N-Day Vulnerabilities: Minimizing Risk With Self-Learning AI
See how Darktrace PREVENT, a self-learning AI program, can help your security team measure risk & address N-Day vulnerabilities before an attack occurs.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Oakley Cox
Director of Product
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27
Jul 2022
Responding to the latest critical vulnerability has become a regular routine in the daily life of cyber security professionals.
In the last two years, there has been a carousel of patches for vulnerabilities affecting email servers (ProxyLogon), remote working infrastructure (Atlassian Confluence), third party tools (Kaseya), and supply chain software (Log4j).
In the days following the public disclosure of such vulnerabilities, any associated exploit is referred to as an “N-day”. The release of a patch marks day 1, but over the following days any unpatched systems are at risk of attack from exploits which target the vulnerability. This contrasts with zero-day attacks, which exploit vulnerabilities for which no patch is available, often because knowledge of the vulnerability isn’t yet in the public domain.
N-days occupy a unique space in cyber risk analysis. Headline-grabbing zero-day attacks have the potential to be high impact, but in reality such attacks are rare and have a low likelihood. A more common cyber-attack, using commodity malware which has been well documented in the wild, may have a high likelihood but will have a low impact when faced with a mature security stack. But in the hours and days following the publication of a new vulnerability, there is a high likelihood of a high impact attack against an organization which makes use of a new exploit.
Table 1: A potential qualitative risk analysis comparing three cyber risks: a threat group targets an organization using either commodity malware, a zero-day exploit, or by leveraging an N-day vulnerability.
After a critical vulnerability is published, security teams battle against time and resourcing constraints to apply the appropriate patch or patches, all the while trying to protect assets without a playbook of what an attack may look like. Darktrace has found that 85% of high-risk vulnerabilities are not patched within one week and 70% remain unpatched after a month. In the meantime, threat groups have become armed with a new attack method: an N-day exploit.
In their latest research, Darktrace’s Inside the SOC team detail how the techniques used by Self-Learning AI to detect zero-day attacks can also be leveraged by organizations to Detect and Respond to N-day attacks.
But with Darktrace PREVENT, defenders can go one step further, enabling security teams to harden defenses before the next attack vector is even published.
The Darktrace PREVENT product family empowers defenders to model likely attack paths, intelligently prioritize critical servers or highly exposed people in the organization, and test vulnerable pathways by emulating real-world attacks. Darktrace PREVENT then feeds data back into Darktrace DETECT + RESPOND to harden defenses around critical attack paths or assets and further enhance cyber resilience. For example, if Darktrace PREVENT discovers that a critical database is serving high-risk users, it can feed that information back into Darktrace DETECT, which in turn increases the level of scrutiny around that asset.
Figure 1: Visualising Darktrace’s technology vision of a Cyber AI Loop: four interconnected AI engines continuously enhancing each other’s capabilities.
While Darktrace DETECT + RESPOND wrap what amounts to an ‘AI safety blanket’ around vulnerable assets and attack paths, Darktrace PREVENT presents prioritized recommendations for long term risk mitigation. Stretched security teams therefore know, based on Darktrace’s deep and evolving understanding of the entire business, where to focus their time and resources in order to reduce risk to the greatest extent.
As a result, when the next N-day vulnerability comes around, defenders have the confidence that any prospective impact has already been minimized and the potential cyber risk is low.
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Pre-CVE Threat Detection: 10 Examples Identifying Malicious Activity Prior to Public Disclosure of a Vulnerability
Darktrace leverages AI-driven anomaly detection to identify cyber threats before public CVE disclosures. By analyzing behavioral patterns, Darktrace can help organizations detect and contain zero-day exploits early. This proactive approach strengthens cybersecurity posture against nation-state actors, ransomware gangs, and evolving threats across the threat landscape.
Vendors are scrambling to compare MTTD metrics laid out in the latest MITRE Engenuity ATT&CK® Evaluations. But this analysis is reductive, ignoring the fact that in cybersecurity, there are far more metrics that matter.
