MITRE ATLAS

IT Elements 2026 to Stage IT Apocalypse Simulations and Critique Russian Vendors

The IT Elements 2026 conference has unveiled its full program, centering on business continuity after successful cyberattacks, infrastructure failures, or technological disasters. Scheduled for September 9-10 at the Serp i Molot cultural center in Moscow, the event will host over one hundred reports, discussions, demonstrations, and master classes. The opening plenary will examine whether the primary-backup data center model remains viable, if the 3-2-1 backup rule still protects against modern ransomware, and whether CIOs or CISOs should own cyber resilience. Practical sessions will demonstrate a full Microsoft migration in 60 minutes, moving Exchange and MinIO to Russian alternatives, and a detailed review of domestic hyperconvergence strengths and weaknesses. Comparative test results for routers, NGFW, and VM systems will be presented without marketing gloss. Additional tracks will cover monitoring of corporate AI models from GPU to prompt under FSTEC Order No. 117, OWASP Top 10 for LLM, and MITRE ATLAS, alongside real-world cases from Rosatom, Beeline, Sber, and T-Bank. Trubnaya Metallurgicheskaya Kompaniya will simulate a 48-hour IT outage and show how to restart operations in half a day.

Aligning AI Adoption Maturity with AI Security Using CMMI and Russian Regulatory Requirements

Organizations frequently operate at mismatched maturity levels between AI implementation and AI security controls, creating significant gaps that lead to incidents and regulatory violations. The analysis maps both dimensions onto the five-level CMMI scale, showing how adoption often outpaces protection and highlighting the risks of Shadow AI, uncontrolled agents, and non-compliance. Key frameworks including OWASP AIMA, Google SAIF, NIST AI RMF, and MITRE ATLAS are aligned to CMMI levels L1 through L5. Russian requirements under FSTEC Order No. 117, effective March 2026, introduce mandatory controls for trusted AI technologies, data handling, and statistical response validation that activate precisely at the L2-to-L3 transition. The article details predictable organizational states from denial to managed agentic systems and provides a practical matrix linking each CMMI level to minimum security requirements and specific Russian compliance anchors such as GOST R 56939-2024 and ISO/IEC 42001.

Adam Shostack Presents PHANTOM-B Threat Modeling Framework for LLMs at Black Hat USA

Security expert Adam Shostack introduced the PHANTOM-B framework during his Black Hat USA talk titled Threat Modeling LLMs: The PHANTOM-B Approach. The framework adapts classic threat modeling principles to large language models while avoiding duplication of existing security engineering practices such as STRIDE. It centers on eight focused threats including prompt injection, hallucination, anthropomorphization, non-explainability, training issues, overreliance, missing security engineering, and bias. PHANTOM-B was developed after clients requested a practical, low-overhead tool that complements rather than replaces established catalogs from OWASP, MITRE, and NIST. The approach uses the enduring Four Question Framework and fits on a wallet card under a Creative Commons license. It has already been tested with hyperscalers and major banks facing intense pressure to ship AI features quickly.

AI Agents Given Code and API Access Can Now Assist Attackers

An AI assistant that only answers questions can make mistakes, but an AI agent with access to email, code execution, corporate APIs and internal data can make those mistakes inside production infrastructure. The difference is fundamental: once tools, credentials and internal data are connected to the model, it becomes a privileged user that may not distinguish legitimate commands from hidden instructions on a web page. OWASP lists prompt injection, sensitive data disclosure, unsafe output handling and excessive autonomy as key risks for LLM applications. MITRE ATLAS specifically describes techniques involving prompt injection, context poisoning and tool invocation by AI agents. The article examines how agents differ from chatbots, how attackers can control them through untrusted content, and why a system prompt alone cannot protect code, data and APIs. CyberED is running its free NeuroAugust series of events and materials on AI in cybersecurity, including a session on secure AI system development.

Bank of Russia Publishes Methodological Recommendations No. 3-MR on AI Security for Financial Market Participants

The Bank of Russia has released methodological recommendations No. 3-MR dated 16 June 2026, providing detailed guidance on ensuring information security during the development and use of artificial intelligence systems in the financial sector. The document builds on the earlier Code of Ethics for AI in finance and integrates with existing risk management, operational resilience, and data protection frameworks already familiar to credit institutions and other market participants. It introduces standardized terminology for AI-specific threats such as hallucinations, data drift, and poisoned datasets while outlining six risk categories and a four-stage AI system lifecycle model. Organizations are advised to apply threat modeling based on FSTEC methodology, implement proportional controls across data preparation, development, training, and operation phases, and maintain human oversight for high-risk automated processes. Special attention is given to supply chain risks involving third-party vendors and open-source components, requiring due diligence, provenance tracking, and contractual safeguards aligned with existing outsourcing standards. The recommendations remain non-binding yet signal clear regulatory expectations that are likely to influence future compliance checks and audits.