AI Security

Robert Scoble & Irena Cronin
June 03, 2025

AI is no longer a theoretical ambition—it is a foundational layer in global infrastructure. From autonomous systems and predictive analytics to content generation and national defense, AI is reshaping how decisions are made, data is processed, and value is created. But as AI systems grow in capability and scale, so too does the imperative to secure them.

AI security is not just about protecting models from being hacked. It spans data integrity, algorithmic trustworthiness, system robustness, access control, and regulatory alignment.

1. What is AI Security?

AI security refers to the set of principles, technologies, and practices that protect AI systems from malicious manipulation, unauthorized access, unintended behavior, and systemic vulnerabilities. It encompasses the security of:

Training data (to prevent poisoning and leaks)
Models and architectures (to defend against theft or inversion attacks)
Outputs (to reduce harmful or misleading content)
Deployment environments (to harden endpoints and APIs)
Usage policies (to ensure AI is used safely and ethically)

The security landscape for AI is fundamentally different from traditional software systems because AI systems learn from data. They adapt. They generalize. And they operate with a level of complexity that is often difficult to fully audit or interpret.

2. Key Risks in AI Security

2.1 Data Poisoning Attacks

One of the most subtle and dangerous threats to AI systems lies in data poisoning—intentionally introducing corrupt or misleading data into a training dataset to bias or disrupt the model. Attackers can:

Inject mislabeled examples into open datasets
Subtly manipulate text or images to teach false associations
Exploit data curation pipelines that lack verification

For example, poisoning a facial recognition system with altered faces can degrade its accuracy or introduce backdoors that allow evasion by specific individuals.

2.2 Model Inversion and Membership Inference

AI models, especially those trained on sensitive personal data, can leak private information through:

Model inversion attacks: where an attacker can reconstruct training data from outputs
Membership inference: where attackers can determine if a particular sample was used during training

These attacks are especially concerning for medical, financial, and biometric AI systems, where data privacy is paramount.

2.3 Adversarial Examples

Adversarial attacks involve crafting inputs that appear normal to humans but fool AI systems. A slight alteration in an image—imperceptible to the human eye—can cause a computer vision model to misclassify it entirely.

This vulnerability is not academic. In real-world conditions, adversarial examples can cause:

Misidentification in surveillance systems
Failure in autonomous vehicle object detection
Evasion of malware classifiers

Because adversarial inputs exploit the mathematical foundations of deep learning, defending against them requires more than just input validation.

2.4 Model Theft and Reverse Engineering

AI models can represent years of research and millions of dollars in training compute. Yet, many are exposed via APIs or embedded in products. This makes them vulnerable to:

Model extraction: where repeated queries to an API are used to recreate the model
Intellectual property theft: where weights and architectures are exfiltrated from compromised servers
Parameter stealing: by inferring internal structure from observable behavior

Such theft not only undermines proprietary advantage but also allows attackers to reuse models for malicious purposes.

2.5 Hallucinations and Misinformation

One of the emerging AI risks is content unreliability. Language models can “hallucinate” information—fabricating facts with confidence. These hallucinations:

Undermine trust in AI-generated output
Are difficult to detect without human verification
Can be weaponized to spread misinformation

In high-stakes domains like law, journalism, or medicine, ungrounded content from LLMs poses reputational and legal threats.

2.6 Misuse and Autonomous Agents

As agentic AI systems become more capable—executing goals, writing code, automating workflows—they raise a profound risk: autonomous misuse. Consider:

Agents that automatically search for software vulnerabilities and exploit them
Autonomous bots that spread targeted disinformation campaigns
AI-generated phishing or impersonation at scale

These capabilities drastically reduce the cost and increase the precision of cyberattacks.

3. Securing the AI Lifecycle

To address AI security, it’s critical to adopt a lifecycle approach:

3.1 Secure Data Collection and Curation

Use cryptographic signatures to verify dataset integrity
Sanitize and filter data from unknown or unreliable sources
Monitor data pipelines for unexpected anomalies or injections

3.2 Hardened Model Training

Train on secure, access-controlled infrastructure
Apply differential privacy during training to reduce leakage risk
Use robust training techniques (e.g., adversarial training) to resist perturbations

3.3 Model Evaluation and Red Teaming

Perform adversarial testing using automated tools and human red teams
Evaluate robustness across a range of input distributions and edge cases
Simulate malicious usage scenarios

Anthropic, OpenAI, and Google DeepMind all maintain red teams to probe their models for both security and safety vulnerabilities before public deployment.

3.4 Secure Model Deployment

Limit exposure via rate-limiting and monitoring of API endpoints
Apply access controls, authentication, and anomaly detection
Use watermarking or fingerprinting to trace misuse of outputs

3.5 Ongoing Monitoring and Logging

Continuously monitor for abnormal usage patterns or data exfiltration
Apply real-time logging and audit trails for accountability
Enable feedback loops to retrain or patch models when threats evolve

4. The Role of Regulation and Standards

AI security is not just a technical problem—it is a governance and policy issue. Governments are beginning to act, but much remains to be done.

