Historically, the main limitation for cyber adversaries was skilled human labor. Reconnaissance took weeks, exploit development required high expertise, and successful phishing needed writing skills. Every intrusion attempt consumed operator time, often making organizations unattractive targets.
However, the emergence of offensive artificial intelligence has eliminated this limitation, simultaneously depriving medium-sized organizations of a certain form of protection they might not have known about.
What Has Changed in Attacks
Every stage of an attack is now automated and cheaper. A new vulnerability can be turned into a weapon in minutes instead of weeks. Reconnaissance is conducted continuously and autonomously. Phishing messages are generated individually for each target, in the local language, referencing real suppliers and people. The most significant change has been the collapse of the qualification threshold: offensive capabilities are now rented, not acquired through training.
This is reflected in incident data. According to the Unit 42 Global Incident Response Report for 2026, in the fastest quarter of cases, the time from initial access to confirmed data exfiltration was 72 minutes, demonstrating approximately a fourfold reduction in attack timelines compared to the previous year. Furthermore, the CrowdStrike Global Threat Report for 2026 indicates that the average time to exit isolation and move laterally is 29 minutes. Unit 42 also finds that about 65% of initial access is based on credentials, not CVEs (publicly cataloged software vulnerabilities): this refers to actual credentials, stolen tokens, cloud misconfigurations, and trust relationships that do not receive criticality ratings.
The Problem Is No Longer Exclusively Corporate
Automation does not select targets based on revenue. When reconnaissance becomes free and constant, the reach extends to everyone simultaneously: it could be a mid-market manufacturer, a private hospital group, a municipal utility, or a supplier with forty employees at a large bank, and all are in the line of fire alongside large corporations.
Thus, stealth is no longer a measure of defense: being small often means that a smaller team faces the same machine. Smaller organizations are attacked because of whom they serve, as a supplier with extended access to a larger client represents a cheaper path to penetration than that client's own perimeter.
The Defender's Structural Flaw
Comparing the two sides shows that the imbalance is not in effort or budget. The attacker conducts one continuous, integrated operation with a single goal and no possibility of disconnection. The defender uses six or more tools, each seeing only part of the picture: a scanner, Endpoint Detection and Response (EDR), Cloud Security Posture Management (CSPM), Identity and Access Management (IAM), Security Information and Event Management (SIEM), and Governance, Risk, and Compliance (GRC). Each tool is competent within its domain, but none sees the path between them—and that is where the intrusion occurs.
Validation follows the same pattern: quarterly, scope-limited penetration testing leaves most of the year unnoticed. Prioritization is even worse, as the average infrastructure contains tens of thousands of identified issues, sorted by severity in isolation, while only a small fraction of them lies on the path an attacker can take. The seventh tool adds findings, not solutions.
Restoring Balance
Parity is achieved not by increasing scanning volume. It is achieved by adopting the attacker's model: building a single graph of all assets, identities, credentials, and vulnerabilities, and traversing it as the adversary would, so that risk becomes a property of the path, not just a found issue. It is necessary to prove, not assume, that a chain is exploitable before it enters the remediation queue. And this cycle must run continuously, not quarterly.
This aligns with Gartner's concept of continuous threat management, but most CTEM programs stop at one word. Manual execution of scoping, detection, prioritization, validation, and mobilization stages is possible. However, their continuous execution requires automation, which is provided by agentic AI.
RedRok was created to fill this gap. Four agents, each responsible for a different view of the infrastructure, combine their findings into a single verified graph, thus turning a path that no single agent could see into one proven chain. Since chains converge on common infrastructure, such as a directory service or host bastion, RedRok calculates bottlenecks: in a typical infrastructure, three fixes eliminate nine out of eleven proven chains. Fixed remediation throughput then provides a disproportionate reduction in actual risk, summarized into a single status metric understandable by the board of directors.
Attackers have automated their side of this battle. Defenders can do the same, and until they do, the imbalance will only grow.