Thales Group Report Reveals Surge in AI Agent Adoption and Rising Cybersecurity Budgets Worldwide
Thales Group has released a comprehensive report examining how artificial intelligence is reshaping information security practices around the globe. The study, based on responses from more than 3,000 participants in 20 countries, highlights both the opportunities and the mounting risks created by widespread AI adoption.
According to the findings, 34 percent of organizations are already using AI agents in their operations, while 73 percent intend to implement them within the coming year. The introduction of these autonomous agents has accelerated data processing speeds and volumes, making it significantly harder for security teams to maintain adequate protection levels.
As a direct consequence, the number of companies earmarking dedicated budgets for AI-related security has grown by 50 percent year-over-year. Last year only 20 percent of respondents allocated specific funds for AI security; this year the figure has risen to 30 percent. Nevertheless, 53 percent still cover AI protection costs from their general information-security budgets.
Speed of change within AI ecosystems was cited by 70 percent of respondents as a major security challenge. Additionally, 61 percent confirmed that their AI applications had become direct targets of attacks aimed at exfiltrating sensitive information. New forms of reputational harm are also emerging: 48 percent of organizations reported financial losses caused by AI-generated disinformation, and 59 percent encountered deepfake-based attacks.
Among realized AI-driven attacks, those focused on disclosing confidential data showed the strongest growth. Cloud resources continue to dominate attacker priorities, with cloud storage named by 35 percent, SaaS applications by 34 percent, and cloud management infrastructure by 32 percent of respondents. User credentials have climbed from eighth to fifth place among top targets, cited by 21 percent, while identity data moved from tenth to sixth place.
Regular security audits demonstrate clear benefits. More than half of organizations that conduct routine audits reported no data leaks from cloud services (54 percent) or corporate systems (57 percent). In contrast, only 6 percent of companies that skipped audits could claim the same. Human error remains the leading cause of incidents, accounting for 28 percent of breaches.
Russian companies share concerns about public AI services
The InfoWatch expert center also presented results from a survey conducted by the Russian BISA association among domestic information-security and IT specialists. Respondents identified the transfer of confidential data to public AI services as the primary threat vector (89 percent), followed by risks associated with embedded AI agents and unintended disclosure to third parties (both 59 percent).
Almost half (46 percent) of Russian respondents said they had either experienced or were aware of data leaks involving generative AI tools. While 59 percent acknowledged the value of AI in daily workflows, they stressed the need for better regulation, methods to detect AI-generated text, and mechanisms to recall data from public models. Most organizations currently allow limited use of AI tools (46 percent) and rely on employee training (48 percent) and enhanced monitoring (30 percent) to mitigate risks.
Related articles
AgentForger Vulnerability in ChatGPT Workspace Agents Enabled Malicious AI Deployment via Single Phishing Link
A vulnerability in ChatGPT Workspace Agents allowed attackers to create and deploy a malicious AI agent inside an organization from a single phishing link. Named AgentForger, the flaw was fixed by OpenAI on June 8, 2026. The attack exploited a permissive parameter in the Agent Builder that accepted instructions directly through the URL. An authenticated user opening the prepared link would trigger automatic execution of the command without additional confirmation. The victim required access to Workspace Agents and at least one pre-authorized enterprise connector such as Outlook, Gmail, Google Drive, Slack, Teams, or Google Calendar. The malicious prompt instructed the platform to create an agent, connect available applications, disable approval requests, publish the component, and schedule it for recurring operation. In the demonstration, the agent monitored emails from the attacker with subjects starting with “TASK” and executed the contained instructions while returning results to the attacker-controlled address.
AI Coding Tools Under Fire: Grok Build Uploads Entire Git Histories, Claude Code Suspected of Silent Transfers
Security researcher cereblab uncovered that Grok Build 0.2.93 establishes separate HTTPS channels to exfiltrate full Git repositories, resulting in a 27800-fold traffic discrepancy between task context and storage uploads to Google Cloud Storage buckets. The tool ignores user instructions such as "do not read" and decouples the improve_model_enabled client switch from the server-controlled trace_upload_enabled flag, allowing continued uploads even when privacy settings are disabled. Similar concerns emerged around Claude Code, which maintains undisclosed WebSocket connections that transmit file paths, dependency trees, and code metadata without user awareness or audit logs. Comparative traffic audits showed that Codex and Gemini produced no anomalous outbound activity, while Grok Build and Claude Code were the only tools confirmed to perform data transfers beyond user authorization. The incidents highlight systemic issues including server-side remote control of client behavior, lack of third-party audits for closed-source binaries, and the conflict between model training data needs and user data sovereignty. Experts recommend zero-trust measures such as network blocking, Docker sandboxing without mounting .git directories, git filter-repo sanitization, and preference for auditable open-source alternatives like Continue.dev or locally deployed Ollama models.
PentesterFlow Launches Open-Source AI CLI Tool for Penetration Testers and Bug Bounty Hunters
PentesterFlow is a new open-source, human-in-the-loop AI command-line tool designed specifically for penetration testers and bug bounty hunters. It automates the full workflow from reconnaissance to report generation while requiring explicit analyst approval before executing sensitive commands. The tool addresses common issues in agentic AI security tools such as hallucinations, weak context retention, and poor tool integration by incorporating built-in pentesting skills and evidence-based vulnerability confirmation. It supports connections to local or hosted LLMs including Ollama, Gemini, Groq, and others, and features continuous local learning that stores user preferences and lessons without retraining models. A key differentiator is its integration with Burp Suite and a permission-based execution model that includes a YOLO mode for isolated environments. The project positions itself as a transparent alternative to fully autonomous tools like PentAGI and PentestGPT.
Optimizing Cybersecurity Content for LLMs: How Sites Can Enter Generative AI Answers
Search engines and AI services like ChatGPT, Gemini, Perplexity, Copilot and Google AI Overviews increasingly deliver synthesized answers instead of link lists. For cybersecurity publishers this changes competition because high traditional rankings no longer guarantee visibility or accurate citation. The article explains GEO, AEO and LLMO practices, shows how material moves through indexing, fragment selection and summarization stages, and stresses the need for self-contained facts that survive extraction and paraphrasing. It provides concrete writing frameworks for vulnerability reports, including required fields such as CVE identifiers, affected versions, attack conditions and real-world exploitation evidence. Technical requirements cover correct robots.txt handling for Googlebot, OAI-SearchBot, GPTBot and Bingbot plus the use of IndexNow for rapid updates. The piece also warns about poisoning risks, prompt injection and slopsquatting attacks that can feed false data into generative systems.