Identify Potential Vulnerabilities Early Using an Agentic AI-Driven Approach.
Learn Morexhield.tech is the next-generation security intelligence layer from SkureCloud.com, designed to quantify risk before formal VAPT. By reasoning across architecture, code, cloud configuration, and identity boundaries, it discovers attack paths early and prioritizes remediation with probabilistic risk scoring.
Multiple cooperating agents work over a shared Security Knowledge Graph to continuously reason about exposure, dependencies, and remediation impact.
Risk is quantified using probabilistic attack-path modeling and impact weighting, producing a normalized score for fast prioritization.
R_system = Σ P(A_i) × I(A_i)
P(A_i) = Π P(s_j | s_{j-1})
R_norm = 100 × (1 − e^(−R_system))
Continuously maps cloud assets, identities, and exposed services before deployment.
Correlates libraries, services, and misconfigurations to reveal hidden risk chains.
Bayesian learning and attack-path math prioritize the highest-impact fixes.
Validated on AWS architectures with measurable risk reduction and ROI.
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