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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63686

AI-GENERATED CYBERSECURITY THREAT INTELLIGENCE REPORTS.

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63686

Volume:

10 |

5 |

May 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63686

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

HEMANTH GOWDA K S

HEMANTH GOWDA K S

MASTER OF COMPUTER APPLICATION

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY

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Mentors

Dr.AMJAD KHAN

Dr.AMJAD KHAN

MASTER OF COMPUTER APPLICATION

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY

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Abstract

In today’s digital environment, the number, speed, and level of advanced cybersecurity threats have increased exponentially, making manual threat intelligence gathering and analysis practices increasingly obsolete. Presently, Security Operations Center (SOC)s and threat analysts are faced with an influx of data from various sources such as vulnerability listings (CVEs), blogger/security vendors’ blogs, social media channels, dark web communities, and RSS feeds. Due to the enormous amount of unstructured data, there is a substantial bottleneck that often leads to missing or delayedanalysisofcriticalintelligence,thushinderingan attack from being thwarted. Moving beyond the limitations of conventional web-scraping tools, this research introduces a generative AI framework designed to decode the nuanced context of emerging cyber threats. By autonomously mapping observed adversarial behaviors against the MITRE ATT&CK framework, the system bridges the gap betweenrawdataandactionableintelligence.The first goal of the research would be to achieve substantial reduction in the Mean Time to Analyze, offering high-fidelity instant information to the security leaders. Oneof thekey aspects of the current research is the ability to use two reporting engines – one creating high-level strategic reports and another offering detailed technical information about Ioc’s. The two-way approach makes sure that SOC professionals and C-level executives stay in sync with each other. All in all, what is achieved here helps move towards proactive measures against threats that are fueled by advanced AI systems.

KEYWORDS

Keywords: Automated multi-source data context-aware natural language processing intelligentseverity scoring & prioritization automatic mapping to MITRE ATT&CK Framework.

Cite This Article

HEMANTH GOWDA K S, Dr.AMJAD KHAN, "AI-GENERATED CYBERSECURITY THREAT INTELLIGENCE REPORTS.," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026. https://doi.org/10.55041/IJSREM63686

Full Article/Conclusion

It is evident that the AI-Generated Cybersecurity Threat Intelligence Reports initiative stands for amajor step forward in terms of cybersecurity technology. This world where there is an increasing imbalance on the attacker side, because of the high numberofattacksandhencelowavailabilityofhuman intelligence to handle them, has found an effective solution in this project. BysuccessfullymergingsophisticatedLargeLanguage Modelswithstrongwebscrapingandautomationtools, the project resolves the key problem of “Information Overload.” This project has clearly proven that AI can not only help in the process of acquiring data but can actually do high-level cognitive tasks such as Summarization, Mapping to Context, and Severity Assignment with overall high accuracy. The execution of this technology enables the Security Operations Centers to refocus. Rather than devoting hours to manually documenting information and consuming countless blog posts, they can now simply rely on the system to provide them with the most pertinent information,sothey can dedicate themselves to decision-making and incident response. The feasibilitystudyhasestablishedthatthistechnologyhas cost feasibility and technological feasibility, and the testing phasehasconfirmedreliability.Ultimately,this project enables organizations to transition from a reactive approach to predictive, which will shake the very foundations of the pernicious cyber threats of the future.

References

1. MITRECorporation.(2023). MITREATT&CK®: DesignandPhilosophy.TheMITRE Corporation. 2. NIST.(2023).Cybersecurity Framework  2.0. NationalInstituteofStandardsandTechnology, U.S.DepartmentofCommerce. 3.. OpenAI.(2023). GPT-4 Technical Report. OpenAI Research. 4. Recorded Future. (2022). The Machine Learning Approach to Threat Intelligence. Recorded Future Whitepapers. SANS Institute. (2022). Automating the SOC: A guide to practical implementation. SANS Reading Room

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Publication Date

5/22/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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