Global Telecom Provider: Powering and Protecting the World's Data Giants
This global leader plays a critical role in keeping the world connected. The company works with some of the largest and most influential public and private organizations in the world to enable ultra-fast data transmission.
Safeguarding the systems that keep the world connected
Standing at the forefront of global connectivity, this industry leader designs and manages large-scale communications systems that power the world’s most data-intensive enterprises – including social media giants, hyperscale cloud providers, and major data center operators. Given the scale, confidentiality, and sensitivity of the systems and data it helps transport, the company faces complex cybersecurity challenges.
Protecting sensitive customer data
Most of the organization’s projects are custom-designed and highly proprietary, making data privacy and Intellectual Property (IP) protection critical to maintaining trust and confidentiality with customers. In an industry where every competitor knows the landscape intimately, any loss of data could cause significant damage.
International security implications
The company faces a broad range of advanced cyber threats – from corporate espionage and supply chain risks to cyber-physical attacks on critical infrastructure. Its international footprint adds complexity, including cross-border regulatory compliance. A successful attack could disrupt business, compromise IP, or trigger wider consequences like disruptions to international data transfers and other critical services.
The global leader works closely with communities to anticipate threats that could impact the global communications network at large.
In this environment, cybersecurity is a foundation for international trust,” said the organization’s CISO.
Building a resilient cybersecurity strategy from the ground up
The CISO had the rare opportunity to build the IT and cybersecurity infrastructure from scratch. "Initially, we bought what everyone else buys,” referencing the traditional mix of firewalls, routers, and antivirus tools. “But I knew we needed to do more.”
Self-Learning AI – “the missing piece”
With solid perimeter defenses in place, the security team sought deeper protection inside the network. Darktrace’s Self-Learning AI stood out. “Unlike other solutions, Darktrace’s AI looks beyond known threat signatures, learning what’s normal for our environment and flagging what’s not. That was the missing piece – something that could help us even when everything else failed.”
A solution and partnership that delivered
The CISO said he appreciated the ability to observe Darktrace in action before full deployment, noting that the Darktrace team was there every step of the way, providing guidance and expertise to ensure he got the most out of his investment.
Partnership was especially valuable given the company’s explosive 400% growth over the last six years. As resources were stretched and priorities shifted, “Darktrace remained patient and responsive. We’re slow and methodical, but the Darktrace support team was phenomenal, never losing momentum and earning our trust.”
The CISO appreciates how, as a unified cybersecurity platform, Darktrace has an intuitive user interface, which makes it easier for his team to investigate alerts visually, even without deep technical expertise.
Advancing defenses while impacting the bottom line
A 24/7 “safety net”
The fact that this company has never been hacked is the clearest proof it made the right decision with Darktrace, said the CISO. Initially rolled out in Human Confirmation Mode, meaning it would not take autonomous action without explicit approval from the security team, Darktrace immediately uncovered threats and anomalies that other tools had missed.
Darktrace acts as a must-have safety net—ready to step in when other tools fall short,” said the CISO.
From monitoring internal behavior and identifying unusual attack patterns, to autonomously neutralizing threats after hours, the platform provides peace of mind in a high-stakes industry. “Darktrace is my dark horse – the thing I have in my back pocket if everything else fails. It’s here to save the day, save my company, and maybe even save my career.”
Autonomous capabilities free up time for skilled analysts
Darktrace’s AI-powered detection and response capabilities are deeply embedded in the team’s day-to-day operations, autonomously investigating and responding to the majority of potential threats. Cyber AI Analyst conducted a total of 2,776 total investigations within three months, averaging just 12 minutes to autonomously investigate an incident. Of those 2,776 investigations, Darktrace resolved 2,671 (96%) autonomously and escalated only 105 (4%) to analysts. Darktrace has dramatically reduced alert fatigue and freed up analysts to focus on what really matters, saving the security team 486 analyst hours on investigations within a 20-day period.
