AI Security for Government and Public Sector

The Risks of AI in Government Agencies

AI technology analyzes large datasets to identify new patterns and deliver fresh insights that previously went unnoticed, offering new recommendations and improving decision-making. Generative AI goes further, creating new content based on the patterns and connections observed in a model’s training data. This enables language-based interactions and rapid content generation, facilitating a range of exciting new use cases.

Regardless of the use case, at the core of any AI innovation is data analysis beyond what was previously possible. Public sector agencies hold large amounts of data that, when combined with cutting-edge AI applications, could significantly enhance government services and improve operational efficiency.

However, this data is incredibly sensitive, related to private citizens, vital government services, and the critical infrastructure behind these services. This data is also subject to stricter laws and regulations that define how agencies can use and share information while maintaining data security.

Due to their access to classified information, infrastructure, and public policy decisions, government agencies are prime targets for cyberattacks, including those carried out by nation-states with significant resources and capabilities beyond those of typical cybercrime groups.

Many public sector organizations must also operate within tight budgets while relying on a mix of digital infrastructure acquired over many years, some of which are outdated for today’s operations and security needs. The difficulty of patching or retrofitting security measures further complicates current AI defense strategies.

These factors combine to make AI security for government agencies vitally important. Unfortunately, the stakes are generally higher in the public sector, and agencies need robust protections to prevent, detect, and mitigate today’s AI security risks.

Common AI Security Threats

As AI technology is embedded in government operations, it also presents new security risks that must be accounted for. AI security threats come in many forms, from direct attacks leveraging AI tools to vulnerabilities in the design or implementation of AI technologies. Understanding these risks is key to developing effective AI security practices for government agencies.

Attacks Using AI

AI is enhancing many cyberthreats, enabling more sophisticated, better-targeted attack vectors that increase the likelihood of breaching public sector networks and systems. Examples include:

• AI-Driven Social Engineering Attacks: Generative AI can be leveraged for advanced deepfakes, more convincing phishing messages, and automated social engineering campaigns. For example, AI models can generate fake audio, video, or text to impersonate officials, deceive citizens, or manipulate public opinion. With access to models that can rapidly generate next-generation social engineering content, AI makes it harder to detect and mitigate these attacks.

• Evasive Malware Strains: AI is enabling attackers to develop new malware strains that avoid traditional detection techniques and increase the success rate of cybercrime campaigns. This includes polymorphic malware that changes its code to bypass static signature-based detection methods.

• Faster, Personalized Ransomware: AI-supported ransomware campaigns enable cybercriminals to act more quickly while incorporating personalized extortion tactics. AI automation also improves ransomware efficiency, reducing attack timelines.

Attacks Targeting AI Systems

AI systems offer a new attack surface for cybercriminals to target. This includes manipulating models to reveal sensitive data or directly impacting model performance. Whether it is internally developed or fine-tuned models trained on government data, public sector employees sharing sensitive data without proper data loss prevention procedures in place, or AI agents with direct access to critical data stores, there are many ways AI usage can lead to data leaks.

Research from the Check Point 2026 Cybersecurity Report found that risky prompts increased by 97% in 2025, with 90% of organizations experiencing the issue, and 1 in 41 prompts classified as high-risk.

Model-level attacks include adversarial attacks, in which malicious inputs are fed to AI models to trick them into making incorrect decisions, and data poisoning, in which training datasets are altered to reduce model accuracy or introduce biases. These attacks can compromise the integrity of critical government services and skew decision-making processes, thereby impacting potential outcomes.

Design and Implementation Failures

AI security vulnerabilities can also arise from flaws in the design and implementation of AI systems themselves. Poorly trained models, inadequate data protection, or a lack of oversight during development can lead to systems that are vulnerable to exploitation. For instance, an AI model might rely on biased data, leading to discriminatory outcomes, or it might lack proper security measures, leaving it open to unauthorized access.

Additionally, the rapid deployment of AI systems without sufficient testing or auditing can introduce unforeseen risks, especially if these systems are not continuously updated to address new threats. In the public sector, where AI is often used to manage sensitive data, such design and implementation failures can have dire consequences for national security, public trust, and privacy.

Public Sector AI Security

AI security for government agencies demands a purpose-built approach that reflects the sensitivity, scale, and criticality of public-sector systems. Governments must protect everything from everyday generative AI use by staff to advanced AI agents supporting intelligence, healthcare, transportation, and citizen services. At the same time, agencies can leverage AI itself to strengthen their security posture, automate protection, and minimize cyber risk.

A modern approach to AI security for government should include the following capabilities:

• Comprehensive AI Visibility: Agencies must maintain full awareness of where and how AI is being used. This includes identifying unauthorized or unsanctioned AI tools (“shadow AI”) that could expose sensitive or classified data. Visibility enables consistent enforcement of public sector security controls and data handling requirements.
• AI-Focused Data Protection: Protecting sensitive government data, such as PII, health records, law enforcement data, and classified information, requires AI-aware data loss prevention programs. This means identifying and safeguarding sensitive data as it moves through AI workflows, even when that data is summarized, rephrased, or transformed by model outputs.
• Runtime Monitoring and Threat Detection: Government AI systems should be continuously monitored to detect misuse, manipulation, or anomalous behavior. Effective runtime protections distinguish between legitimate usage and suspicious activity, reducing false alarms while enabling rapid response to real threats.
• Controlled Model Interaction and Safeguards: Agencies can reduce risk by enforcing strict controls on how AI models are accessed and used. Input sanitization, predefined constraints, and usage limits help prevent prompt injection, data leakage, and unintended model behavior, especially in high-impact government applications.
• Adversarial Testing and Red Teaming: Proactively stress-testing AI systems through red teaming helps agencies uncover vulnerabilities before adversaries do. By simulating real-world attack scenarios, government organizations can validate the resilience of any AI systems supporting critical missions.
• AI Agent and Supply Chain Security: As agencies adopt AI agents that interact with internal systems or third-party tools, additional safeguards are required. These include isolating agent toolchains, enforcing strict authorization, and closely monitoring integrations with vendors and partners across the government ecosystem.
• AI-Enhanced Threat Prevention and Response: AI can also strengthen government cybersecurity by improving detection accuracy and response speed. Behavioral analysis powered by machine learning enables agencies to identify zero-day threats, insider risks, and sophisticated attacks that evade traditional, signature-based defenses.
• Strong AI Governance and Compliance: Effective AI security in the public sector depends on clear governance frameworks. Agencies must define ethical AI use, enforce consistent security policies, and maintain detailed audit logs to meet regulatory, legal, and oversight requirements.