AI is changing the cybersecurity threat landscape (Practical AI #294)
Bibliographic record
Abstract
This week, Chris is joined by Gregory Richardson, Vice President and Global Advisory CISO at BlackBerry, and Ismael Valenzuela, Vice President of Threat Research & Intelligence at BlackBerry. They address how AI is changing the threat landscape, why human defenders remain a key part of our cyber defenses, and the explain the AI standoff between cyber threat actors and cyber defenders.Join the discussionChangelog++ members save 10 minutes on this episode because they made the ads disappear. Join today!Sponsors:Fly.io - The home of Changelog.com - Deploy your apps close to your users - global Anycast load-balancing, zero-configuration private networking, hardware isolation, and instant WireGuard VPN connections. Push-button deployments that scale to thousands of instances. Check out the speedrun to get started in minutes.Notion - Notion is a place where any team can write, plan, organize, and rediscover the joy of play. It's a workspace designed not just for making progress, but getting inspired. Notion is for everyone - whether you're a Fortune 500 company or freelance designer, starting a new startup or a student juggling classes and clubs.Eight Sleep - Take your sleep and recovery to the next level. Go to eightsleep.com/PRACTICALAI and use the code PRACTICALAI to get $350 off your very own Pod 4 Ultra. You can try it for free for 30 days - but we're confident you will not want to return it. Once you experience AI-optimized sleep, you'll wonder how you ever slept without it. Currently shipping to: United States, Canada, United Kingdom, Europe, and Australia.Featuring:Gregory Richardson – LinkedInIsmael Valenzuela – GitHub, LinkedIn, XChris Benson – Website, GitHub, LinkedIn, XShow Notes:The AI Standoff: Attackers vs. Defenders | Blackberry BlogBlackberrySomething missing or broken? PRs welcome!
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.354 | 0.202 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".