A compromised AI system
doesn't crash your infrastructure.
It makes bad decisions on your behalf.
tointelligence · omer taki
· why it is different

AI cybersecurity is not an extension of classic cybersecurity.

Classic cybersecurity protects systems. A firewall, an antivirus, a SOC : these devices prevent malicious actors from accessing your infrastructure or data. The consequences of a successful attack are visible: downtime, data exfiltration, ransomware encryption.

AI cybersecurity is different in nature. AI systems are not just targets. They are decision vectors. A compromised AI system doesn't crash your infrastructure : it keeps running normally while producing systematically biased recommendations or decisions. The consequence is not visible. It is progressive, operating at the level of your decisions.

The most dangerous AI attack is not the one that stops your systems. It is the one that leaves them running while producing the wrong answers.
· three decisional risk vectors

What the board must evaluate : not delegate.

· vector 1 · model poisoning

A malicious actor introduces corrupted data into the training or feeding process of an AI model. The model continues to function normally, but its outputs are biased in a direction predictable to the attacker. This type of attack is particularly dangerous because it is difficult to detect without active monitoring of model outputs over time.

· vector 2 · decisional system manipulation

Carefully constructed inputs (prompt injection, adversarial inputs) manipulate the outputs of an AI system used for critical decisions. A fraud detection system can be manipulated to validate fraudulent transactions. Human oversight of critical decisions is not a regulatory luxury. It is the only effective line of defence against this vector.

· vector 3 · vendor supply chain exposure

Your AI vendor is itself an attack surface. The data you transmit via its APIs, the contexts you share, the deep integrations into your systems : all create exposure your own security posture doesn't cover.

· why responsibility escalates

The decisions that create AI vulnerabilities are strategic decisions.

In classic cybersecurity, operational responsibility belongs to the CTO or CISO. The board oversees, approves budgets, receives reporting. This model works because the risks are technical and the responses are technical.

AI cybersecurity doesn't work on this model. The decisions that create AI vulnerabilities : which vendor to use, which data to externalise, which AI systems feed which critical decisions : are strategic decisions made or validated at leadership level.

The responsibility to secure these choices therefore escalates to the same level that made them. Not because IT is incompetent. Because these decisions are not technical in nature.

CTO / CISO
Infrastructure security
Protect systems, detect intrusions, respond to incidents. Technical perimeter, technical response.
Executive leadership
Exposure decisions
Which data flows to which vendors. Which AI systems influence which critical decisions. Which dependencies create attack vectors.
Board
Oversight and liability
Validate the acceptable risk framework. Supervise AI decisional systems. Bear liability toward regulators and stakeholders.
· the EU AI Act angle

Regulation acknowledges what technology alone cannot manage.

The EU AI Act requires operators of high-risk AI systems to maintain effective human oversight : an obligation that is governance-based, not technical. It explicitly recognises that some AI risks cannot be managed purely by technical measures.

Human oversight of AI decisional systems is simultaneously the primary regulatory obligation and the primary defence against AI cyber risk vectors. That is not a coincidence.