Category: blogs

The EU AI Act, published in the Official Journal of the European Union on July 12, 2024, marks a significant regulatory milestone for artificial intelligence (AI) within the European Union. It has significant implications for enterprises, especially those involved in the development, deployment, or use of AI systems not just within the EU but also outside of it. The primary aim of the Act is to ensure that AI systems are safe and transparent, and respect fundamental rights, while it also introduces significant challenges that marks a new era of compliance and accountability for enterprises. 

As enterprises strive to meet the EU AI Act’s requirements, AI model security emerges as a critical component. Adversarial attacks pose a significant threat to AI systems, potentially compromising data integrity, decision accuracy, and overall performance.

Understanding the EU AI act: Implications for enterprises

The EU AI Act is effective on January 1, 2025. It’s a comprehensive regulatory framework designed to ensure the safe and ethical deployment of AI technologies across Europe. The framework categorizes AI applications based on their risk levels, with strict regulations imposed on high-risk AI systems.

Key points of the EU AI Act:

For organizations this means:

1- Increased scrutiny for high-risk AI systems:

• AI systems used in critical areas—such as healthcare, finance, and infrastructure—are classified as high risk. These systems must meet rigorous standards for transparency, documentation, and risk management.
• Non-compliance with these requirements can result in significant penalties, legal repercussions, and damage to reputation.

2- Enhanced documentation and transparency:

• High-risk AI systems must provide detailed information about their functioning and limitations. This includes rigorous documentation on how the AI models were developed and how they handle adversarial threats.
• Failure to document and disclose these aspects can lead to compliance issues and legal challenges.

3- Mandatory conformity assessments:

• Before deployment, high-risk AI systems must undergo thorough conformity assessments to ensure they meet all regulatory requirements.
• This process also involves demonstrating the robustness of the AI models against adversarial attacks, which requires advanced security measures and testing.

The compliance challenge: Why companies must act now

For enterprises, the implications of non-compliance with the EU AI Act are significant. Failing to meet the Act’s requirements can result in:

• Heavy fines and penalties: Non-compliance can lead to substantial financial penalties, which can impact an organization’s bottom line.
• Operational disruptions: Legal disputes and regulatory scrutiny can disrupt business operations and hinder AI deployments.
• Reputation damage: Failing to adhere to the Act’s standards can damage an organization’s reputation and erode trust with clients and stakeholders.

Despite the clear guidelines, many enterprises might struggle to comply with the EU AI Act due to the complexity of AI systems and the evolving nature of adversarial attacks. Common challenges include:

• Identifying vulnerabilities: Detecting and addressing vulnerabilities in AI models can be daunting without specialized tools and expertise.
• Implementing robust security measures: Developing and maintaining robust security measures to protect AI systems from adversarial attacks is a continuous and resource-intensive process.

Why our AI model security product is essential

To ensure compliance with the EU AI Act and safeguard your AI systems, Styrk’s products offer critical advantages:

1- Adversarial attack detection:

• Our product employs cutting-edge techniques to identify and propose mitigation mechanisms for adversarial attacks on AI models. This proactive approach helps ensure that your AI systems remain robust and compliant with regulatory standards.

2- Comprehensive documentation and reporting:

• We provide detailed documentation and reporting features that align with the EU AI Act’s transparency requirements. This includes thorough records of your AI model’s security measures and performance.

3- Seamless conformity assessment support:

• Our solution streamlines the conformity assessment process, helping you demonstrate compliance with the Act’s rigorous standards. This includes automated testing and reporting that simplify the assessment process.

4- Expert support and guidance:

• Our team of experts provides ongoing support and guidance to ensure that your AI models adhere to the latest regulatory requirements and best practices in AI security.

The EU AI Act represents a significant shift in the regulatory landscape for AI, imposing strict requirements on high-risk systems and emphasizing transparency and security. For enterprises, this means a pressing need to ensure compliance and robustness in AI deployments. By choosing Styrk, you not only safeguard your AI models against adversarial attacks but also position your organization to meet the EU AI Act’s requirements effectively.

Don’t wait for compliance challenges to arise—act now to secure your AI systems and ensure a smooth transition into the new regulatory environment. Contact us today to learn how our AI model security solutions can help you navigate the EU AI Act with confidence.

In the rapidly evolving landscape of artificial intelligence, the concepts of explainability and bias are at the forefront of discussions about security and trust. As AI systems and large language models (LLMs) are increasingly integrated into various sectors, from healthcare to finance, ensuring these systems are both understandable and unbiased is crucial. But why are explainability and bias themselves considered security risks, and what can be done to mitigate these risks?

The Importance of Explainability in AI

Explainability refers to an AI model’s ability to understand and interpret the decisions made by its systems. For users and stakeholders to trust AI, they need to know how decisions are reached. In critical applications such as medical diagnosis or loan approvals, the inability to explain AI decisions can lead to mistrust and even harmful outcomes.

Example: Healthcare

Imagine an AI system used to diagnose diseases. If the system identifies a condition but cannot explain how it arrived at that conclusion, doctors may find it difficult to trust the diagnosis. Worse, if the AI is wrong, patients might receive inappropriate treatments, leading to severe consequences. Transparent AI models that provide insights into their decision-making process can help medical professionals make better-informed decisions, thus enhancing trust and safety.

