Protect Elections, Healthcare and National Security: A Practical Plan to Reduce AI Risks

AI risks to national security, elections, and healthcare - and how to reduce them

AI is now embedded in daily life. It speeds up drug discovery and personalizes care, but it can also help identify elements for biological weapons, fool vehicle vision systems, and amplify attacks on critical infrastructure.

The risks are varied and specific. They include biased training data, adversarial attacks that flip model outputs, misinformation at scale, market manipulation, and low barriers to abuse because high-capability tools are widely available.

Where the risks hit hardest

Elections and markets. Misinformation is cheaper and faster than ever. Fake accounts and targeted content can sway voters; Romania's 2024 presidential elections were suspended amid foreign interference. In finance, fabricated stories move prices: an AI-generated image of an explosion near the Pentagon in 2023 reportedly triggered a brief sell-off. Adversarial tweaks can even game AI credit models, approving the wrong applicants.

Healthcare. During COVID-19, false claims about vaccines and lockdowns spread quickly and eroded trust. Bias in clinical AI can deny care to underrepresented groups; a Cedars-Sinai study found several large language models suggested worse psychiatric treatments when a patient was described as African American. Hospitals now have larger attack surfaces, making them prime targets for ransomware and data theft.

National security. The war in Ukraine shows how drones, automated targeting, and AI-enabled cyberattacks change conflict. Energy grids and transit networks have been disrupted. Information operations use AI to confuse enemies and sway public opinion. Training and running large models also consumes significant energy, adding another layer of pressure on resources.

What individuals can do right now

• Choose AI providers that comply with established security and privacy standards (for example, ISO 27001, SOC 2, HIPAA where applicable) and publish bias and safety evaluations.
• Check claims, not vibes: ask for sources, verify with trusted outlets, and report deceptive content or model abuses when you see them.
• Limit sensitive data you share with AI systems. Use strong authentication and keep software up to date.
• Be skeptical of sensational images, audio, or headlines-especially near elections or market opens. Look for context and provenance before sharing.

What institutions and companies must put in place

• Model security. Threat modeling, adversarial testing, red teaming, and continuous monitoring. Use adversarial training, detection layers, and fallback rules. Add human-in-the-loop for high-stakes decisions.
• Governance. Define risk tiers by use case, document data lineage, and set approval gates before deployment. Track incidents and near-misses in an internal log for rapid learning.
• Operational defenses. Content provenance/watermark checks, anomaly detection on inputs/outputs, and kill switches for out-of-policy behavior. Tabletop exercises that include legal, comms, and security.
• Equity and safety in health. Validate models across demographics, set minimum performance thresholds by subgroup, and audit for bias. Use external benchmarking and clinical oversight before and after rollout.
• Business continuity. Segment networks, back up critical models and data, and practice recovery. Procure cyber and AI-specific insurance that covers adversarial attacks and model failure.
• Workforce readiness. Train teams on secure AI use, data handling, and prompt hygiene. Build playbooks for misinformation surges, market rumors, and deepfake escalation.

What governments should do

The World Economic Forum lists adverse AI outcomes as a major global risk. A risk-based legal approach-like the EU's AI Act-sets clearer duties for high-risk systems while allowing lower-risk innovation to proceed with guardrails.

• Adopt risk-based rules, procurement standards, and audits for systems used in public services, healthcare, and elections.
• Fund research in secure and privacy-preserving machine learning, content provenance, and evaluation benchmarks.
• Build channels for cross-border data sharing and incident reporting. Encourage contributions to open incident registries.
• Support energy efficiency standards and reporting for large model training and deployment.

WEF Global Risks Report and the EU AI Act overview provide useful context for policy and compliance planning.

Sector-specific quick checks

• General public. Verify before you share. Use reputable tools, turn on two-factor authentication, and treat AI outputs as drafts until you can confirm.
• Government. Require suppliers to meet security and bias-testing standards. Stand up an AI incident response function that coordinates with election authorities, energy, finance, and health agencies.
• Healthcare. Demand demographic performance reports, human review for high-risk outputs, and clear escalation paths. Protect PHI with strict data minimization and de-identification.

The path forward

AI's upside is real, but so are its threats. With better choices by individuals, stronger controls in organizations, and smart regulation, we can reduce the downside while keeping the benefits.

If your team needs practical training to build these capabilities, explore role-based options at Complete AI Training.