Understanding AI Security

While organizations feel competing pressures to accelerate the use of AI, the rapidly evolving technology brings new levels of concern and responsibility to their data security practices. Data is one of the most valuable assets for any organization, and it must be protected to ensure the security of AI systems. Organizations must implement robust security protocols, encryption methods, access controls and monitoring mechanisms to safeguard AI assets and mitigate potential risks associated with their use. But managing AI security and risk goes even deeper.  

AI security refers to the practices, measures and strategies implemented to protect artificial intelligence systems, models and data from unauthorized access, manipulation or malicious activities. Concerns about bias, hallucinations, transparency, and trust, along with the ever-changing regulatory landscape, make it challenging to effectively test and monitor AI systems.

As daunting as that may seem, AI can also aid in your security initiatives with the ability to automate protections and fix vulnerabilities. AI is being used to address every phase of cybersecurity, including: 

• Real-time data analysis for fraud detection and other malicious activities
• Adversarial testing to learn how a model behaves when provided with harmful input to guide mitigation
• Risk identification/assessment with the ability to analyze vast amounts of data to identify potential risks
• Risk scoring and categorization with adaptive learning and real-time data processing to evaluate and prioritize risks
• Bias testing to detect disparities in outcomes across different demographic groups
• Pattern recognition for identity verification and threat detection
• Automated tracking to reduce manual efforts and human error, aiding in compliance and risk management
• Risk prediction using predictive modelling to analyze patterns and anomalies that humans might miss
• Threat detection using behavioral analysis and responding by isolating affected devices and blocking malicious activities

Common AI security risks   

Unlike traditional IT security, AI introduces new vulnerabilities that span data, models, infrastructure and governance. It’s important to understand the risks to each component of an AI system:

• Data operations risks resulting from mishandling data and poor data management practices such as insufficient access controls, missing data classification, poor data quality, lack of data access logs and data poisoning.
• Model operations risks such as experiments not being tracked and reproducible, model drift, stolen hyperparameters, malicious libraries and evaluation data poisoning.
• Model deployment and serving risks such as prompt injection, model inversion, denial of service, large language model hallucinations and black-box attacks.
• Operations and platform risks such as a lack of vulnerability management, penetration testing and bug bounty, unauthorized privileged access, a poor software development lifecycle and compliance.

Understanding vulnerabilities specific to AI applications   

It’s also important to understand and identify vulnerabilities relevant to your specific AI use cases instead of analyzing all possible threat scenarios. Different deployment models require different controls. For an explanation of the different AI deployment models and how to align the components of your AI systems with the models deployed and the potential risks, download the Databricks AI Security Framework (DASF)

Impact of security risks on organizations   

AI systems are complex and can operate with little human oversight. AI security problems can be costly in ways that go well beyond the successful data security attacks of recent years. Unsafe data handling can still reveal personal data and present privacy risks, but the lack of oversight, testing, and monitoring can lead to unintended consequences such as downstream error propagation and ethical dilemmas around social and economic inequality. Bias introduced during model training can lead to discrimination and unfair practices.

A lack of transparency for how AI systems are built and monitored can lead to distrust and adoption resistance. AI can be co-opted to spread disinformation and manipulate for competitive and economic gain.

And the liabilities of regulatory non-compliance are forcing organizations to keep pace with new regulations as technology advances. The world’s most comprehensive AI regulation to date was just passed by a sizable vote margin in the European Union (EU) Parliament, while the United States federal government and state agencies have recently taken several notable steps to place controls on the use of AI.

The extensive Executive Order on the Safe, Secure and Trustworthy Development and Use of AI provides protections from discrimination, consumer safety, and antitrust. One of the primary efforts under the executive order is for the National Institute of Standards and Technology to expand its Artificial Intelligence Risk Management Framework to apply to generative AI. The recently formed U.S. AI Safety Institute within NIST will support the effort with research and expertise from participating members, including Databricks.

