Government agencies are adopting AI across citizen services, benefits eligibility, case management and internal knowledge systems. These applications often process sensitive records and untrusted public content, creating security and governance risks that traditional controls may not fully address.
Cygeniq helps public sector organizations discover AI assets, assess risk, test AI systems against real-world threats, enforce runtime guardrails, govern AI risk and detect AI-native attacks through one Runtime AI Trust Platform.
One platform across citizen-facing and internal AI
Government AI systems increasingly interact with citizen records, submitted documents, agency knowledge and operational workflows.
A malicious instruction hidden inside an application, appeal or correspondence can manipulate an AI system processing that content. AI agents may expose information across records or perform unintended actions. Third-party AI can also operate without complete visibility into its risk and behavior.
AI is treated like other back-office software, so existing document security and records-access controls fully cover it.
AI processing citizen submissions can be manipulated by instructions hidden in that content, and can expose or act on sensitive records without triggering a conventional security alert.
This creates risks including:
Public sector AI security needs to protect AI systems while maintaining the visibility, accountability and evidence expected from government technology.
AI supporting eligibility or case decisions requires appropriate risk practices, human oversight and evidence.
Applications, appeals and correspondence are untrusted content that can introduce malicious instructions into AI workflows.
AI assistants accessing agency records may expose sensitive information when manipulated through prompts or retrieved content.
AI systems missing from the inventory can also remain outside risk assessment, governance and reporting processes.
Third-party AI still requires sufficient visibility into performance, security and risk for agencies to govern its use.
One connected sequence, from discovery through defense, mapped to the modules that deliver each step.
Create an AI Bill of Materials covering models, agents, prompt flows, data sources and APIs, including acquired and contractor-operated AI, with ownership, data classification and risk tiering to support a continuously maintained AI inventory.
Assess risk at the individual AI asset level based on factors such as rights impact, data access and system authority, providing structured evidence behind AI risk determinations.
Connect AI assets with controls, risk information and supporting evidence. Controls can be mapped across relevant government AI policies and frameworks to support more continuous governance.
Run adversarial testing against citizen-facing and case-handling AI for prompt injection, unauthorized information extraction and unsafe role manipulation.
Monitor prompts, outputs and agent actions and apply runtime guardrails while AI systems operate. Changes to models, prompts or retrieval sources can trigger renewed testing.
Provide visibility and traceability for AI-native attacks and connect AI security insights with existing security operations.
AI phishing and BEC against agency staff, deepfake impersonation of officials and citizens, AI-generated malware and autonomous intrusions, and credential attacks on citizen and employee accounts, detected, blocked and contained under human control.
Book a walkthrough of discovery, adversarial testing, runtime protection and AI-native detection for government AI.
| AI Use Case | Key AI Security Risk |
|---|---|
| Benefits & Eligibility AI | Decision risk, insufficient oversight and missing evidence |
| Citizen Service Agents | Sensitive information exposure and unsafe responses |
| Case Management AI | Cross-record access and manipulated AI behavior |
| Document Processing | Prompt injection through applications and correspondence |
| Internal Knowledge Assistants | Unauthorized information retrieval and data exposure |
| Third-Party AI | Limited visibility into AI risk and behavior |
| Agency-Wide AI Adoption | Shadow AI and incomplete AI inventories |
Controls and evidence map once, in GRCortex AI, to the regimes an agency answers to: the NIST AI RMF, OMB AI-use and acquisition memorandums and agency AI use-case inventories, FedRAMP for cloud services, the EU AI Act (public-authority uses such as benefits eligibility are high-risk), the UK Algorithmic Transparency Recording Standard and ISO/IEC 42001, with technical testing aligned to the OWASP LLM and Agentic Top 10 and MITRE ATLAS.
Continuous adversarial red teaming from a 1M+ scenario library, model validation and drift monitoring for every model, copilot and agent in scope, with findings flowing into GRCortex AI as evidence and into CyberTix AI as detection context.
Dynamic AI risk register fed by runtime findings, controls mapped once across every framework in scope, continuous control monitoring, audit-ready evidence and board-ready AI assurance.
The runtime security layer for enterprise AI. CyberTix Secure discovers every LLM, RAG pipeline and agent, blocks AI-native attacks inline and contains threats in real time. CyberTix Defend stops AI phishing and BEC, deepfakes, AI malware and autonomous intrusions, one backbone, one AI-risk picture.
Discover → Understand → Validate → Protect → Govern → Monitor → Assure → Improve
A public sector agency runs Cygeniq across citizen-facing and case-management AI, with GenAI security incidents reduced, audit preparation effort cut substantially and AI-specific attacks detected in minutes rather than days, compliance evidenced continuously.
Reference calls available under NDA.Common risks include malicious instructions in citizen-submitted content, sensitive record exposure, unsafe AI agent actions, incomplete AI inventories and third-party AI risk.
Agencies can identify AI assets, classify their risk, test systems against AI-specific attacks, apply runtime guardrails and maintain governance evidence.
GRCortex AI connects AI assets with ownership, risk, controls and supporting evidence to help agencies maintain structured AI governance.
Hexashield AI tests AI applications against direct and indirect prompt injection and provides runtime controls across AI interactions.
Yes. Acquired and contractor-operated AI is discovered and inventoried alongside in-house systems, red-teamed on the same basis, and governed with the same controls and evidence, with the AI bill of materials capturing supplier, model and data lineage.
No. Cygeniq adds AI-specific discovery, testing, runtime protection, governance and threat detection alongside existing cybersecurity controls.
Gain visibility into government AI, understand its risk, test it against real-world attacks and maintain controls as AI systems evolve.
Cygeniq helps public sector organizations secure AI systems, govern AI risk and prove control.
AI Trust Infrastructure for secure, governed and accountable enterprise AI
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