When patching isn’t fast enough, NDR helps contain the next generation of threats.
If you’ve been following advances in AI, you know that the exploit window, the short buffer that organizations relied on to patch and protect vulnerabilities after they were made public, is rapidly closing.
Anthropic’s new model, Claude Mythos and his project Glasswing, showed that finding exploitable vulnerabilities and subtle cracks in defenses in operating systems and browsers, a task that once took weeks for experts, can be done in minutes using AI. As a result, the chance of a patch window has become almost zero. The situation is so critical that Treasury Secretary Scott Bessent and Federal Reserve Chairman Jerome Powell recently convened an emergency meeting with the CEOs of major U.S. financial institutions to discuss potential risks. The point was simple and clear. The proliferation of AI capabilities has transformed the risk profile and had a significant impact on the stability and integrity of organizations across industries.
The myth also highlights the gap between discovery and restoration. This effortlessly exceeded human expertise, solving a complex enterprise network simulation that would have taken over 10 hours of professional programming skills. Its findings also uncovered issues with decades-old software that had been missed by thousands of security reviews.
From myth to era of violation of assumptions
Mythos isn’t the only AI model that can find vulnerabilities this quickly. Other parties have found themselves using more basic LLMs.
If your company uses any type of software, you should assume that it probably contains thousands of these unknown vulnerabilities waiting to be discovered and exploited by AI-assisted discovery. This is not a failure of the security team. Rather, it is a structural result of 30 years of accumulated software complexity and dramatic advances in offensive AI capabilities.
“Patch faster” or “patch better” is no longer enough, as near-zero exploit windows have become the norm. Security teams will need new playbooks based on what-if breach models. This means breaches will happen, and detecting them when they occur and stopping them at scale is paramount. These results are determined in real time over the network.
How to implement a breach assumption model into daily operations
The breach assumption model has three operational requirements, each of which uses automated methods designed to reduce time to containment.
Detect post-breach behavior before the threat spreads throughout the enterprise Reconstruct the complete attack chain as soon as possible Contain the threat quickly and limit the blast radius
In practice, this method of containment requires:
Visualize containment as a scoreboard
Prioritize reducing mean time to containment (MTTC) to limit damage while maintaining surveillance detection and response metrics (MTTD and MTTR). As AI accelerates exploitation and reshapes attack techniques, the speed with which threats can be accurately identified, contained, and resolved becomes increasingly important. Compressing MTTC starts with real-time, comprehensive network visibility. This allows the SOC to detect post-compromise behavior, determine the detonation radius, and interrupt the event before it spreads further.
Monitoring AI-preferred methods
Autonomous AI attacks increasingly use sophisticated techniques to evade detection, such as living-off-the-land (LOTL) techniques that hide malicious activity within legitimate tools and processes. Network detection and response (NDR) platforms play a critical role in identifying these subtle indicators of compromise. This is done by continuously monitoring network traffic for anomalous behavior. Signs of such activity can show up as unusual SMB administrative shares, NTLM with Kerberos expected, or new RDP/WMI/DCOM pivots, all of which can indicate lateral movement across the network.
Advanced NDR platforms can also leverage LOTL techniques to maintain command and control communications and detect attackers stealing data while avoiding generating alarms. Indicators of command and control can appear as beacon-like connection patterns, rare JA3/JA4 and SNI pairs, high-entropy DNS, or unauthorized DoH or DoT. Anomalies such as off-hours uploads, upload/download asymmetry, first-time destinations (such as S3, Blob, GCS, or new CDN), compression before output, and the presence of tunnels or VPNs to new destinations can indicate an exfiltration.
Software inventory automation and maintenance
Many organizations still lack a real-time, accurate inventory of their software and struggle to understand how their assets connect and communicate. This gap gives the enemy an opening. By automating asset inventory and mapping, organizations can understand their exposure, respond more quickly to new threats, and reduce the window of opportunity for vulnerabilities to be exploited.
Correlating and reconstructing attack chains
Once a breach is detected, it is important to quickly understand its scope, especially since AI-driven threats are too fast for manual analysis. The once painstaking process of reconstructing events must be automated and delivered in real-time.
Corelight Investigator, part of the company’s Open NDR platform, automatically correlates alerts with network activity to help reconstruct a detailed timeline of an attack. This makes it easy to automate response workflows with your own systems and improve resilience against these attacks.
Containment automation
Advances in detection and attack reconstruction should facilitate definitive and reliable containment. Limiting the spread of threats is the third step in the breach assumption model and turns data and insights into tangible protection. By incorporating automated containment into network defense workflows, you can reduce the risk of rapidly changing threats escalating into widespread incidents.
Towards the future of Mythos-enabled security
Claude Mythos and other AI models are rapidly upending long-standing practices in cybersecurity. Preparing for this dynamic situation, in part, means building adaptive defense layers that help accelerate defenses against adversarial AI.
Monitoring: Maintain continuous visibility of your network and automate detection to identify threats early. Assume a breach: Operate as if a breach will occur and focus on rapid response and containment. Protect: Protect your trusted ecosystem by increasing control of where AI-driven attacks can cause the most damage. Build a “Mythos-enabled” security program from the Cloud Security Alliance. Sharpen: Continually refine our playbooks and response strategies to combat evolving threats.
Corelight network discovery and response
Discover new attack techniques with Corelight’s Open NDR platform. With comprehensive network visibility and deep behavioral analytics, Corelight is designed to help SOCs detect advanced AI-powered threats faster and respond to incidents before they escalate. For more information, visit corelight.com/elitedefense.
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