Dark Side of AI model: How to Make AI Trustworthy

As artificial intelligence (AI) becomes more embedded in business operations, the imperative to safeguard these technologies against security threats has never been more critical. AI’s transformative potential is undeniable, but the risks associated with its deployment require rigorous security measures to protect investments and ensure trustworthiness.

The Urgency of Securing AI Investments

The rise of AI has been accompanied by significant concerns about security and privacy. Both benign and malicious actors pose risks that can compromise the performance, fairness, and integrity of AI models and the data they process. This issue is particularly pressing as AI technologies become more prevalent. According to the 2020 Gartner Hype Cycle for Emerging Technologies, AI accounted for over a third of the technologies discussed, underscoring its importance and widespread adoption.

Despite the growing adoption of AI, there is a notable lack of standardized and comprehensive tools designed to protect these systems. Consequently, businesses are often left to navigate these challenges on their own. Moreover, a Gartner survey reveals that customers expect firms to be held accountable when AI systems malfunction or are misused.

To preserve the value of AI investments, it is crucial for organizations to implement robust security measures to counteract potential attacks. The threats to AI extend beyond mere security risks, impacting model performance and the outcomes they produce.

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Understanding the Risks of AI

AI deployment introduces three primary categories of risks:

1. Security Risks: As AI systems become integral to critical functions, vulnerabilities are increasingly exposed. For instance, a flaw in the AI system of a self-driving car could lead to severe accidents, highlighting the need for stringent security protocols.
2. Liability Risks: AI systems that use sensitive customer data can make decisions that negatively affect individuals. For example, erroneous AI credit scoring can deny loans, resulting in financial loss and reputational damage.
3. Social Risks: “Irresponsible AI” can produce biased or opaque outcomes, leading to unjust consequences for customers. Even minor biases in AI algorithms can cause significant issues, emphasizing the need for transparency and fairness.

Common Attack Vectors in AI

Criminals target AI systems through two primary methods: malicious inputs and query attacks.

1. Malicious Inputs: These attacks involve manipulating AI systems using harmful data. Adversarial AI, for example, can exploit AI-generated voices for fraudulent purposes, such as impersonating a CEO to execute unauthorized financial transactions. In March of last year, a criminal group used an AI-generated voice to falsely instruct a bank to transfer $243,000.
2. Query Attacks: These attacks involve probing an AI system to understand its operation. They can be classified into black box and white box queries:
   • Black Box Queries: Attackers submit queries to identify and exploit vulnerabilities in the AI system. For example, researchers have deceived translation models by inputting perturbed data, resulting in incorrect translations.
   • White Box Queries: These attacks involve reconstructing a model by accessing its training data, potentially leading to the theft of intellectual property. An example includes a speech recognition vendor being outperformed by a competitor who copied and sold their technology.

Building Trustworthy AI: New Security Pillars

To address AI-related risks effectively, organizations must enhance both existing and new security pillars. These include:

1. Human-Focused and Enterprise Security Controls: Implement adversarial training for staff and adopt enterprise security policies to reduce attack surfaces. Leveraging blockchain technology for tracking the provenance of AI models and training data can enhance transparency and accountability.
2. AI Model Integrity: Focus on maintaining the integrity of AI models through anomaly detection and robust data protection practices. Techniques such as differential privacy and synthetic data can safeguard against data breaches and misuse.
3. AI Data Integrity: Ensure data repositories are current, high-quality, and inclusive of adversarial samples to protect against data poisoning. Utilize a range of open-source and commercial solutions to bolster resilience against data poisoning, adversarial inputs, and model leakage.

Proactive Measures for Safeguarding AI

Security professionals should undertake the following steps to protect AI applications:

1. Conduct Threat Assessments: Apply stringent access controls and monitor training data, models, and data processing components to minimize attack surfaces during development and production.
2. Enhance SDLC Security: Address AI-specific aspects in the software development lifecycle (SDLC), including model construction threats, detection of vulnerabilities, reliance on third-party models, and exposure of data pipelines.
3. Protect Data Repositories: Maintain up-to-date, high-quality data repositories and incorporate adversarial samples to prevent data poisoning. Employing advanced solutions can improve resilience against various threats.

Detecting AI model compromise can be challenging unless the perpetrators are caught and their systems are forensically examined. As AI technologies continue to evolve, securing them from the outset is crucial. Retrofitting security measures is often more costly and less effective than integrating them from the beginning. Therefore, investing in robust AI security measures today is essential to ensure reliable and trustworthy AI systems in the future.

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