Graph-powered Identity and Access Management (IAM) analysis is emerging as a crucial tool in cybersecurity to address the complexities of permissions, roles, and access rights that traditional static IAM tools often miss. These traditional tools can leave organizations vulnerable to attacks through overlooked accounts, indirect permissions, and misconfigured roles, often exploited by attackers for lateral movement within systems. Graph databases enhance visibility by treating every user, role, device, app, and resource as interconnected nodes, allowing for clear mapping of direct and indirect permission paths, detection of dormant accounts, and identification of hidden gateways between systems. This approach helps security teams simulate potential breaches through "what-if" scenarios, thereby tightening access paths and preventing privilege escalation. By employing graph algorithms like Betweenness Centrality and Degree Centrality, organizations can effectively analyze and mitigate risks associated with over-permissioned identities and potential insider threats, ultimately reducing the likelihood of breaches such as the 2023 LastPass incident. As IAM systems need to operate in real-time, graph databases provide the necessary context and flexibility to adapt to the dynamic nature of access management, helping prevent costly security breaches.