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Where do new collaboration tools (Microsoft Teams, etc.) sit within your organization?

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How much has virtual work affected org structure at your organization?

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AI Coding Assistants: Velocity or Vulnerability?

Latest research reveals a hidden feedback loop threatening software security:
The Evidence: • ≈33% of Copilot suggestions echo CWE Top-25 flaws (Asare et al., 2024) • ~50% of LLM code snippets contain exploitable bugs (CSET, 2024) • Developers with AI help feel more confident while shipping less secure code (Stanford, 2023)
The Systemic Risk: Flawed AI output is pushed to public repositories, polluting the next model's training data—risk compounding with every release cycle.

What leading teams are doing this quarter:
1. Audit AI-tool usage and establish approved lists
2. Insert AI-aware SAST (e.g., Snyk Code, Semgrep Assistant) into IDEs and CI/CD pipelines
3. Adopt OWASP LLM Top 10 + MAESTRO for threat modeling
4. Track percentage of AI-generated code and its defect rates

Question: How are you measuring AI-generated technical debt today, and what is your plan to stop it from becoming tomorrow's supply-chain crisis?

Share metrics or tools that worked for you.

Ukraine’s CERT-UA just exposed how real AI-powered cyber threats have become. The LAMEHUG malware used Hugging Face’s Qwen model to generate attack commands in real time, one click and every PDF on a laptop gets exfiltrated. Detection is nearly impossible when malware writes fresh commands on demand.

Is your team monitoring LLM API usage or training SOC analysts to spot prompt-generated threats? What steps are you taking to prepare for AI-powered adversaries?

Full details: https://cert.gov.ua/article/6284730

Does your organisation have a formalised threat hunting programme? If so - what were the key drivers for you in setting one up and what made it successful?

If not - what is stopping you from setting this up?

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