Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response

Ercan Erkalkan

Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response

Abstract

Artificial intelligence is increasingly used in cybersecurity operations for intrusion detection, anomaly discovery, malware analysis, and alert triage. However, many AI-based security systems remain weakly interpretable and disconnected from governance and risk-management practices used by security teams. This paper proposes a governance-aware explainable AI framework that transforms model outputs into analyst-readable evidence packages for threat detection and risk-based response. The framework integrates machine-learning detection, SHAP-based feature attribution, MITRE ATT&CK-oriented behavioral hypothesis mapping, contextual risk scoring, and analyst feedback. A reproducible leakage-aware validation was conducted on a public CICIDS2017-derived sample. The upstream file contained 56,661 labeled flows; 12,598 exact duplicate feature/label rows were removed before splitting, leaving 44,063 records. A group-aware held-out split produced zero exact feature-vector overlap between training and test partitions. In the binary benign-versus-attack task, sigmoid-calibrated Random Forest achieved 0.993 accuracy, 0.993 balanced accuracy, 0.995 precision, 0.990 recall, 0.993 F1-score, 0.9992 ROC-AUC, 0.9990 PR-AUC, and 0.0059 Brier score. Original-label outcomes are reported to avoid hiding rare-class behavior. The reported run used mean absolute SHAP values for global attribution and representative local SHAP examples covering true-positive, false-positive, false-negative, and true-negative cases. The risk-response layer is evaluated as an illustrative governance mechanism because the public CSV does not contain organization-specific asset criticality. Results are interpreted as leakage-aware public-sample validation, not as a full official CICIDS2017 benchmark. The framework supports transparent, reproducible, and risk-aware AI adoption in security operations centers while preserving human analyst judgment.

Keywords

References

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Details

Primary Language

English

Subjects

Software and Application Security

Journal Section

Research Article

Authors

Ercan Erkalkan ^*
0000-0001-9259-7112
Türkiye

Publication Date

September 17, 2026

Submission Date

May 11, 2026

Acceptance Date

July 31, 2026

Published in Issue

Year 2026 Volume: 3 Number: 1

IZ

https://izlik.org/JA39XH74US

Cite

RIS / Bibtex

APA

Erkalkan, E. (2026). Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response. Transactions on Computer Science and Applications, 3(1), 14-26. https://izlik.org/JA39XH74US

AMA

1.Erkalkan E. Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response. TCSA. 2026;3(1):14-26. https://izlik.org/JA39XH74US

Chicago

Erkalkan, Ercan. 2026. “Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response”. Transactions on Computer Science and Applications 3 (1): 14-26. https://izlik.org/JA39XH74US.

EndNote

Erkalkan E (September 1, 2026) Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response. Transactions on Computer Science and Applications 3 1 14–26.

IEEE

[1]E. Erkalkan, “Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response”, TCSA, vol. 3, no. 1, pp. 14–26, Sept. 2026, [Online]. Available: https://izlik.org/JA39XH74US

ISNAD

Erkalkan, Ercan. “Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response”. Transactions on Computer Science and Applications 3/1 (September 1, 2026): 14-26. https://izlik.org/JA39XH74US.

JAMA

1.Erkalkan E. Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response. TCSA. 2026;3:14–26.

MLA

Erkalkan, Ercan. “Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response”. Transactions on Computer Science and Applications, vol. 3, no. 1, Sept. 2026, pp. 14-26, https://izlik.org/JA39XH74US.

Vancouver

1.Ercan Erkalkan. Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response. TCSA [Internet]. 2026 Sep. 1;3(1):14-26. Available from: https://izlik.org/JA39XH74US

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Governance-Aware Explainable AI for Cybersecurity Threat Detection and Risk-Based Response