CISA’s expanded SBOM guidance increases data collection requirements, but fails to address the core challenge of converting SBOM data into operational risk mitigation.
As manufacturing operations rapidly digitalize, coordination between IT and production remains unclear in many cases, while cyber incidents cause average production downtime of 15.3 hours and damages exceeding one million US dollars per incident.
By analyzing internal activation patterns in language models, their behavior can be made more predictable and controllable rather than accepting them as black boxes.
Responsible AI deployment under the EU AI Act does not mean eliminating all risks, but rather limiting applications, testing thoroughly, clarifying responsibilities, and accepting residual risks.
Security teams that enable fast and transparent adoption through structured AI governance position themselves as strategic partners rather than gatekeepers.
AI systems without intermediate verification between interpretation and command execution endanger the security chain through lack of visibility and validation options.
CISOs should not reject agentic AI outright, but instead use four control questions (data inputs, actions, damage scope, observability) to make risks legible and deliberately constrain them.
Agentic AI systems create security risks through their autonomy, which classical threat models do not cover and which require different control mechanisms.
The adoption of AI systems is systematically outpacing established governance and oversight mechanisms in organizations, creating risks for data protection, compliance, and transparency.
