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Security tools, privacy protection, and threat detection
6 Problem Statements in this Domain (5 Standard + 1 Open Innovation)
Government digital services increasingly span multiple cloud providers and internal systems, each with its own Identity and Access Management (IAM) model, and the effective attack surface is not any single misconfiguration but the combinatorial chain of role assumptions, cross-account trust relationships, and service-to-service permissions that can let a low-privilege compromise escalate into a high-impact breach. These attack paths change continuously as roles, policies, and services are added or modified, making static one-time audits quickly stale, while the actual graph of "what can reach what" across heterogeneous cloud IAM models is too complex for manual review.
Public infrastructure increasingly relies on IoT devices (smart meters, traffic sensors, public safety cameras) sourced from many vendors, some with closed firmware and limited security transparency, and operators currently have very limited ability to verify, at scale and after deployment, whether firmware running on fielded devices has been tampered with or contains malicious functionality — since they typically lack source code, vendor cooperation, or the ability to take devices offline for deep inspection.
Government digital platforms are built on deep, often unmanaged trees of open-source dependencies, any of which could introduce vulnerabilities or, increasingly, deliberately malicious code injected via compromised maintainer accounts or build pipelines — but current practice largely relies on known-CVE scanning, which cannot detect novel malicious behavior or assess the actual reachability/exploitability of a vulnerability within a specific government application's real usage pattern. Meaningfully prioritizing supply-chain risk requires reasoning about which vulnerable code paths are actually reachable and exploitable in context, across an enormous and constantly-updating dependency graph.
Fraud and cyberattack patterns often only become visible when data is compared across multiple institutions (e.g., the same attacker probing several banks), but institutions are legally and competitively unable to share raw customer or security data with each other or a central body, severely limiting collective threat detection to what each institution can see in isolation. The challenge is enabling genuinely useful cross-institutional anomaly detection while provably not exposing any institution's raw sensitive data to the others or to a central aggregator.
As AI models are increasingly embedded into critical infrastructure decision-making (grid load prediction, water treatment control, traffic signal optimization), they introduce a new attack surface: adversarial inputs or subtle data-poisoning designed to manipulate model outputs in ways that look statistically plausible but drive the system toward unsafe or attacker-favorable decisions. Most operators currently have no runtime mechanism to detect when a deployed model's behavior has been subtly compromised or is being actively manipulated, as opposed to simply performing poorly due to natural data drift.
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