Abstract
Enterprise security environments have become increasingly complex due to hybrid work models, multiple access paths such as wireless local area network (WLAN), virtual private network (VPN), and Zero Trust Network Access (ZTNA), diverse device types, and evolving threat patterns. Modern firewalls and access platforms can consume contextual signals such as identity, device posture, time, and location, but commonly rely on static, rule-based enforcement and one-time access decisions. Proposed herein is an agentic artificial intelligence (AI) framework that continuously evaluates multidimensional access context and dynamically generates or adapts firewall security posture during a session lifecycle. The system derives session-level risk from composite context, including identity, device trust, access type, time, behavioral telemetry, and location as a supporting signal, and uses that risk to select and enforce firewall policy profiles. Cooperating stateful agents perform context collection, session modeling, risk reasoning, conflict resolution, policy selection, enforcement orchestration, monitoring, and explainability. By decoupling context reasoning from enforcement platforms, the system supports consistent, adaptive firewall enforcement across campus, VPN, and ZTNA environments while interoperating with existing infrastructure.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
M M, Niranjan, "CONTEXT-DRIVEN FIREWALL POLICY PROFILE GENERATION", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10346