AI Security: what every company should already be measuring.
- Data & AI
- article
Artificial Intelligence (AI) has become a corporate governance issue, with boards of directors around the world including AI in their risk agendas, and executives need to be prepared to respond clearly. This article organizes four questions that every C-level executive should be able to answer today.
What to answer when the board asks?
Using AI without risk mapping is equivalent to hiring suppliers without due diligence; exposure exists regardless of intention. When the board asks, the expected response is not a list of tools adopted, but an inventory of active risks: what data is being processed by AI systems (internal or third-party), what business decisions are influenced or automated by models, and what is the level of auditability of those decisions.
Companies that cannot answer these three dimensions are operating with unmanaged exposure. From a CTO’s perspective, this is equivalent to having technical debt with consequences that the board is unaware of. The most honest and effective response to the board begins with the acknowledgment of what is still not mapped. From there, it is possible to build a concrete plan.
How many AI points exist today in your operation and how many does your AI Security cover?
“AI points” do not only refer to formal artificial intelligence projects; they include any instance where AI models process data, generate outputs, or support decisions: productivity tools with embedded AI, third-party APIs integrated into internal systems, scoring models in credit or HR processes, customer service assistants, content automations, and much more.
Most companies underestimate this number, and a significant part of the AI points in operation has been adopted by business teams without undergoing any formal security assessment.
The second part of the question is even more critical: how many of these points does your AI Security effectively cover? Coverage means active monitoring, data traceability, access controls, and incident response capabilities. A relevant indicator is the calculation between mapped AI points and points covered by controls; this gap is the real risk.
Is your AI Security policy a document or a system?
An AI security policy document defines rules, responsibilities, and guidelines; it is necessary but insufficient. An AI security system is the set of processes, tools, and controls that operationalize the policy on a daily basis.
Signs that your company is still in the documentary stage: the policy has been approved, but there is no formal process for new AI projects to undergo risk assessment before going into production; there is no centralized record of AI tools in use; there are no regularly monitored indicators on incidents or deviations.
Signs that the organization is evolving to a system: there is an onboarding process for new uses of AI, with clear approval criteria; there is an updated inventory of AI assets; AI-related incidents have a defined response flow.
How to ensure AI Security when contracting a solution partner?
The starting point is to check if the supplier has structured security processes applied to AI software development. Due diligence needs to understand how the supplier handles sensitive data during development, who has access to testing and production environments, and how incidents are reported and managed.
Success Case
At Objective, data security is a priority. Recently, we developed an AI solution for one of Brazil’s largest beverage companies that integrates order receipt, ERP, notification to the Tax Authority, and issuance of CT-e within legal requirements. The complexity was such that any failure in the tax flow has regulatory consequences.
The solution underwent source code auditing, architectural compliance checks, and security analysis conducted by the client’s internal team, responsible for ensuring that any system complies with the company’s standards. The result was 0% security vulnerabilities and 100% of the code compliant with the manufacturer’s standards. The result is the consequence of a structured development process to support the level that critical operations require. Contact our specialists and find out how we can help you.
An inventory of active risks: what data the AI processes, what decisions it influences, and what the level of auditability is. If your company cannot answer, this is the starting point.
More than you imagine. Most businesses underestimate this number because much of it was adopted by business teams without a security assessment.
No. Policy is a document and AI security is a system. If there is no process for new projects to undergo assessment before going live, the policy exists only on paper.
It is a distributed responsibility: accountability at the C-level, governance in a dedicated committee or role, and execution in the areas that operate with AI.
Evidence and not promises. Code audit, architectural traceability, and clear contractual clauses regarding intellectual property, incident responsibility, and regulatory compliance.