From noise to actionable insight
Darktrace delivers meaningful data and meaningful alerts. “If Darktrace escalates an incident, we drop everything and work on that. We trust in Darktrace.” When analysts do need to investigate an incident, Darktrace’s forensic logs and guided remediation suggestions have slashed the time analysts spend on investigations by four to five times.
Stronger security. Lower cost.
The CISO says, “Darktrace is a money-saver for our organization, making continued investments an easy sell to the CEO and the board.” When he found himself down a resource after a member of the security team left the organization, the CISO turned to Darktrace Managed Threat Detection and Response services for 24/7 expert support. “It was a no brainer. We got better coverage, higher skill levels, and around-the-clock support – all for less than what we would pay to employ a single analyst.”
Scaling securely into the future
Securing networks in motion
The organization is preparing to scale both its operations and security posture across existing distributed, mobile and deployable communications networks that historically have been disconnected. Some of these networks are in constant motion and operating in some of the world’s most volatile regions. “Darktrace will act as an autonomous defender, monitoring for anomalous behavior and intervening, when necessary, especially during those dangerous times when an asset ‘goes dark’ and becomes disconnected from the broader network,” said the CISO.
Applying AI strategically
As the organization continues to evaluate where and how to apply AI, its emphasis will be on technologies that can act independently to contain threats – especially in environments where human response may be delayed. “It’s about using the right kind of AI for the right challenge. That’s why we’re investing in Darktrace, with tools that can adapt and learn even in isolation and provide real-time protection wherever we operate.”
Introducing the AI Maturity Model for Cybersecurity
AI adoption in cybersecurity: Beyond the hype
Security operations today face a paradox. On one hand, artificial intelligence (AI) promises sweeping transformation from automating routine tasks to augmenting threat detection and response. On the other hand, security leaders are under immense pressure to separate meaningful innovation from vendor hype.
To help CISOs and security teams navigate this landscape, we’ve developed the most in-depth and actionable AI Maturity Model in the industry. Built in collaboration with AI and cybersecurity experts, this framework provides a structured path to understanding, measuring, and advancing AI adoption across the security lifecycle.
There’s no shortage of AI solutions, but there is a shortage of clarity and understanding of AI uses cases.
In fact, Gartner estimates that “by 2027, over 40% of Agentic AI projects will be canceled due to escalating costs, unclear business value, or inadequate risk controls. Teams are experimenting, but many aren’t seeing meaningful outcomes. The need for a standardized way to evaluate progress and make informed investments has never been greater.
That’s why we created the AI Security Maturity Model, a strategic framework that:
Defines five clear levels of AI maturity, from manual processes (L0) to full AI Delegation (L4)
Delineating the outcomes derived between Agentic GenAI and Specialized AI Agent Systems
Applies across core functions such as risk management, threat detection, alert triage, and incident response
Links AI maturity to real-world outcomes like reduced risk, improved efficiency, and scalable operations
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How is maturity assessed in this model?
The AI Maturity Model for Cybersecurity is grounded in operational insights from nearly 10,000 global deployments of Darktrace's Self-Learning AI and Cyber AI Analyst. Rather than relying on abstract theory or vendor benchmarks, the model reflects what security teams are actually doing, where AI is being adopted, how it's being used, and what outcomes it’s delivering.
This real-world foundation allows the model to offer a practical, experience-based view of AI maturity. It helps teams assess their current state and identify realistic next steps based on how organizations like theirs are evolving.
Why Darktrace?
AI has been central to Darktrace’s mission since its inception in 2013, not just as a feature, but the foundation. With over a decade of experience building and deploying AI in real-world security environments, we’ve learned where it works, where it doesn’t, and how to get the most value from it. This model reflects that insight, helping security leaders find the right path forward for their people, processes, and tools
Security teams today are asking big, important questions:
What should we actually use AI for?
How are other teams using it — and what’s working?
What are vendors offering, and what’s just hype?
