The AI Security Paradox: Why Do We Trust What We Can't See?

What happened?

Recent research by CyberArk, published in TechRadar, reveals an alarming contradiction in enterprise AI adoption: while 87% of organizations consider their identity management posture ready to support AI-driven automation, 46% acknowledge their identity governance is deficient. This gap, dubbed the 'AI security paradox,' reflects that companies are granting AI systems increasing levels of access and autonomy without establishing clear mechanisms to monitor or verify their behavior. The paradox is not new in the tech world: it echoes the trust-versus-verification gap that emerged with mass cloud adoption a decade ago, when companies migrated workloads without adequate security policies, leading to incidents like the Capital One data exposure in 2019. However, the current speed of AI adoption — driven by top-down pressure to automate processes — surpasses any precedent, according to the report.

Why is it important?

The paradox exposes a fundamental risk: AI agents, operating with autonomous access and inherited permissions, undermine traditional identity governance models. According to the research, 73% of organizations believe that permanent access for AI agents increases security risk, but pressure for operational efficiency leads them to accept those risks. Moreover, 80% of companies cannot determine why an AI agent performed a privileged action, turning governance reactive rather than preventive. Historically, similar issues arose with service accounts in IT environments: excessive permissions and lack of monitoring led to breaches like SolarWinds in 2020. The difference with AI is that agents can make autonomous decisions in milliseconds, exponentially multiplying potential damage. For companies, this means security models based on implicit trust — assuming an internal system is safe — are no longer viable. The CyberArk report also notes that traditional identity management models relied on four key assumptions: predictable behavior, human intent, bounded permissions, and static roles. AI agents break all these assumptions, as their actions can be unpredictable, they lack human intent in the traditional sense, they inherit permissions from users and systems, and they operate with dynamic roles.

Consequences and what readers should know

The spread of 'shadow AI' exacerbates the problem: 53% of organizations regularly find unauthorized AI tools or agents accessing corporate systems, but only 28% can detect them in real time. This phenomenon is comparable to the 'shadow IT' of the cloud era, where departments adopted SaaS without IT's knowledge, but with AI the scale and risk are greater. For example, an unauthorized AI agent could access financial or customer data and perform transactions without oversight — something that has already occurred in test environments according to security reports. For security leaders, this means they must rethink governance, adopting models that enable real-time visibility and control over AI actions. The paradox will not be solved by technology alone; it requires a cultural shift that prioritizes verification over blind trust. Companies should implement 'least privilege' policies for AI agents, similar to what is done for human accounts, but adapted to the autonomous nature of agents. Additionally, the 46% of organizations with deficient governance should consider frameworks like the NIST AI Risk Management Framework or ISO/IEC 42001, which provide guidelines for AI risk management. In the market, solutions like CyberArk, Okera, or Immuta are emerging to address these challenges, but adoption is still nascent. The key lesson is that adoption speed must not outpace governance capacity; trusting without seeing is the new business risk.

The AI security paradox reminds us that adoption speed must not outpace governance capacity. Trusting without seeing is the new business risk.