Emerging Frameworks:

EU AI Act: classifies AI systems into risk categories, mandates risk mitigation for high-risk systems
NIST AI Risk Management Framework: a U.S.-focused guideline that includes robustness, resilience, and transparency
OECD AI Principles: promotes secure and trustworthy AI, with international alignment

Standards bodies like ISO/IEC and IEEE are also working on defining secure AI engineering practices. As these mature, we are likely to see mandatory security audits and certification requirements for certain AI applications.

5. AI Security vs AI Safety: A Clarification

AI security deals with protecting systems from threats—external or internal. AI safety, by contrast, deals with ensuring the system behaves as intended, even if it's not under attack.

While distinct, the two are deeply interlinked. An unsafe model (e.g., one prone to hallucinations or manipulation) can be a security risk. Likewise, an insecure model can become unsafe if compromised or misused.

Leading labs are now investing in both fields, recognizing that trustworthy AI must be secure by design and safe by behavior.

6. The Frontier: Securing Frontier AI Models

As large foundation models and agentic systems begin to approach human-level generalization across tasks, securing them becomes existentially important.

Frontier AI security requires:

Advanced alignment testing (are the models pursuing unintended goals?)
Kill switches and containment strategies
Interpretability tools to trace how decisions are made
Pre-deployment red teaming for national security risks

OpenAI, Anthropic, and others have called for joint oversight bodies—possibly under government authority—to coordinate frontier model security testing and response protocols.

7. What Organizations Should Do Today

Whether you're a startup deploying LLMs or an enterprise integrating AI into workflows, here are immediate actions:

Conduct an AI risk assessment: What would happen if your model failed or was compromised?
Limit exposure: Don’t overexpose APIs or rely on open data pipelines
Build a security-informed AI team: Blend ML researchers with cybersecurity experts
Implement feedback loops: Let users report harmful output or suspicious behavior
Stay compliant: Monitor regulatory trends and align with best practices

AI security is not a luxury or an afterthought—it is foundational to the long-term viability of intelligent systems. As the technology becomes more autonomous, more powerful, and more deeply embedded in society, the stakes will only grow.

Securing AI is not just about avoiding breaches or failures. It’s about preserving trust, protecting people, and ensuring the benefits of automation don’t come at the cost of systemic vulnerability.

The time to act is not when a major AI system is exploited—it’s now, while we still have the leverage to shape its trajectory. AI security is not just a technological frontier—it is a societal one.

Just Three Things

According to Scoble and Cronin, the top three relevant and recent happenings

Dario Amodei Warns: AI Could Wipe Out Entry-Level Jobs and Reshape the Workforce

Anthropic CEO Dario Amodei warns that AI could eliminate up to half of all entry-level white-collar jobs within five years, pushing U.S. unemployment to 10–20%. He urges the government and tech leaders to stop downplaying the risk and start preparing for rapid job displacement in sectors like law, finance, and tech. Amodei emphasizes that the shift from augmentation to full automation is already underway, as companies race to adopt agentic AI that can fully replace human workers. Without urgent public awareness, policy solutions, and equitable economic strategies, he fears growing inequality and a weakening of democracy. Despite AI’s potential for good, Amodei says the time to steer its impact is now. Axios

Meta Replaces Human Risk Reviews with AI, Raising Oversight Concerns

Meta is automating up to 90% of its product risk reviews, shifting decisions about privacy, safety, and misinformation from human experts to AI systems. The goal is to speed up feature rollouts across Facebook, Instagram, and WhatsApp, with instant approvals based on AI-analyzed questionnaires. While Meta says humans will still review high-risk or complex cases, critics—both inside and outside the company—warn this move reduces oversight and increases the risk of harm. Internal documents show even sensitive areas like youth safety and AI integrity could be affected. The shift aligns with Meta’s broader push to move faster and compete with platforms like TikTok, but raises concerns about weakening key guardrails. NPR

Elad Gil’s New AI Bet: Reinventing Traditional Businesses Through Roll-Ups

Elad Gil, a prominent early investor in AI startups like Perplexity, Character.AI, and Harvey, is now focusing on using AI to transform traditional businesses through roll-ups. His strategy involves acquiring mature, labor-intensive companies (like law firms), applying generative AI to automate processes and boost margins, and then using the enhanced profitability to buy more firms. Gil believes AI can fundamentally change cost structures, unlike earlier tech-enabled roll-ups that added only superficial improvements. He’s already backed companies like Enam Co. and continues to bet on AI leaders in sectors like law (Harvey), healthcare (Abridge), and customer service (Sierra AI). While acknowledging challenges in team composition and competition, Gil sees clearer winners emerging across verticals and remains deeply engaged in experimenting with AI technologies to stay ahead. TechCrunch