Tadqiqotdan Mahsulotgacha

Akademik tadqiqotlardan boshlab, ishlayotgan DLP tizimi: metodologiya, algoritmlar, sinov natijalari va ilmiy nashrlar

Tadqiqot Metodologiyasi

Design Science Research Methodology (DSRM) doirasida 6 bosqichli tizimli yondashuv. Akademik qat'iylik + sanoat amaliyoti birga.

DLP muammoni aniqlash

Korporativ tarmoqlarda konfidensial ma'lumotlar sizib chiqishi (data exfiltration) holatlarini tahlil va biznes ta'siri

Maqsad va threat model

Insider threat, malicious leak, accidental disclosure ssenariylarini modellashtirish va metrikalar belgilash

Algoritm va arxitektura

Classification engine, content inspection, ML-based sensitive data detection algoritmlarini ishlab chiqish

Prototip va integratsiya

Endpoint agent + tarmoq sensori + boshqaruv konsoli prototipi. SIEM va Active Directory bilan integratsiya

Sinov va validatsiya

Real korxona muhitida sinov: aniqlik, tezlik, false positive darajasi, foydalanuvchi ta'siri o'lchovlari

Nashr va sertifikatsiya

Natijalarni IEEE/ACM jurnallarida nashr etish, patent ariza topshirish, sanoat standartlariga muvofiqlashtirish

DLP Texnologik Yadrosi

Real-time tarmoq trafigini DPI tahlili

Deep Packet Inspection (DPI) orqali HTTP/HTTPS, SMTP, FTP, IM va boshqa protokollarda harakatlanadigan ma'lumotlarni real vaqtda tahlil qilish. TLS inspection, SNI parsing, encrypted traffic fingerprinting

Deep Packet Inspection TLS / SSL Inspection Protocol Decoding Stream Reassembly

Sensitive Data Discovery va klassifikatsiya

ML asosidagi engine PII, PCI, PHI, intellektual mulk, ichki hujjatlarni avtomatik tanib oladi. RegEx + ML hybrid approach, OCR for images/PDFs, fingerprinting va exact data matching

PII / PCI / PHI Detection Fingerprinting (EDM/IDM) ML Content Classification OCR Document Analysis

Policy Engine va incident javobi

Real vaqtda aniqlangan ma'lumot oqimi siyosatlarga qarshi tekshiriladi va avtomatik javob: bloklash, karantin, ogohlantirish, audit log. Risk scoring + adaptive thresholds

Policy Engine Risk Scoring Auto-Block / Quarantine SIEM Integration

Korporativ Sinov Natijalari

30-50%

Ma'lumot sizib chiqishi kamayishi

Sinov o'tkazilgan korxonalarda data exfiltration hodisalari kamayishi (mavjud yechimlarga nisbatan)

2-4%

False positive darajasi

Yolg'on ogohlantirishlar darajasi — soatiga 5-12 ta o'rniga 2-4%. SOC samaradorligi oshdi

20-50%

TCO kamayishi

Mavjud DLP yechimlariga nisbatan jami egalik xarajatlari (TCO) qisqarishi: litsenziya + integratsiya + xizmat

4.5/5

Compliance qondirish

GDPR, HIPAA, PCI-DSS auditlari uchun avtomatik hisobot. Compliance officer'lar reytingi

Ilmiy Nashrlar va Patentlar

Algorithms for Real-time Monitoring of Confidential Data Movement in Computer Networks

IEEE Transactions on Network Security

This paper presents novel algorithms for real-time monitoring of confidential data movement in computer networks...

2024 Ko'rish

Machine Learning Approaches for Confidential Data Classification

ACM Computing Surveys

This survey paper reviews machine learning approaches for confidential data classification...

2024 Ko'rish

Network Protocol Analysis for Security Applications

Computer Networks

This paper presents methods for network protocol analysis in security applications...

2024 Ko'rish

Tadqiqot

Tadqiqotdan Mahsulotgacha

Tadqiqot Metodologiyasi

DLP muammoni aniqlash

Maqsad va threat model

Algoritm va arxitektura

Prototip va integratsiya

Sinov va validatsiya

Nashr va sertifikatsiya

DLP Texnologik Yadrosi

Real-time tarmoq trafigini DPI tahlili

Sensitive Data Discovery va klassifikatsiya

Policy Engine va incident javobi

Korporativ Sinov Natijalari

Ilmiy Nashrlar va Patentlar

Algorithms for Real-time Monitoring of Confidential Data Movement in Computer Networks

Machine Learning Approaches for Confidential Data Classification

Network Protocol Analysis for Security Applications

Tadqiqot va Tasdiqlash