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Official GitHub Repository: https://github.com/14mb1v45h/CYBERDUDEBIVASH-AI-INTEGRITY-SCANNER-v2026
Premium Enterprise Version & Support: https://www.cyberdudebivash.com/apps-products/
Built by CyberDudeBivash Pvt Ltd — Global Cybersecurity Authority since 2025
TL;DR
On December 29, 2025, CyberDudeBivash Pvt Ltd officially launches AI Integrity Scanner v2026.1 — the first Zero-Trust, local-only Python tool designed specifically for cybersecurity professionals to detect AI-generated text, hallucinations, and deepfake intelligence risks. Free open-source version with CLI + GUI now live on GitHub. Premium enterprise edition with advanced ML models and API coming Q1 2026. Download today and defend against the weaponization of AI in 2026 threat landscapes.
The year 2026 marks a pivotal inflection point in cyber warfare: artificial intelligence is no longer just a productivity tool — it has become a primary weapon for threat actors worldwide. State-sponsored groups, ransomware syndicates, and hacktivist collectives are increasingly leveraging large language models to generate hyper-realistic phishing emails, fake threat intelligence reports, deepfake executive voices, and hallucinated vulnerability disclosures that mislead SOC teams and trigger costly false-positive responses.According to multiple 2025-2026 forecasts from Mandiant, CrowdStrike, and Microsoft's Digital Defense Report, AI-assisted attacks rose 400% in 2025 alone. Business Email Compromise (BEC) campaigns now routinely use LLM-generated narratives tailored to victim psychology. Fake CVE announcements flood underground forums, causing defenders to chase non-existent patches. Deepfake audio calls impersonating CISOs authorize fraudulent wire transfers worth millions.Perhaps the most insidious development is the emergence of "hallucinated intelligence" — AI models fabricating IOCs, TTPs, and exploit details that appear authoritative but lead investigations down dead ends, exhausting analyst resources and creating alert fatigue at scale.CyberDudeBivash Pvt Ltd, founded by Bivash Kumar Nayak , identified this critical gap in defensive tooling during Q4 2025 threat hunting operations. Existing commercial detectors were cloud-dependent, privacy-invasive, and not optimized for cybersecurity workflows. No tool existed that could run in air-gapped environments while providing actionable integrity scores tied to threat mitigation playbooks.Thus, AI Integrity Scanner v2026.1 was born — a deliberately local, Zero-Trust, Python-native solution built exclusively for defenders, analysts, journalists, and enterprises who cannot afford to trust unverified AI outputs in high-stakes environments.[This section continues with covering geopolitical AI weaponization, regulatory landscape (EU AI Act 2026 amendments), enterprise case studies of AI abuse, statistical trends from 2025 breach data, and why local scanning is now mandatory for critical infrastructure.]
The scanner employs a multi-layered statistical and semantic analysis pipeline entirely in pure Python with minimal dependencies (numpy, scipy) to ensure portability and Zero-Trust execution.Core detection modules:
[This section expands with full mathematical formulas, code walkthroughs line-by-line from ai_integrity_scanner.py, comparison tables vs commercial tools, limitations discussion, planned torch/BERT integration roadmap for v2026.2, performance benchmarks on 2025 datasets, and OWASP security review of the codebase itself.]
We tested the scanner on five representative samples:
Full JSON reports and screenshots included.[detailed result breakdowns, interpretation guidance, threshold tuning recommendations, false positive/negative analysis.]
Pseudo-YARA style rules for enterprise integration:
rule AI_Generated_Text_Low_Entropy
{
condition:
entropy < 3.5 and burstiness < 0.4
}[ full rule sets, Sigma equivalents, integration with Splunk/ELK.]
Three detailed scenarios: fake ransomware note detection, deepfake CISO call verification, hallucinated CVE chase.
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