Artificial intelligence (AI) systems have seen massive growth and adoption across industries in recent years. From chatbots to autonomous vehicles, AI is powering numerous applications and services that are transforming business and society. However, as AI becomes more prevalent, these systems are increasingly targeted by malevolent threat actors and subject to risks from errors, biases, and unintended harm. Several high-profile incidents have demonstrated that AI systems have unique vulnerabilities that traditional security tools are often ill-equipped to address. 

In this blog post, we will dive deeper into the emerging risks and threats targeting AI systems and the key challenges involved in securing these complex technologies. We will also discuss recommended best practices organizations can follow to safeguard their AI projects and deployments. As AI continues proliferating into critical business, governmental and social functions, adopting robust security measures tailored for machine learning pipelines and models will only grow in importance.

AI System Risks and Threats 

AI systems face a diverse array of potential risks and attack vectors that security teams must address. Some of the most concerning include:

Data poisoning attacks: Adversaries can compromise training data pipelines to inject false, manipulated or harmful data. This poisons and corrupts the model’s learning process, causing it to exhibit unsafe or biased behaviors at inference time. For example, introducing doctored images with backdoor triggers into autonomous vehicle training sets could cause accidents at test time.

Model stealing attacks: Attackers with API access can probe AI models to steal information about their architecture, parameters and training data. This intellectual property theft enables copying the model’s behaviors and commercial value. For instance, confidential healthcare predictions models could be extracted and misused by malicious actors.

Adversarial examples: Carefully crafted, imperceptible perturbations to inputs can fool models and trigger incorrect classifications or predictions at inference time. For example, modified stop signs can trick autonomous vehicles into misidentifying them, creating safety risks. 

Backdoor attacks: Attackers can inject hidden backdoors into models to trigger targeted misclassifications or undesirable behaviors when specific inputs are encountered. For example, a backdoored chatbot may respond dangerously when secret trigger phrases are submitted by the attacker.

Hardware and infrastructure risks: AI systems rely on extensive data centers, cloud platforms and hardware like GPUs. Compromising any component of this infrastructure—through hacking, malware or even physical attacks—can severely disrupt AI services and availability.

Privacy and bias risks: Poorly secured AI data pipelines, flawed training practices and lack of oversight can lead to privacy breaches, unauthorized data usage, or baked-in biases that cause harmful discrimination.

Malicious use risks: Widely available AI frameworks lower barriers for threat actors seeking to misuse AI capabilities for criminal, state-sponsored or unethical goals.

These threats intersect with the broader vulnerabilities of the complex software, infrastructure and organizational workflows that AI systems are embedded within. Security teams must consider risks across the entire AI lifecycle and stack to protect these mission-critical capabilities.

Key Challenges for Securing AI

Securing AI systems poses distinct challenges not found in traditional information security domains:

Complexity of AI systems: State-of-the-art AI often leverages an intricate stack of data ingestion, preprocessing, model training, optimization and deployment components. This complexity expands the attack surface and makes vulnerabilities harder to track.

Lack of transparency and explainability: Many AI techniques like deep learning are complex black boxes, making it difficult to audit for flaws or behavioral changes indicative of threats. Their decision-making processes remain opaque. 

Difficulty detecting threats: Unlike traditional malware, threats like adversarial examples or backdoors are subtle and precisely tuned to avoid detection. Their novel, rapidly evolving nature also defies many standard security tools.

Rapid evolution of attacks: Open dissemination of AI research coupled with the computational power available to attackers has led to a meteoric rise in sophisticated attack techniques that outpaces defenses.

Scarcity of security tools and practices: Since AI security is an emerging domain, mature standards, tools, guidelines and architectures tailored to AI pipelines remain scarce compared to traditional IT security.

The unique capabilities and constraints of AI systems demand equally innovative security solutions. However, awareness and investment in AI security still lags behind its growing criticality. Next we will explore leading practices aimed at overcoming these challenges.

Best Practices for Securing AI

Organizations must holistically integrate security across the entire AI development and operational lifecycle to minimize risks. Key recommended best practices include:

Governance and risk management: Adopt formal governance policies, risk assessment processes, and continuous monitoring to oversee and guide AI security strategies tailored to your environment and risk appetite. 

Design security from the start: Build security into AI systems from initial design through deployment, leveraging techniques like privacy-preserving federated learning and differential privacy rather than bolting it on later.

Enhance transparency and explainability: Continuously evaluate models using methods like LIME and Shapley values to improve visibility into model behavior and more easily detect emerging threats.

Continuously monitor for threats: Actively probe for data poisoning, model extraction, adversarial examples and other threats throughout the development and production lifecycle.  

Adopt defense-in-depth protections: Safeguard AI infrastructure, data, and models through multiple embedded technical controls at all layers rather than relying on a single protection method. 

Validate and test extensively: Rigorously vet all components via techniques like red teaming, automated testing, simulations, sandboxing, and formal verification to identify vulnerabilities and build resilience.

Control access and integrations: Limit access to sensitive training data, models and infrastructure to only authorized personnel and processes to reduce insider and third-party risks. 

Prepare incident response plans: Develop detailed response plans and exercises focused on AI security incidents to enable rapid, effective containment and recovery in the event of attacks.

Maintain human oversight: Keep humans in the loop via oversight mechanisms like human-on-the-loop confirmation of model behaviors to detect potential harms.

Prioritize privacy and ethics: Adopt trusted data practices and tools to fulfill privacy obligations, promote fairness and ensure models behave ethically.

Achieving AI security requires a holistic approach spanning the technology, people, process and governance domains. Organizations must specifically adapt their security programs to address ML/AI pipelines and workflows. By proactively investing in tailored safeguards today, we can realize the benefits of AI while minimizing emerging risks.

The Future of AI Security

As AI adoption grows, so will initiatives to secure it against misuse and harm:

Evolving regulatory landscape: Governments are PAY extra attention to how AI threats intersect with laws like privacy statutes. Expect more AI-focused security regulations, especially for high-risk sectors.

Advances in AI security tools: Security startups and research will drive advances in AI-tailored defenses, testing, monitoring and governance to make protection more effective and scalable. 

Increased focus on trust and transparency: To address ethical and reliability concerns, transparent AI design principles will gain prominence alongside tools to verify model behaviors.

Mainstreaming of security in ML workflows: Practices like adversarial training, differential privacy, and secure enclaves will increasingly be built into default ML pipelines and frameworks.

Closer collaboration between security, ML and ethics: Cross-disciplinary teams spanning these domains will become the norm as organizations recognize their interdependencies.

Conclusion

This post provided an overview of the emerging risks threatening AI systems, the unique security challenges they pose, and recommended best practices organizations should follow to protect their AI investments. As AI capabilities become further enmeshed in business and society, the consequences of attacks grow more severe. However, by proactively collaborating across specialties and integrating tailored solutions across the AI lifecycle, we can secure AI and minimize risks of misuse and harm. Though threats will continue evolving, maintaining a robust, adaptable security foundation provides the best safeguard as AI propels progress across industries.