CyberSentinal: A Unified Framework for Automated Vulnerability and Exposure Assessment, Multi-Vector Attack Surface Mapping, Passive Packet Telemetry Analysis, and Cross-Layer Risk Correlation

Modern digital enterprise architectures and hybrid multi-cloud environments expose an expanding, complex attack surface vulnerable to multi-stage cyber intrusions. Identifying vulnerabilities, validating defensive postures, and mitigating exposures across diverse architectural tiers present significant operational complexity for security analysts. Conventional security tools operate in isolated technological silos: network port scanners produce uncontextualized lists of exposed sockets, HTTP header checkers assess web endpoints without transport telemetry, and passive packet capture utilities produce vast streams of unparsed raw payloads. Consequently, security teams must manually correlate disparate findings, leading to analytical delays, elevated cognitive burden, and potential threat oversights. This paper presents CyberSentinal, a unified, modular, web-based vulnerability and exposure assessment framework designed for authorized security evaluations. CyberSentinal integrates active target reconnaissance, TCP port and service version scanning via Nmap, HTTP defensive security header compliance auditing, client-side credential complexity analysis, privacy-preserving bounded metadata-only packet inspection via Scapy, and multi-vector Attack Surface Mapping (ASM) within a coherent analytical pipeline. Crucially, the framework introduces an algorithmic Cross-Layer Cyber Risk Correlation Engine that synthesizes perimeter vulnerability indicators with passive transport-layer telemetry. The engine executes deterministic correlation rules to identify compounded threat vectors, computes a normalized 0–100 composite risk score categorized into standardized severity tiers (Low, Medium, High, Critical), and reconstructs multi-stage potential attack trajectories.