For more than a decade, my team, including my co-founders Vladimir Jelić and Dušan Todorović, and I lived on the buyer’s side of the enterprise cybersecurity table. Leading global application security and security architecture across highly regulated gaming, lottery, and financial transaction infrastructures at IGT and GTECH, we evaluated, deployed, and managed nearly every major static and dynamic security tool on the market. Prior to that, I served as Chief Security Officer (CSO) leading security at GTECH G2, and earlier held CEO and CxO roles across several software development and technology companies. Alongside my industry leadership, as a university professor I have spent years teaching computer science, operating systems, and cybersecurity while conducting deep academic research in these fields (you can find more about my background at dragan-pleskonjic.com/about-me).
When we founded GLOG.AI® in November 2024, together with co-founder Andrija Pleskonjić leading business development, we did not set out to build yet another noisy scanner. We set out to solve the exact operational bottleneck that keeps CISOs, CTOs, and engineering leaders awake at night: closing the window between vulnerability discovery and verified code remediation without ever forcing a regulated enterprise to surrender its proprietary source code to a public cloud.
The Three Numbers Breaking Modern AppSec
Today’s engineering and security teams are caught between AI-accelerated attackers and strict regulatory mandates:
According to the Verizon 2026 Data Breach Investigations Report, organizations take a median of 43 days to patch a known, exploited vulnerability. This delay contributes to a $4.99M global average data breach cost, reaching $11.5M in the US (IBM 2026).
Up to 80% of AppSec time goes to triaging false alarms. On the RealVuln v3.1 benchmark, only 14.7% of findings from a leading rule-based scanner were real vulnerabilities. Meanwhile, AI coding tools introduce flaws in 44% of tasks (Veracode 2026), and 83% of developers routinely bypass legacy controls.
While 77% of enterprises cite IP leakage concerns with cloud AI, binding mandates now carry massive penalties. Overlapping GDPR, NIS2, DORA, and EU CRA rules (active since September 2026) expose firms to ~€45M per incident, alongside strict US DoD CMMC enforcement.
Why Code Security Alone Isn’t Enough: Enter the Glogosphere
A question we often hear from industry peers and investors is: “Why build an integrated three-product ecosystem like the Glogosphere rather than a standalone code scanner?”
From our experience running enterprise security operations, the answer is straightforward: code does not exist in a vacuum. A vulnerability in a repository is a theoretical risk, whereas a vulnerability connected to an exposed external asset and abnormal network traffic is an active breach.
That is why we architected the Glogosphere as a unified AI cybersecurity platform under one login (“Predict. Protect. Remediate.”):
Glog.AI (Software Security)
Embedded directly into CI/CD pipelines (GitHub, GitLab, Azure DevOps, Bitbucket, Jenkins, or CLI), our engine combines proprietary rules, Python code-graph reachability analysis, and full-context LLMs to filter out false alarms and commit verified code fixes directly to a developer branch for review. Includes automated SBOM compliance checks across 7 regulatory profiles (including EU CRA) and CBOM post-quantum readiness checks.
Security Predictions
Continuously maps the external attack surface, correlates exposed weaknesses against daily CISA KEV and EPSS feeds, detects AI-labeled lookalike phishing domains and leaked credentials, models evidence-backed attack paths, and exports board-level reports plus STIX 2.1 and MISP feeds directly into enterprise SIEMs and SOCs.
NetEcho (Network Security)
Inspects strictly network traffic metadata using proprietary in-house machine-learning models without ever decrypting or logging payload data. NetEcho automatically correlates unusual network flows with the external customer assets discovered by Security Predictions to ensure complete operational confidentiality.
Measured Proof, Not Marketing Claims: #1 on RealVuln v3.1
In cybersecurity, accuracy must be measured, not asserted. On the public RealVuln v3.1 Python security benchmark across 66 real-world projects, Glog.AI ranked #1 out of 36 evaluated entries:
