing wider scopes while operating with fewer resources. 

That gap does not remain purely theoretical for an extended period. 

From human risk to machine risk 

Historically, cybersecurity risks has centered on individuals: Malicious insiders, negligent personnel, or external adversaries exploiting human vulnerabilities. Today, the most rapidly  expanding source of risk is not attributable to human factors. It is machine-driven.

Service accounts, APIs, bots, automated workflows, and AI agents — these entities now  outnumber human users in number within most environments. They authenticate identities,  transfer data, make determinations, and carry out actions at machine speed. However,  governance concerning them is frequently informal, fragmented, or entirely absent.  

As a recent report by identity security vendor CyberArk recently highlighted, machine identities now constitute the majority  of privileged access pathways in cloud environments (get source); however, they are rarely catalogued or  examined with the same thoroughness as human access. 

This creates a paradox: Organizations allocate significant resources to AI and automation to  “reduce risk” while concurrently introducing new, unmanaged risk factors for which no  individual is fully accountable. 

The illusion of oversight 

Executives are often told that automation enhances control, dashboards provide enhanced visibility, AI detects irregularities, and tools assure enhanced efficiency.  

But control without ownership is not control — it is theatre. 

In numerous organizations, AI-powered systems operate across various business units,  vendors, and jurisdictions. Hence, authority for decision-making becomes more widely  distributed. When issues arise, the typical response is, “The system provided the  recommendation,” or “The process is automated.” 

Automation does not remove accountability. It simply redistributes it, often without  transparency. 

Regulators are starting to acknowledge this. The EU AI Act, ISO/IEC 42001, and ongoing  Canadian government discussions on AI governance all emphasize the importance of human oversight, risk-based controls, and well-defined accountability frameworks. However, implementing these expectations requires time, people and governance maturity, essentially those very  resources that are currently being diminished. 

When risk becomes invisible 

One of the most concerning outcomes of the risk gap is not breach or failure, it is false confidence. Risk assessments are conducted solely due to mandatory requirements, rather than to support meaningful decision-making. Metrics are available. but are not utilized for strategic planning. Incident response plans look robust; however, they depend on teams that  no longer exist at scale.

In executive discussions, risk is often depicted as a binary choice: Either compliant or non compliant, secure or insecure. In actuality, risk is fluid, contextual, and inherently  operational. 

Automation does not remove accountability. It simply redistributes it, often without  transparency. 

When teams are stretched thin, risks do not disappear. They are merely left undocumented, unchallenged, or deferred. Over time, this results in “digital debt” accumulated exposure that becomes apparent only during incidents, audits, or regulatory investigations. 

By that point, the cost is no longer theoretical. 

AI is not the issue — Governance is 

It is tempting to portray artificial intelligence as the villain in this narrative; however, that is not the case.

AI serves merely as a force multiplier, both for good governance and poor  governance. Organizations with robust foundational controls, well-defined ownership, and advanced risk management practices can leverage AI responsibly. Organizations lacking those foundational elements will discover that AI will accelerate their weaknesses. 

The question that executives ought to be asking is not, “Are we utilizing AI?” It should be, “Do we understand the areas in which AI is making decisions, the data it touches, and  the accountability when it fails?” 

Without that clarity, artificial intelligence becomes an additional level of obscurity,  remarkable in demonstrations but dangerous in implementation. 

Bridging the risk gap demands a different conversation 

Bridging the risk gap does not start with the procurement of additional tools. It begins with the process of redefining expectations. Executives must acknowledge that risk management  is an evolving process. If threat surfaces broaden and teams diminish, compromises must  be made. Either the scope needs to be reduced, or the risk tolerance needs to be elevated, whether explicitly acknowledged or not. 

This requires honesty at the leadership level: 

• What risks are we knowingly accepting because we lack capacity?
• Where have we automated decisions without adequate oversight?
• Which controls exist on paper but cannot be sustained operationally?
• Who owns machine-driven decisions today? 

These are not technical questions. They are governance questions.

A call for executive realism 

Cybersecurity maturity is not assessed solely by the number of frameworks an organization  adopts. It is assessed based on how much it aligns strategy, personnel, and accountability with the reality of its environment. Canadian organizations are approaching an inflection  point. The pressure to innovate is real. The pressure to reduce costs is real. The pressure to comply is real. Neglecting the tension between these forces does not eliminate it; it rather compounds it. 

The risk gap will continue to widen unless leaders are prepared to address difficult realities  regarding resourcing, scope, and accountability within a technology-driven environment. 

Risk cannot be effectively controlled through intentions alone. It requires allocation of  resources, clarity of purpose, and the courage to acknowledge when expectations surpass  capacity. Until then, the most dangerous threat facing organizations may not be external  attackers or malicious AI, but the quiet belief that control still exists where it no longer does.