How to Interact with AI Models Without Exposing Sensitive Data
Conversations with public AI models such as ChatGPT, Gemini, Claude and GigaChat are scanned by automated filters and may be reviewed manually by employees or contractors of the model providers. Corporate administrators and, in some cases, law-enforcement agencies can also access chat histories.
To reduce future exposure, users should first disable the option that allows the model to learn from their dialogues. In OpenAI settings this is done by turning off “Improve the model for everyone.” Google Gemini requires disabling chat history so that conversations are automatically deleted after 72 hours. Similar toggles exist for DeepSeek and Anthropic Claude, while Sber GigaChat offers no such control in its consumer interface.
Additional hygiene measures include replacing real names, phone numbers and other identifiers with placeholders such as {name} or {phone}. When large volumes of data must be processed, tools like Redacto or Гарда Маскирование can automate masking, although results still require manual verification.
Long-running chats accumulate contextual information; therefore old conversations should be deleted and new tasks started in fresh sessions. Custom GPTs or Gemini Gems can store persistent instructions so that behavior does not need to be re-explained each time.
Models downloaded from Hugging Face should be scanned for malicious payloads with utilities such as HiddenLayer Model Scanner. Prompt-injection risks can be mitigated by pasting any third-party prompt into a word processor and applying a uniform text color to reveal hidden commands.
The most private approach is to run models locally with Ollama. After installation, a small model such as llama3.2:1b can be pulled and executed entirely on the user’s hardware. Larger production models like qwen3-coder:30b require at least 40 GB of RAM. Running the service inside a Docker container further isolates it from the host system.
For hybrid use, the open-source client ChatBox can connect to both local Ollama instances and paid API endpoints such as Cloud.ru Evolution Foundation Models. Users paste their API key, select compatible models, configure embedding and reranker components for RAG, and upload documents to a knowledge base that the model consults during generation.
Related articles
Agent-Ops 0.4.0 Released: Methodology for Secure Human-AI Collaboration in IT Operations
Sergey Zhitinsky, founder of Git in Sky, has published the public normative candidate for Agent-Ops 0.4.0, an open industry methodology governing how engineers and AI agents jointly handle IT infrastructure tasks. The framework keeps humans firmly in the decision-making loop while using deterministic programs for data collection and approved changes. It addresses risks such as prompt injection through processed data, unverified model outputs, and unclear accountability when AI recommendations lead to incidents. The methodology divides work across eight explicit steps and three separate planes: data, governance, and independent verification performed by a Guardian role. Two additional companies have joined as maintainers following agreements at the IT Elements 2026 conference, turning the project into a multi-organization effort. Contributors are invited to help refine contracts, schemas, and operational scenarios through GitHub and GitVerse.
ProxyKey MCP: Securing API Access for AI Agents Without Exposing Credentials
ProxyKey has released an MCP server that allows AI coding agents such as Claude Code and Cursor to manage API credentials without ever reading the actual secret values. The solution addresses the risk that any key visible to an agent becomes compromised through logging, tracing, or prompt injection. Real provider keys are stored encrypted with AES-256-GCM and never returned by any API endpoint after initial entry. Agents instead receive limited virtual passes that support IP binding, rate limits, TTL, and detailed request logging. A pending-secret workflow lets agents prepare services before the real token exists, with the human entering the secret only through a web panel. The approach deliberately restricts the MCP tool contract so no operation can read or return secret values.
Shadow AI in CI/CD: Why AI Agents Must Be Modeled as Security Threats
A new analysis from the CNCF highlights the growing risks of Shadow AI within continuous integration and continuous deployment pipelines. The report argues that AI agents should be treated as potential threats rather than simple productivity tools. Starting from a developer's laptop and extending to Kubernetes clusters, these agents can introduce unauthorized access paths and data exposure risks. Security teams are urged to incorporate AI agent behavior into formal threat modeling exercises. The discussion emphasizes the need for visibility and control over autonomous AI components operating in production environments.
Detecting Lateral Movement with Neural Networks Trained Solely on Synthetic Data
A researcher generated entire corporate network histories using a 135-line configuration file to create synthetic authentication logs containing lateral movement attacks. Neural networks trained exclusively on these artificial datasets were then evaluated against 1.65 billion real authentication events from Los Alamos National Laboratory, including 749 red team events across 301 compromised machines. The best ensemble of six models flagged 3.6 million hourly machine windows and placed 16 genuine attacks among the top 23 highest-scoring entries, producing only seven false positives. In comparison, a simple threshold counter required 161,000 false alarms to reach the same detection level. The approach also demonstrated an iterative feedback loop where detector errors directly informed refinements to the synthetic world generator. The work shows that synthetic data can reach AUC performance comparable to models trained on real labeled attacks while providing full control over the underlying attack definitions.