Artificial intelligence (AI) technologies hold immense potential to revolutionize various sectors. However, as AI systems become more pervasive, they also present unique cybersecurity challenges and risks that legacy security measures often struggle to address, adversely affecting individuals, groups, organizations, communities, society, the environment, and the planet.
According to a Webroot report, over 90% of cybersecurity professionals expressed concerns about hackers using AI to launch increasingly sophisticated and difficult-to-detect cyberattacks. Similarly, in a survey by CyberArk, 93% of cybersecurity professionals expect AI-enabled threats to impact their organization.
In a rapidly evolving technological landscape, governments and regulators are introducing AI guardrails. The National Institute of Standards and Technology (NIST) leads this initiative by establishing comprehensive guidelines and frameworks to enhance the security, reliability, and ethical use of AI systems, ensuring they are robust against emerging cybersecurity risks and aligned with best practices.
This blog delves into how NIST AI guidelines aim to mitigate the cybersecurity risks associated with AI, providing a framework for organizations to safeguard their AI systems against emerging threats.
Exploring AI Cybersecurity Risks
AI cybersecurity risks refer to potential vulnerabilities and threats that can undermine the integrity, security, and reliability of AI systems. AI cybersecurity risks are engineered to target the processes, models, and data utilized in the development, deployment, and utilization of AI systems with the aim of disrupting these systems' regular operations or obtaining sensitive data. The risks comprise a diverse array of malicious activities and vulnerabilities within systems that may take advantage of AI systems, resulting in data breaches, unauthorized access, system manipulation, and other adverse impacts.
Common AI-Specific Threats
Data Poisoning
A malicious actor introduces errors or biases into an AI system by manipulating its training data. For instance, they might modify a dataset utilized in the training process of a facial recognition system to cause it to identify individuals incorrectly.
Adversarial Attacks
Attackers trick AI algorithms into generating inaccurate predictions or classifications by feeding them well-constructed data. For example, they might slightly alter a picture such that, despite its appearance to human viewers, an AI system misclassified it.
Model Inversion Attacks
Attackers try to piece together sensitive input data from a machine-learning model's outputs. For example, they might obtain personal data from a model that was developed using sensitive data.
Model Stealing
Attackers use recurrent queries and output analysis to create an AI model. For example, without having access to the code or data of the original model, developing a copycat model that imitates the actions of a proprietary AI system.
AI System Manipulation
Attackers exploit vulnerabilities in the AI system’s decision-making processes, such as tampering with the data sets to make inaccurate decisions.
Privacy Attacks
Attackers obtain sensitive personal data from AI models or datasets.
Trojan Attacks
Attackers introduce covertly malicious behavior into an AI model that is prompted by certain commands. For example, a backdoored AI system that acts maliciously when given certain inputs but functions properly in other situations.
AI cybersecurity risks have wide-ranging, potentially devastating impacts on individuals and organizations. These risks can lead to unauthorized access, sensitive data breaches, and privacy violations, which can lead to financial losses and reputational damage, especially in sectors like healthcare, finance, and autonomous systems.
Introduction to NIST AI Guidelines
NIST offers a framework for businesses engaged in designing, developing, deploying, or using AI systems to help manage AI risks and promote trustworthy and responsible development and use of AI systems. Key NIST publications relevant to AI security include the NIST AI Risk Management Framework and the NIST Cybersecurity Framework.
Key Aspects of NIST Guidelines for AI Security
AI Risk Management Framework
Managing AI risk is an essential element of developing and using AI systems responsibly. The NIST AI RMF is designed to provide a structured approach to managing the risks associated with AI systems. The AI RMF comprises several key components:
- Governance - Develop and implement a risk management culture within organizations working with AI systems. It outlines methods, processes, documents, and plans to forecast, identify, and manage risks to ensure responsible AI development and deployment.
- Map - Establishes the context for framing risks to an AI system, enabling organizations to proactively develop trustworthy AI systems.
- Measure - Assessing the performance, effectiveness, and risk levels of AI systems.
- Manage - Implementing strategies to mitigate identified risks, continuously monitoring AI systems, and adapting to emerging threats.
By integrating these components, the NIST AI RMF helps organizations systematically address AI-related risks, promoting the development of secure, reliable, and ethical AI systems.
Learn more about the NIST AI RMF.
Security Controls for AI Systems
Security controls are critical components of AI systems as they ensure the confidentiality, availability, and integrity of AI technologies and the data they contain. These controls include a wide variety of practices, such as adversarial training to combat adversarial attacks, strong data validation and sanitization processes to avoid data poisoning, and encryption methods to protect sensitive data.
Additionally, access controls and authentication mechanisms are crucial to ensure that only authorized individuals can access or modify AI systems and their underlying datasets. Regular security assessments, monitoring, and updates are also critical to identify and mitigate emerging threats, ensuring the AI systems remain secure and resilient against evolving cyber threats.
For AI systems to be more secure and resistant to cyberattacks, NIST recommends several security controls outlined in numerous publications. These controls include:
Data Management Controls
Data Validation and Sanitization: Implement robust validation and sanitization processes to identify and eliminate malicious or inaccurate data to ensure data quality.
Data Encryption: Encrypt data at rest and in transit to protect personal and sensitive data against unauthorized access and tampering.
Model Security Controls
Adversarial Training: Train AI models to identify and protect adversarial inputs that try to trick the AI system.
Regular Model Updates: Schedule regular updates to the AI models to integrate evolving threat intelligence algorithms and enhance resilience against evolving threats.
Access and Identity Controls
Role-Based Access Control (RBAC): Introduce an RBAC system that restricts system access to authorized individuals based on their roles and responsibilities within the organization.
Multi-Factor Authentication (MFA): Similar to 2FA, implement MFA to add additional guardrails and enhance the security of user access to AI systems.
System Monitoring and Response Controls
Continuous Monitoring: Utilize continuous monitoring technologies to swiftly identify and address any unusual activity detected by the system that may compromise the system’s integrity, confidentiality, and security and result in a potential security breach.
Incident Response Planning: Swiftly resolve security incidents by creating and maintaining an incident response strategy specifically designed for your AI systems.
Algorithm and Model Management Controls
Explainability and Transparency: Ensure AI models are easy to understand and transparent to facilitate ease of use and trustworthiness.
Bias Detection and Mitigation: Establish tools and processes to detect and mitigate biases within AI models.
Governance and Compliance Controls
Policy Development: Establish comprehensive security policies governing the use and management of AI systems.
Compliance Audits: Conduct regular audits to ensure AI systems comply with applicable security standards and evolving regulations.
How Securiti Can Help
Securiti’s Data Command Center enables organizations to comply with the NIST AI RMF by securing the organization’s data, enabling organizations to maximize data value, and fulfilling an organization’s obligations around data security, data privacy, data governance, and compliance.
Organizations can overcome hyperscale data environment challenges by delivering unified intelligence and controls for data across public clouds, data clouds, and SaaS, enabling organizations to swiftly comply with privacy, security, governance, and compliance requirements.
Request a demo to witness Securiti in action.
