Can you share any tools/resources your org is using to deliver AI literacy training? Are you relying on a framework?

AI & Machine LearningGenerative AI
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CEO in Services (non-Government)9 months ago

I have simply worked with my L&D team and mapped AI capability by roles in the organization and then mapped that back to online courses that provide certifications, then let people to access what is required or suggested for their roles along with some communities of interest in where they can talk about the learnings and application in the daily work (this is to me the most important part ...)

VP of Technology9 months ago

Nulia has an integrated learning/training platform called Nulia Works. This platform is integrated with Microsoft 365 and CoPilot, but the use case is there for use in any other type of AI platform as well. See: https://www.linkedin.com/feed/update/urn:li:activity:7193941254475255808/ and the linked article for more information about Nulia Works.

9 months ago

Not everyone is cut out to be a SW developer.  Industry should pull back on the reigns of everyone being a developer.

CIO9 months ago

We are putting a training session together using LinkedIn training.  We will have the introductory course for everyone and then additional training for specific areas (Finance, Marketing, etc.) 

Director of IT in Healthcare and Biotech9 months ago

We have made som standard introductory courses to GenAI, but believe more in learning through hands-on use of the tools. Have therefore made fictional scenarios and ask the participants to use various tools to solve certain problems with the help of GenAI, where they need to rethink how to approach the problem with these new tools.

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AI-Powered Cyber Threats

Ukraine’s CERT-UA just exposed the future of cyber warfare. One click could leak every PDF on a government laptop in minutes. The LAMEHUG malware campaign uses large language models to generate real-time attack commands, turning legitimate AI services into weapons.

Here’s what happened: CERT-UA reports that phishing emails carrying “Додаток.pdf.zip” dropped a .pif executable. Once run, the Python-based LAMEHUG implant calls the Qwen 2.5-Coder-32B model via Hugging Face API to turn pre-set prompts into OS commands, harvests documents, then exfiltrates data over SFTP (port 22) or HTTP POST to compromised hosts like stayathomeclasses[.]com. Activity attributed with moderate confidence to APT28.

The tactical shift is fundamental:
• Signature-only detection can’t keep pace when malware writes fresh commands on demand
• Legitimate AI services become attack infrastructure
• Threat actors scale human creativity with machine speed

Your Monday morning reality check:
Are you monitoring AI API usage for anomalous patterns? Can your SOC detect dynamically generated attack commands? Do your incident response procedures account for prompt-generated threats?

Immediate actions:

1. Inventory every outbound LLM/API destination and block unknown hosts
2. Baseline legitimate LLM traffic and alert on new patterns
3. Extend threat models with AI-assisted TTPs using the MITRE Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS)
4. Review the CSA MAESTRO AI Security Controls Matrix and OWASP Top 10 for LLM Applications
5. Train SOC analysts on detecting prompt-generated command signatures

The existing frameworks are MITRE Adversarial Threat Landscape for Artificial Intelligence Systems (ATLAS), CSA MAESTRO AI Security Controls Matrix, and OWASP AI Security & Privacy Guide. The question isn’t whether AI will transform cyber warfare; it’s whether your defenses will adapt to adversaries who blend malicious intent with artificial reasoning.

Ukraine’s CERT-UA team deserves credit for exposing this evolution and providing the technical intelligence we need to adapt our strategies. Full technical details: https://cert.gov.ua/article/6284730

What steps is your organization taking to prepare for AI-powered adversaries?

AI deepfakes have cost the industry over $200M this year. As CISOs, can we scale detection fast enough, or is regulatory pressure the only way to drive adoption?

New NSA, CISA, and FBI guidance reveals 3 critical threats that can compromise AI systems:
1. Data Supply Chain Vulnerabilities – tampering or poisoning for as little as $60–$1,000
2. Maliciously Modified Data – adversarial poisoning and model inversion
3. Data Drift – natural evolution creates security blind spots

Where does your organization sit on the AI security maturity curve? Are you enhancing existing tools or implementing AI-specific platforms?

Read the full guidance: https://media.defense.gov/2025/May/22/2003720601/-1/-1/0/CSI_AI_DATA_SECURITY.PDF

Ukraine’s CERT-UA recently exposed LAMEHUG malware that uses Large Language Models to generate attack commands in real-time via legitimate AI services (Hugging Face API). What is your organization’s current readiness level for AI-powered cyber threats?

A) Fully Prepared - We have AI-specific threat detection, monitoring of AI API usage, and updated incident response procedures for AI-assisted attacks

B) Partially Prepared - We’re monitoring some AI services and have basic awareness, but lack comprehensive AI threat modeling and detection capabilities

C) Early Assessment - We’ve identified the risk and are evaluating AI security frameworks (MITRE ATLAS, CSA MAESTRO, OWASP AI guides) but haven’t implemented controls yet

D) Unprepared - We haven’t specifically addressed AI-powered threats in our security strategy or implemented AI-focused monitoring

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Do you believe that (RPA, Automation, and intelligent Automation), coupled with clear guidelines, robust process mapping, and effective monitoring and control capabilities, can serve as a key enabler for successfully integrating AI and GenAI into an organization's operations and strategies?

Strongly Agree29%

Agree63%

Disagree10%

Strongly Disagree1%

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