Translation result
[DigitalToday AI Reporter] Side-channel attacks are highlighting structural weaknesses in traditional rule-based security detection systems.
On May 4, SiliconANGLE reported that Evan Powell, CEO of DeepTempo, said AI-security conversations have largely focused on model failures. He warned a more urgent problem is the blind spots current detection systems still miss.
Side-channel attacks rely on physical signals—power consumption, electromagnetic emissions and processing time—rather than software code. Adversaries can measure tiny hardware leakages to extract sensitive information like encryption keys. Recent studies show an external observer can infer the subject of AI interactions by analyzing only the structure, timing and order of encrypted traffic, without decrypting or inspecting payloads.
The core issue lies less with the attack techniques than with how detection systems are designed. Rule-based detection assumes predefined indicators—known signatures, clear anomalies or perimeter breaches. But side-channel attacks, low-and-slow intrusions, misuse of legitimate tools and AI-driven attack chains often look benign when considered as isolated events. These threats only reveal themselves when you analyze the full sequence of activity over time.
That can leave security teams with no alerts to investigate. It’s not merely low-confidence flags; sometimes there is no analyzable signal at all. Powell describes this as a structural limitation rather than a simple coverage gap, because some attack patterns cannot be captured by rules.
That blind spot is growing. As organizations broaden AI use across operations and as attackers incorporate AI into their toolkits, the areas traditional detection models miss are also expanding. Yet much security spending still focuses on post-detection efficiency—alert summarization, automated investigations and prioritization. Those measures have little effect against attacks that never trigger alerts.
Experts recommend shifting detection from isolated events to continuous behavior analysis, mapping relationships between systems and tracking changes over time. For example, lateral movement over encrypted channels leaves traces in changing access patterns rather than in traffic content; side-channel leakage appears in system behavior and structure rather than in the data itself. Organizations should first assess what their current detection systems actually see in operational environments before adding new tools.
Most Commented