Dark Side of AI model: How to Make AI Trustworthy

To secure AI model investments, it is in every organization’s best interest to establish security measures that counter attacks.

Artificial intelligence adoption is hampered by security and privacy concerns, and with good reason. The performance, fairness, security, and privacy of AI models and data can be jeopardized by both benign and hostile actors.

This isn’t something businesses can afford to overlook as AI grows more ubiquitous and offers a slew of advantages. In fact, AI accounted for more than a third of the technologies mentioned in the most recent Gartner Hype Cycle for Emerging Technologies, 2020.

At the same time, AI has a dark side that is often overlooked, especially as the present machine learning and AI platform market lacks standardized and comprehensive tooling to protect businesses. This implies that businesses are on their own. Worse, according to a Gartner survey, customers believe that when AI goes wrong, the firm that uses or provides technology should be held accountable.

To preserve AI investments, it is in everyone’s best interest to develop security measures to counter attacks. Threats and attacks against AI impact not only the security of AI models and data but also the performance and outcomes of the models.

There are two basic ways that criminals attack AI, as well as activities that technical professionals can take to neutralize these dangers, but first, let’s look at the three main risks that AI faces.

Security, liability, and social risks of AI

Organizations that deploy AI face three categories of dangers. As AI grows increasingly popular and integrated into vital business functions, security vulnerabilities are increasing. For example, there could be a glitch in the AI model of a self-driving car that causes a tragic accident.

As AI models employing sensitive customer data are increasingly used to make decisions that harm customers, liability issues are rising. Incorrect AI credit rating, for example, can prevent consumers from obtaining loans, resulting in financial and reputational losses.

As “irresponsible AI” produces negative and unjust repercussions for customers by making biased judgments that are neither transparent nor easily understood, social hazards are rising. Even little biases can lead to major algorithmic misbehavior.

How criminals commonly attack AI

The hazards outlined above can arise from one of two typical methods criminals target AI malicious inputs or perturbations and query attacks.

Adversarial AI, manipulative digital inputs, and malicious physical inputs are all examples of malevolent inputs to AI models. Adversarial AI could take the form of socially engineering humans using an AI-generated voice, which could be used for any type of crime and be regarded as a “new” form of phishing. For example, in March of last year, criminals impersonated a CEO’s speech and demanded a fraudulent transfer of $243,000 to their own accounts using AI synthetic voice.

Query assaults, which can take the form of a black box or a white box, entail criminals submitting queries to an organization’s AI models in order to figure out how they work. A black box query attack, in particular, decides which unusual, perturbed inputs should be used for the desired output, such as financial gain or evading discovery. By altering the output, some scholars have been able to deceive leading translation models, resulting in an incorrect translation.

A white box query attack regenerates a training dataset in order to reproduce a comparable model, which could lead to the theft of sensitive data. For example, a speech recognition vendor was victimized by a new, foreign vendor who counterfeited their technology and then sold it, allowing the foreign vendor to gain market share based on stolen IP.

Newest security pillars to make AI trustworthy

It’s critical for IT leaders recognize AI dangers in their organizations so that they can analyze and strengthen both existing security pillars (human-focused and enterprise security controls) and new security pillars (AI model integrity and AI data integrity).

Organizations should investigate adversarial training for staff and use enterprise security policies to decrease the attack surface. This pillar also includes the use of blockchain for the provenance and tracking of AI models and the data used to train them as a means for businesses to make AI more trustworthy.

To tackle AI dangers, AI data integrity focuses on data anomaly analytics, such as distribution patterns and outliers, as well as data protection, such as differential privacy or synthetic data.

Technical personnel working in security technologies and infrastructure should take the following steps to safeguard AI applications:

Conduct a threat assessment and apply stringent access control and monitoring of training data, models, and data processing components to reduce the attack surface for AI applications during development and production.
Enhance traditional software development life cycle (SDLC) security measures by tackling four AI-specific aspects: threats during model construction, detection of AI model vulnerabilities, reliance on third-party pre-trained models, and exposed data pipelines.
Protect and maintain data repositories that are current, high-quality, and inclusive of adversarial samples across all data pipelines to avoid data poisoning. For boosting resilience against data poisoning, adversarial inputs, and model leakage attacks, a growing variety of open-source and commercial solutions are available.

It’s difficult to verify whether an AI model has been compromised unless the fraudster is apprehended and the organization conducts forensics on the fraudster’s system. At the same time, businesses aren’t going to stop employing AI, therefore protecting it is critical to properly implementing AI in the workplace. Retrofitting security into any system is much more costly than building it in from the outset, so secure your AI today.

Dark Side of AI model: How to Make AI Trustworthy

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