Rostelecom AppSec engineer Yuri Tumanov, together with Igor Korkin from Positive Technologies and Oksana Dokuchaeva from FMBA Russia, published a detailed analysis of how generative AI is reshaping both offensive and defensive operations in cybersecurity.
The authors describe five concurrent roles of AI: accelerator of attacks, compromised trusted assistant, leakage channel, protective shield, and primary target. They emphasize that AI does not replace phishing, malware or poor decisions but lowers the cost of iteration, allowing attackers to generate context-aware lures and code at machine speed.
AI as Attack Accelerator
Generative models produce hundreds of tailored phishing variants, translate messages, adjust tone and embed project-specific details harvested from prior breaches. Defenders are advised to move beyond grammar checks toward independent confirmation channels, sandbox detonation and clear user reporting workflows.
Compromised Assistant and Data Leakage
When an AI agent runs inside a browser or desktop client, a compromised endpoint gives attackers access to the full authorized session, clipboard, connected documents and tool permissions. The article warns against storing secrets in chat history or retrieval corpora and recommends strict connector scoping, device posture verification and immutable audit logs of every tool call.
AI as Shield and Direct Target
While models can triage alerts and draft investigations, any high-risk action must pass deterministic policy engines and human approval. On the offensive side, prompt injection, poisoned retrieval documents and excessive agent permissions create new attack surfaces that require provenance tracking, schema validation of model output and least-privilege tool scopes.
The authors conclude that the most effective controls remain mundane: separate verification of rights, comprehensive logging and explicit confirmation buttons before irreversible operations.