Will AI ever replace people in the SOC?
These questions are valid, and they’re not always easy to answer. That’s why we created this model: to help security leaders move past buzzwords and build a clear, realistic plan for applying AI across the SOC.
The structure: From experimentation to autonomy
The model outlines five levels of maturity :
L0 – Manual Operations: Processes are mostly manual with limited automation of some tasks.
L1 – Automation Rules: Manually maintained or externally-sourced automation rules and logic are used wherever possible.
L2 – AI Assistance: AI assists research but is not trusted to make good decisions. This includes GenAI agents requiring manual oversight for errors.
L3 – AI Collaboration: Specialized cybersecurity AI agent systems with business technology context are trusted with specific tasks and decisions. GenAI has limited uses where errors are acceptable.
L4 – AI Delegation: Specialized AI agent systems with far wider business operations and impact context perform most cybersecurity tasks and decisions independently, with only high-level oversight needed.
Each level reflects a shift, not only in technology, but in people and processes. As AI matures, analysts evolve from executors to strategic overseers.
Strategic benefits for security leaders
The maturity model isn’t just about technology adoption it’s about aligning AI investments with measurable operational outcomes. Here’s what it enables:
SOC fatigue is real, and AI can help
Most teams still struggle with alert volume, investigation delays, and reactive processes. AI adoption is inconsistent and often siloed. When integrated well, AI can make a meaningful difference in making security teams more effective
GenAI is error prone, requiring strong human oversight
While there is a lot of hype around GenAI agentic systems, teams will need to account for inaccuracy and hallucination in Agentic GenAI systems.
AI’s real value lies in progression
The biggest gains don’t come from isolated use cases, but from integrating AI across the lifecycle, from preparation through detection to containment and recovery.
Trust and oversight are key initially but evolves in later levels
Early-stage adoption keeps humans fully in control. By L3 and L4, AI systems act independently within defined bounds, freeing humans for strategic oversight.
People’s roles shift meaningfully
As AI matures, analyst roles consolidate and elevate from labor intensive task execution to high-value decision-making, focusing on critical, high business impact activities, improving processes and AI governance.
Outcome, not hype, defines maturity
AI maturity isn’t about tech presence, it’s about measurable impact on risk reduction, response time, and operational resilience.
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Outcomes across the AI Security Maturity Model
The Security Organization experiences an evolutionof cybersecurity outcomes as teams progress from manual operations to AI delegation. Each level represents a step-change in efficiency, accuracy, and strategic value.
L0 – Manual Operations
At this stage, analysts manually handle triage, investigation, patching, and reporting manually using basic, non-automated tools. The result is reactive, labor-intensive operations where most alerts go uninvestigated and risk management remains inconsistent.
L1 – Automation Rules
At this stage, analysts manage rule-based automation tools like SOAR and XDR, which offer some efficiency gains but still require constant tuning. Operations remain constrained by human bandwidth and predefined workflows.
L2 – AI Assistance
At this stage, AI assists with research, summarization, and triage, reducing analyst workload but requiring close oversight due to potential errors. Detection improves, but trust in autonomous decision-making remains limited.
L3 – AI Collaboration
At this stage, AI performs full investigations and recommends actions, while analysts focus on high-risk decisions and refining detection strategies. Purpose-built agentic AI systems with business context are trusted with specific tasks, improving precision and prioritization.
L4 – AI Delegation
At this stage, Specialized AI Agent Systems performs most security tasks independently at machine speed, while human teams provide high-level strategic oversight. This means the highest time and effort commitment activities by the human security team is focused on proactive activities while AI handles routine cybersecurity tasks
Specialized AI Agent Systems operate with deep business context including impact context to drive fast, effective decisions.
Join the webinar
Get a look at the minds shaping this model by joining our upcoming webinar using this link. We’ll walk through real use cases, share lessons learned from the field, and show how security teams are navigating the path to operational AI safely, strategically, and successfully.