Best practices for AI

Implementing secure AI frameworks will be extremely helpful in securing AI systems going forward, as they promise to evolve with technology and regulation. The Databricks AI Security Framework (DASF) takes the NIST framework several steps further by helping understand:

• Stakeholder responsibilities throughout the AI system lifecycle
• How different deployment models and AI use cases impact security
• The 12 main AI system components and associated risks mitigation controls
• Relevant risks to your use cases and models, and their impacts
• How to implement controls prioritized by model types and use cases

The DASF recommends the following seven steps to manage AI risks:

• Have a mental model of an AI system and the components that need to work together.
• Understand the people and processes involved in building and managing AI systems and define their roles.
• Understand what responsible AI entails and all the likely AI risks, and catalog those risks across the AI components.
• Understand the various AI deployment models and risk implications for each.
• Understand the unique threats to your AI use cases and map your risks to those AI threats.
• Understand the unique risks that apply to your AI use case and filter for those risks based on your use cases.
• Identify and implement controls that need to be applied per your use case and deployment model, mapping each risk to AI components and controls.

Benefits of Leveraging AI in Cybersecurity   

Employing AI technology in your overall SecOps can help you scale your security and risk management operations to accommodate the increased data volumes and increasingly complex AI solutions. You may also enjoy cost and resource utilization benefits based on the reduction of routine manual tasks and auditing, and compliance-related costs.

Operational efficiency is enhanced with AI-based behavioral analysis and anomaly recognition to improve response times and accuracy of threat detection and mitigation.

By using AI to automate security management processes, you can quickly gain visibility into your attack surface. AI models can be trained for continuous monitoring, IP address tracking and investigation, identifying and prioritizing vulnerabilities based on their impact for proactive mitigation.

AI can perform inventory analysis, tagging, and tracking for compliance management, and automate patching and upgrades. This helps reduce human error and streamlines risk assessment and compliance reporting.

Automation and AI can also provide real-time responses to cyberattacks and reduce false alarms, while continuously learning the changing threat landscape.

The Future of AI Security   

Emerging trends in AI security promise a move away from reactive measures to proactive fortification. These changes include:    

• Machine learning algorithms are used for predictive analytics, identifying patterns and the likelihood of future threats and vulnerabilities based on historical data.
• AI-driven threat detection using behavioral analytics to identify suspicious anomalies and attack patterns.
• AI-automated security orchestration and response (SOAR) to quickly analyze vast amounts of data to generate incident tickets, assign response teams, and implement mitigation measures.
• AI-powered penetration testing, or “ethical hacking,” to speed up the analysis of potential threats.
• The integration of AI into zero-trust frameworks for continuous authentication and authorization.
• Decision-making for self-healing systems that use AI-driven logic to find the best solutions.

There are also several innovations using generative AI for security management, such as creating “adversarial AI” to fight AI-driven attacks and GenAI models to reduce false positives. There is also work being done in post-quantum cryptography to counter the looming threat of quantum computers.

Preparing for future security challenges will involve the continuous evolution of security platforms with AI, and professionals in the security operations center (SOC) will need to learn new techniques and upskill with AI. Combined with AI-driven risk assessment technologies, blockchain will help ensure immutable risk records and provide transparent and verifiable audit trails.

Conclusion: Ensuring safe and ethical AI implementation   

The rapid momentum behind the use of AI has organizations realizing the need to democratize the technology and build trust in its applications. Achieving that will require effective guardrails, stakeholder accountability, and new levels of security. Important collaborative efforts are ongoing to pave the way. The Cybersecurity and Infrastructure Security Agency (CISA) developed the Joint Cyber Defense Collaborative (JCDC) Artificial Intelligence (AI) Cybersecurity Collaboration Playbook with federal, international, and private-sector partners, including Databricks.

Advancing the security of AI systems will necessitate investment in training and tools. The Databricks AI Security Framework (DASF) can help create an end-to-end risk profile for your AI deployments, demystify the technology for your teams throughout the organization, and provide actionable recommendations on controls that apply to any data and AI platform.

Using AI responsibly involves cultural and behavioral education, and leadership that emphasizes ownership and continued learning. You can find events, webinars, blogs, podcasts, and more on the evolving role of AI security at Databricks Security Events. And check out Databricks Learning for instructor-led and self-paced training courses.