What GTG-1002 Revealed

The attack didn't require zero-day exploits or custom malware. Instead, it exploited three critical blind spots that exist in most organizations today.

External AI tool usage is invisible.

Employees use ChatGPT, Claude, Gemini, and hundreds of other AI services daily, but organizations have no visibility into what data employees are transmitting. Sensitive information like customer data, code, and strategic plans routinely flows to external AI services without any controls. Threat actors can gather reconnaissance the same way—asking external AI services about company information that employees have already shared.

Internal AI deployments lack proper controls.

Enterprises deploy internal Copilots and LLM-powered applications that often have direct access to sensitive databases and customer information. There are no clear policies for what data these internal AI systems should access, and no meaningful monitoring for when they're being misused or compromised. A single compromised internal AI system becomes an automated attack tool capable of querying databases, extracting information, and categorizing it by intelligence value.

AI-orchestrated attacks can look like normal activity.

An AI agent performing reconnaissance makes thousands of API requests with structured patterns. Traditional SOC tools alert on "unusual network activity," but this is normal for AI systems. Behavioral analysis designed for human attackers doesn't work for AI agents. The attack blends into network noise.

The Core Challenges Enterprises Face

Securing AI usage requires addressing fundamentally different threats than traditional cybersecurity.

• Challenge 1: Visibility is the foundation.
  External AI usage happens outside your network, leaving no audit trail of what data left the organization. By the time you discover a breach, the sensitive information has already been compromised. Without visibility, policy enforcement is impossible.

• Challenge 2: Internal AI systems are difficult to secure.
  LLM applications need access to data to be useful—databases, documents, APIs. But broad data access creates risk if the system is compromised or misused. It's difficult to distinguish between legitimate AI queries and suspicious patterns. Traditional access controls don't map well to AI system behavior.

• Challenge 3: Detection methods are outdated.
  SOC teams are trained to detect human attacker behavior such as login anomalies, lateral movement, or data transfers. AI agents may operate differently or attempt to mimic common usage patterns. They're faster, more systematic, and leave different traces. Existing alert patterns generate too much noise to be useful. Security teams need new behavioral signatures specifically for AI-orchestrated activity.

How SuperAlign Helps Enterprises Address These Challenges

SuperAlign Radar was built specifically to address the AI security gaps that GTG-1002 exposed.

Visibility into external AI tool usage
SuperAlign Radar gives organizations real-time awareness of where sensitive data is being sent to external AI services. It identifies which external AI tools are in use across the organization, who is using them, and for what purposes. More importantly, it detects when sensitive data—customer information, credentials, proprietary code, strategic information—is about to be transmitted to an external AI service, enabling organizations to either block the transmission or alert users before data is compromised.

Control over internal AI interactions
SuperAlign Radar monitors internal AI systems and the data they access. Organizations can establish policies around what data internal AI systems should be able to reach, what kinds of queries are normal, and what patterns indicate misuse or compromise. This allows enterprises to deploy internal Copilots and LLM applications with the productivity benefits they need while maintaining security boundaries.

Behavioral understanding of AI activity
SuperAlign's approach to behavioral analysis is rooted in understanding how AI systems actually operate. Besides alerting on activity that looks suspicious to humans, SuperAlign also identifies patterns that can be specific to automated AI behavior—rapid-fire API requests with structured payloads, systematic reconnaissance patterns, bulk data extraction followed by categorization operations. These are the signatures of AI-orchestrated attacks that traditional SOC tools miss.

Practical integration with existing security workflows
SuperAlign Radar works with existing security infrastructure rather than replacing it. Security teams can see AI-specific context within their existing tools and processes, enriching alerts and enabling faster, more targeted investigation when threats are detected.

Why This Matters Now

The GTG-1002 campaign wasn't an edge case. It demonstrated that AI-powered attacks are now operationally feasible at scale, threat actors are actively adopting these techniques, and organizations lack basic visibility into the attack surface. Traditional security approaches are insufficient.

The barriers to entry for AI-powered cyberattacks have dropped. Threat actors now have a new toolkit. Most enterprises are unprepared to defend against it.

The organizations that establish visibility, enforce controls, and develop detection capabilities now will be the ones prepared for what's coming next. SuperAlign makes this practical by addressing all three layers at once—ensuring that enterprises have the visibility, controls, and detection capabilities needed to stop AI-orchestrated attacks before they succeed.

The question isn't whether AI-powered attacks will target your organization. The question is whether you'll be ready when they do.