Artificial Intelligence Threatens Your Reaction Time: How AI Reduces Response Windows

Post Views: 12

The Increasing Threat of AI-Powered Cyber Attacks

The increasing use of artificial intelligence (AI) has significantly altered the cybersecurity landscape, compressing the time between exposure and exploitation. In the past, defenders had a relatively lengthy window to respond to vulnerabilities, but AI-powered adversarial systems can now identify and exploit weaknesses in a matter of minutes.

Historical Context: The Exploitation Window

Historically, the exploitation window favored defenders, allowing them to assess their exposure and apply patches before attackers could capitalize on vulnerabilities. However, AI has shattered this timeline. In 2025, over 32% of vulnerabilities were exploited on or before the day the CVE was issued. This is due in part to the massive infrastructure powering AI-powered scan activity, which can reach 36,000 scans per second.

AI-Powered Attackers: Speed and Context

While speed is a significant factor, context is equally important. AI-powered attackers focus on the small fraction of exposures that can be chained into a viable route to critical assets, ignoring the “noise” that comprises 99.5% of identified security issues. This approach allows them to isolate the most critical vulnerabilities and exploit them quickly.

Scenarios of AI-Powered Attacks

There are two distinct scenarios to consider when evaluating the threat posed by AI-powered attackers. The first scenario involves AI as an accelerator, where attackers use machine speed and scale to exploit the same vulnerabilities and misconfigurations they always have.

This can take the form of automated vulnerability chaining, where attackers use AI to link together “Low” and “Medium” issues to breach a system.
Another tactic involves “identity hopping,” where AI-driven tools map token exchange paths from a low-security dev container to an automated backup script, and finally to a high-value production database.
Social engineering has also surged, with AI allowing attackers to mirror a company’s internal tone and operational “vibe” perfectly, creating context-aware messages that bypass usual “red flags” employees are trained to spot.

AI as the New Attack Surface

The second scenario involves AI as the new attack surface, where attackers target an organization’s AI adoption to create entirely new vulnerabilities.

This can involve the Model Context Protocol and Excessive Agency, where attackers use prompt injection to trick public-facing support agents into querying internal databases they should never access.
Sensitive data is then surfaced and exfiltrated by the very systems trusted to protect it.
Another tactic involves poisoning the well by feeding false data into an agent’s long-term memory (Vector Store), creating a dormant payload.
The AI agent absorbs this poisoned information and later serves it to users, all while appearing to be normal activity.
Attackers can also poison the supply chain by using large language models (LLMs) to predict the “hallucinated” package names that AI coding assistants will suggest to developers.

Reclaiming the Response Window with CTEM

To reclaim the response window, organizations must shift from reactive patching to Continuous Threat Exposure Management (CTEM). This operational pivot aligns security exposure with actual business risk, focusing on the convergence points where multiple exposures intersect.

By doing so, organizations can close the paths faster than AI can compute them, effectively reclaiming the window of exploitation.

CTEM requires organizations to answer a critical question: which exposures actually matter for an attacker moving laterally through the environment?

By prioritizing the exposures that can be chained together into viable paths to critical assets, organizations can eliminate dozens of routes with a single fix.

This approach recognizes that AI-enabled attackers don’t care about isolated findings, but rather the convergence points that allow them to move laterally through the environment.

Conclusion

Ultimately, the ordinary operational decisions made by teams can become a viable attack path in a matter of minutes.

By adopting a CTEM approach, organizations can close these paths faster than AI can compute them, effectively reclaiming the window of exploitation and staying ahead of attackers in the era of AI.