Topic starter
24/10/2026 8:52 pm
TL;DR: AI-driven attacks are accelerating as attack surfaces expand across cloud, SaaS, AI systems and external digital assets, according to Infoblox from a global survey of 550 cybersecurity professionals, while teams struggle to turn improved visibility into meaningful risk reduction. The practical shift is from seeing more to governing exploitability.
Editorial analysis by NHI Mgmt Group, based on content published by Infoblox: “Securing the Expanding Attack Surface in the Age of AI”.
By the numbers:
- Infoblox based the report on interviews with over 550 cybersecurity professionals.
Key questions
Q: What breaks when exposure management only measures visibility instead of risk reduction?
A: Teams end up with more findings but no better decision-making.
Q: Why do cloud and SaaS identity paths increase exposure risk so quickly?
A: Because access scope, trust relationships, and configuration drift can combine into a routable attack path in minutes or hours.
Q: How can security teams tell whether a fix actually reduced exposure?
A: They should test whether the original attack outcome can still be reproduced after remediation.
Practitioner guidance
- Prioritise exploitable exposures first Rank exposed assets by reachability, privilege and business criticality, then remediate the items that can be used immediately rather than the loudest findings.
- Govern delegated access paths Inventory SaaS, cloud and AI integrations that rely on tokens, webhooks, service accounts or other non-human credentials, and assign owners for lifecycle review.
- Constrain externally reachable identities Reduce standing privilege and remove unused tokens or accounts that create internet-facing paths into internal systems, especially where services interconnect across domains.
Bottom line: AI-driven threat activity is forcing exposure management to move beyond inventory and toward exploitability.
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