Notifications
Clear all
Topic starter
10/07/2026 9:15 pm
TL;DR: At RSAC 2026, CISO AI uncertainty was framed as a board-level operating reality, according to Elisity, with Gartner citing that by 2028 half of incident response efforts will involve custom-built AI-driven applications and IBM reporting 97% of AI-related breaches in organisations without proper AI access controls. The decisive issue is not model trust, but what AI can reach, because containment and blast-radius control now matter more than prediction.
NHIMG editorial — based on content published by Elisity: CISO AI Uncertainty at RSAC 2026: Joan Goodchild Interview
By the numbers:
- By 2028, 50% of all enterprise cybersecurity incident response efforts will focus on incidents involving custom-built AI-driven applications.
- 97% of AI-related breaches occurred in organizations without proper AI access controls, and shadow AI was a factor in 20% of breaches, adding $670,000 to average breach costs.
Questions worth separating out
Q: How should security teams govern AI systems that can take actions on their own?
A: Security teams should govern AI systems as non-human identities with bounded reach, explicit ownership, and runtime containment.
Q: Why do AI-enabled workflows increase blast radius risk?
A: AI-enabled workflows increase blast radius risk because they can combine access, decision-making, and tool use in one runtime path.
Q: What do security teams get wrong about AI trust?
A: Teams often focus on whether the model is trustworthy instead of whether the surrounding control plane is bounded.
Practitioner guidance
- Define AI access as a privileged identity boundary Inventory every AI system that can authenticate, call tools, or reach enterprise data, then classify those paths with the same discipline used for privileged non-human identities.
- Constrain AI workloads with explicit network segments Place AI-enabled applications and agents into narrowly scoped segments so the workload cannot reach broad internal services by default.
- Replace confidence statements with reachability tests Before approving AI use cases, test what the system can actually reach under valid credentials, then document the reachable data sets, APIs, and downstream automations.
What's in the full article
Elisity's full article covers the interview detail this post intentionally leaves at the governance level:
- The full RSAC interview context around Joan Goodchild's panel observations and board-room questions.
- The 60/9/30 board framing in more detail, including how CISOs can explain controlled, compensated, and moving-risk areas.
- The article's discussion of AI agents, microsegmentation, and why reachability matters more than abstract model trust.
- The surrounding RSAC commentary on AI uncertainty, awareness training, and emerging AI security expectations.
👉 Read Elisity's interview on CISO AI uncertainty at RSAC 2026 →
CISO AI uncertainty at RSAC 2026: what controls should teams fix first?
Explore further
10/07/2026 9:38 pm
AI uncertainty is becoming an identity governance problem, not just a model governance problem. The article shows that boards are asking how much AI they can trust, but practitioners should be asking what identity an AI system uses, what it can access, and how its reach is contained. That is the governing question because the risk materialises through privilege, not language output. The implication is that AI governance now belongs in IAM, PAM, and segmentation planning, not only in AI policy discussions.
A few things that frame the scale:
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, which shows that the control gap is behavioural as well as technical.
A question worth separating out:
Q: Who is accountable when an AI system causes an incident?
A: Accountability should sit with the team that approved the access scope, monitored the runtime boundary, and owned the response path. If no group can answer those three questions, the programme has treated AI as a feature instead of an identity-bearing actor. Clear ownership is a governance requirement, not an optional add-on.
👉 Read our full editorial: CISO AI uncertainty at RSAC 2026 reveals a control gap
