Maxim Bar Kogan on Context for AI Agent Actions

Why the same database action can have different implications depending on the request.

MBMaxim Bar KoganInvité · Onyx Security
Chaîne
No Priors: AI, Machine Learning, Tech, & Startups
Publié
30 mai 2026
Vidéo
0:53
Lecture
1 min
Original
anglais
Ouvrir sur YouTube
En faisant défiler, la vidéo se réduit en bas à droite et continue. Les sous-titres suivent votre langue de lecture.
Langue de lectureDisponible en 2 langues
Cet article n’est pas encore disponible en français ; il s’affiche dans l’original en anglais.
RÉSUMÉ

En 30 secondes

Kogan argues that security controls should account for the context in which an AI agent acts.

  1. 1AuthorizationA requested operation differs from an unsolicited one.
  2. 2ContextKogan questions what conventional tools can infer.
  3. 3Agent controlsHe argues for context-aware controls.

À retenir

  1. Authorization changes the meaning of an action.
  2. Existing tools may lack task context.
  3. Kogan advocates agent-specific controls.
TEXTE INTÉGRAL

Lire le texte remanié, chapitre par chapitre

Transcription seule
01

The same action, different context

Onyx Security’s Maxim Bar Kogan compares an explicitly requested database recreation with an agent independently deciding to delete and recreate a database while handling another task.

Onyx Security’s Maxim Bar Kogan compares an explicitly requested database recreation with an agent independently deciding to delete and recreate a database while handling another task.

He says the first action could save work when authorized, while the same action taken without relevant context could be dangerous.

02

Controls and context

Kogan argues that conventional endpoint and API security tools may not have enough context about why an agent takes an action.

Kogan argues that conventional endpoint and API security tools may not have enough context about why an agent takes an action.

He advocates controls designed for agentic systems, warning that otherwise organizations may either overly restrict useful work or miss dangerous actions.

Sur la même chaîne

38:06EN
No Priors: AI, Machine Learning, Tech, & Startups· 29 oct. 2026Podcast · 3 voixServal’s Jake Stauch on coding agents for enterprise ITInvité : Jake Stauch · Anime : Sarah Guo, Elad GilStauch explains why Serval is challenging ServiceNow, how it approaches permissions and approvals, and why background agents remain difficult to deploy safely.IA en entrepriseAgents de codeWorkflow automation+2
1:07EN
No Priors: AI, Machine Learning, Tech, & Startups· 27 oct. 2026PodcastGeorge Kurtz on How AI May Change CyberattacksInvité : George KurtzThe CrowdStrike CEO describes his assessment of evolving threats.
39:23EN
No Priors: AI, Machine Learning, Tech, & Startups· 15 oct. 2026Podcast · 2 voixThe Defenders' Window: Greg Brockman on AI-Generated Knowledge, Drug Discovery, and the New Physics of CybersecurityAnime : Sarah Guo, Elad GilOpenAI's president explains why solving open math problems is a proof point for science, how proactive agents change product form, and why security spending must exceed software spending.SécuritéAI securitySécurité et gouvernance+3
1:04EN
No Priors: AI, Machine Learning, Tech, & Startups· 14 oct. 2026PodcastWalter Goodwin on the Slow Steps in Chip DesignInvité : Walter GoodwinA chip founder explains why automation does not remove every computational bottleneck.SemiconductorsAI cost & performance
1:09:44EN
No Priors: AI, Machine Learning, Tech, & Startups· 9 oct. 2026Podcast · 2 voix10,000 GB300s for Four Weeks: Reflection’s Open-Model LedgerAnime : Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.Open source et communautéOpen-weight modelsReinforcement Learning+3
0:39EN
No Priors: AI, Machine Learning, Tech, & Startups· 8 oct. 2026PodcastMichael Lee on Rethinking Organizations with AIInvité : Michael LeeA business leader argues that the opportunity goes beyond faster tasks.