Why AI Monitoring Demands a Different Approach From Traditional Software Oversight

Mitigating AI's Unique Risks with AI Monitoring

AI monitoring is emerging as a distinct discipline within IT operations, driven by the unique challenges AI brings to organizations. Coralogix, a security and observability vendor, recently expanded its capabilities by acquiring AI monitoring startup Aporia in December 2024. This move led to the launch of Coralogix AI Center in March, a platform designed to track AI usage, enforce security guardrails, and measure response quality and costs.

Why AI Monitoring Is Different

Ariel Assaraf, CEO of Coralogix, explains why AI requires a different approach than traditional software monitoring. Unlike software where systems are either working or failing, AI operates on a spectrum. There’s no clear-cut error signal when AI causes damage or underperforms—issues can arise silently without triggering conventional alerts.

“People often assume AI monitoring is like monitoring code, but that’s incorrect,” Assaraf says. “There’s a gradient of outcomes, and harm can happen without any error or metric going off.” This nuance makes AI monitoring critical, especially for established enterprises facing the risks of integrating AI into their operations.

Challenges for Enterprises

While smaller companies may see AI as an opportunity, larger organizations often view it as a significant risk. AI introduces a dramatic shift that requires new strategies to manage safely. For many enterprises, figuring out how to handle AI’s unpredictability and potential for subtle failures is a pressing concern.

Mapping AI Usage with Security Posture Management

The foundation of effective AI monitoring is understanding what AI tools are in use within an organization. Coralogix calls this approach AI security posture management, similar to established cloud security posture methods used by providers like Google, Microsoft, and Palo Alto Networks.

Coralogix AI Center automatically discovers and inventories AI models deployed across the enterprise. It then applies proprietary models to monitor AI outputs and enforce guardrails designed to address key risks, including:

• Preventing sensitive data leaks
• Stopping AI hallucinations and toxic responses
• Avoiding referrals to competitors

When a guardrail is triggered, the system logs the event and allows teams to replay interactions. This helps resolve issues proactively by engaging with affected users before problems escalate.

Balancing Guardrails and Flexibility

While governance is essential, AI’s value comes from its nondeterministic nature. Over-restricting AI with too many guardrails risks turning it into just another piece of complex, costly software. Assaraf stresses the importance of finding a balance that keeps AI flexible enough to deliver value without exposing the business to unchecked risks.

Effective AI monitoring means guiding AI behavior without boxing it in.