How AI is reshaping attack path analysis
Cybersecurity teams are overwhelmed with data and short on clarity, while adversaries use AI to move faster and operate at unprecedented scale.
Most organizations collect enormous volumes of findings: vulnerabilities, misconfigurations, penetration test results, detection gaps, threat intelligence, and control assessments. Individually, these data points may be accurate, but they don’t always reflect the big picture. As attackers increasingly leverage AI to rapidly identify weaknesses and chain exploits, defenders must keep pace by leveraging AI to rapidly identify and defend against these weaknesses. However, security teams often struggle to determine what matters most, how it could realistically be exploited, and what to fix first.
This is where frameworks like MITRE ATT&CK have proven indispensable. While the core use case remains the same, AI is beginning to fundamentally change how these frameworks are used in practice.
Rather than treating MITRE ATT&CK as a static reference or reporting artifact, forward-looking teams are applying AI to rapidly analyze coverage, surface gaps, and model realistic attack paths. In addition to better testing, this results in clearer, faster decision-making that helps defenders scale their efforts as quickly as attackers scale theirs.
For teams looking to move from an overwhelming volume of findings to actionable insight, this often starts with consolidating manual testing results and automated security data into a single consolidated view within an exposure assessment platform, such as PlexTrac.
The MITRE heat map as a lens, not a ledger
At its core, the MITRE ATT&CK framework organizes adversary behavior into two essential dimensions: tactics (the “why”) and techniques (the “how”). This structure gives security teams a shared language for understanding how real attackers operate across the full lifecycle of an intrusion.
A MITRE heat map builds on this by visually representing how an organization’s controls, tests, detections, or findings align to those tactics and techniques. Heat maps have traditionally been used for reporting to summarize coverage or gaps at a point in time. But when paired with AI, they become a powerful, dynamic analytical tool.
This approach is most effective when heat maps are built from a unified data set that includes both automated and real-world manual testing results, ensuring coverage reflects how attackers actually operate, not just what tools can detect.
Revealing blind spots that humans miss
One of the most immediate benefits of a MITRE heat map is gap analysis. Areas with little or no coverage stand out visually, forcing a necessary question: Are we truly covered, or have we simply never tested here?
For penetration testers and red teams, this perspective is invaluable. Attackers do not distribute their effort evenly. They look for the fastest, least-resisted path to impact. A heat map that highlights sparse testing or detection coverage can guide testers toward techniques that are more likely to succeed.
AI helps by identifying patterns across engagements, environments, and historical data. AI reduces manual efforts by automatically surfacing under-tested techniques and coverage gaps, helping teams more quickly identify true areas of strength and weakness.
Helping leadership make sense of the data
Heat maps are an invaluable tool for communication at a high level. CISOs and security leaders are expected to make investment and resource decisions based on highly technical inputs, often distilled into spreadsheets or reports that lack clarity.
MITRE heat maps translate this complexity into a visual narrative any stakeholder can easily understand. They allow leadership to see where defensive maturity is strong, where risk clusters, and where blind spots exist. This guides more informed conversations around budget, staffing, tooling, and ongoing assessments with quantifiable, visual evidence.
When AI is applied, these visuals evolve beyond static snapshots. They can incorporate trends, confidence scoring, and predictive insights, which helps leaders understand where the organization stands today and where risk is likely to emerge next.
Moving beyond lists: The power of attack path visualization
While heat maps answer the question “Where are we weak?”, they do not always explain “How could this actually be exploited?” This is where attack path modeling comes into play.
Most security teams are accustomed to reviewing long lists of findings. While necessary, these lists rarely reflect how attackers think. Real-world exploitations are chains of actions that constantly build upon one another. Attack paths reframe security analysis around how attackers actually leverage weaknesses.
Seeing relationships, not just issues
Attack path visualizations use node-based diagrams to represent entities such as systems, credentials, applications, and networks. The links between these nodes represent the relationships or potential exploit steps.
This approach allows security professionals to see how individual vulnerabilities, misconfigurations, or weak controls can be chained together. A low-severity issue on its own may seem insignificant, until you can visually see how it becomes the entry point to a path that leads to domain-wide impact.
AI plays a crucial role here by correlating findings that would otherwise remain disconnected. It can infer plausible paths based on environment context, historical attack patterns, and known adversary behaviors. AI helps teams quickly identify and focus on the truly exploitable.
Identifying choke points
One of the most valuable outcomes of attack path analysis is choke point identification. In many environments, multiple attack paths converge on a small number of critical nodes or relationships, such as overly permissive service accounts or weak identity controls.
Fixing these choke points can collapse entire categories of attack paths at once. This shifts remediation from a reactive, whack-a-mole exercise to a strategic risk-reduction effort.
This insight enables prioritization based on impact, not just volume, and supports more data-backed decisions about where to invest remediation resources.
The future of AI for security risk awareness
The combined use of AI-driven MITRE heat maps and attack path visualization creates value across the organization.
For testers and technical teams, it improves efficiency and effectiveness. Effort is guided toward areas of highest likelihood and highest impact. Testing becomes more adversary-focused, more realistic, and ultimately more valuable to the organization.
For leadership, it provides clarity so risk is visibly understood, contextualized and able to be used for business decisions.
AI amplifies human expertise and helps teams move faster and more efficiently. Cybersecurity teams must learn to apply AI to gain clarity into complex data, identify meaningful patterns, and visualize real-world exploitation.
This clarity is essential to shift from reactive defense to getting ahead of exploits with intentional risk management.
Cybersecurity teams that apply AI effectively can move from fragmented findings to coherent insight, shifting from reactive defense to intentional risk management. In an industry defined by complexity and uncertainty, the ability to clearly visualize risk may be one of the most powerful defensive advantages available.
To see how PlexTrac helps teams consolidate manual testing and automated security data to support MITRE mapping and attack path visualization, learn more or book a demo.