The Challenge of Bias in AI

Bias in AI occurs when a model produces prejudiced outcomes due to flawed data or algorithms. Bias can manifest in various forms, such as racial, gender, or socioeconomic biases, and can significantly impact the fairness and equity of AI applications.

Example: Hiring Practices

Consider an AI system used for hiring employees. If the training data predominantly includes resumes from a specific demographic, the AI might learn to favor candidates from that group, perpetuating existing inequalities. Such bias not only undermines the fairness of the hiring process but also exposes companies to legal risks and reputational damage.

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Explainability and Bias as Security Risks

Both explainability and bias directly impact the security and trustworthiness of AI systems. Unexplainable AI decisions can be manipulated or misinterpreted, leading to security vulnerabilities. For instance, if an AI system’s behavior cannot be understood, malicious actors might exploit this opacity to manipulate outcomes without detection.

Bias, on the other hand, can erode the foundational trust in AI systems. Biased outcomes can lead to discriminatory practices, resulting in social and ethical issues that compromise the security and integrity of AI applications.

Mitigating Risks with Explainability and Bias Management

To address these challenges, it is essential to implement robust mechanisms that enhance the explainability of AI models and actively manage and mitigate bias.

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Introducing Styrk’s Trust Solution

At Styrk AI, we recognize the critical importance of explainability and bias management in AI systems. Styrk’s Trust is designed to measure, monitor, and mitigate bias in AI models and LLMs. With comprehensive and configurable scans, our solution assesses the results using industry-standard metrics, ensuring that your AI systems remain fair, transparent, and trustworthy.

By leveraging Styrk’s Solution, organizations can enhance the security, trustworthiness, and ethical standing of their AI applications, ultimately driving better outcomes and fostering greater trust among users and stakeholders.

 Managing risk proactively

Explainability and bias in AI are not just technical challenges; they are fundamental security risks that require proactive management. By adopting comprehensive solutions, organizations can address these risks head-on, ensuring that their AI systems are both fair and transparent, thereby safeguarding their integrity and trustworthiness in an increasingly AI-driven world.

Artificial intelligence (AI) is rapidly transforming our world, from facial recognition software authenticating your phone to spam filters safeguarding your inbox. But what if these powerful tools could be tricked? Adversarial attacks are a growing concern in AI security, where attackers manipulate data to cause AI systems to make critical mistakes. Gartner predicts that 30% of cyberattacks will target vulnerabilities in AI, either through manipulating training data, stealing the AI model entirely, or tricking it with deceptive inputs, highlighting the urgency of addressing these vulnerabilities.

Traditional AI models can be surprisingly susceptible to these attacks. Imagine a self-driving car mistaking a stop sign for a yield sign due to a cleverly placed sticker. A 2018 study by researchers, found that adding just a few strategically placed stickers on traffic signs could trick a deep learning model into misclassifying the sign with a staggering 84% success rate*. The consequences of such an attack could be catastrophic. But how exactly do these attacks work?

Adversarial attacks come in many forms, all aiming to manipulate an AI model’s decision-making processes. Here are some common techniques that attackers use to exploit models:

The susceptibility of AI models to adversarial attacks varies depending on their architecture. Even models with millions of parameters can be fooled with cleverly crafted attacks.

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Mitigating attacks with Styrk

Enterprise usage of AI is increasingly threatened by adversarial attacks, where AI models are deceived using manipulated data. To address this, Styrk offers its AI security product, Armor,  which assesses and enhances the robustness of AI models. Armor scans labeled data and performs pre-selected adversarial attacks on it. After executing these attacks, the system identifies any vulnerabilities and reports them to the customer in a comprehensive report format. 

In addition to identifying adversarial attacks, Styrk’s Armor also proposes defense mechanisms against adversarial attacks. As attacks continue to increase and evolve constantly, Armor keeps adding new attacks and defenses to its systems, keeping ahead of the curve in developing robust solutions that customers can use to keep their AI models safe and performant. At Styrk, we provide solutions that can help identify such attacks and propose mitigation mechanisms to ensure that AI technology helps, not hinders, enterprises. 

Contact us to understand how Armor can help safeguard your AI model from adversarial attacks. 

*https://openaccess.thecvf.com/content_cvpr_2018/papers/Eykholt_Robust_Physical-World_Attacks_CVPR_2018_paper.pdf

Between 2022 and now, the generative AI market value has increased from $29 billion to $50 billion–representing an increase of 54.7% over two years. The market valuation is expected to rise to $66.62 billion by the end of 2024* and  suggests  a surge in companies seeking to integrate generative AI into their operations, often through tools like ChatGPT, Llama, and Gemini, to enhance and automate customer interactions.

While AI technology promises significant benefits for businesses, the growing adoption of generative AI tools comes with the risk of exposing users’ sensitive data to LLM models. Ensuring the privacy and security of users’ sensitive data remains a top priority for enterprises, especially in light of stringent regulatory requirements like the EU AI Act to protect personal and financial data of its users.

To keep enterprise data secure while using the generative AI tools, Styrk offers multiple privacy-preserving mechanisms and a security wrapper that enables businesses to harness the power of generative AI models. This safeguards sensitive information and maintains compliance with data protection regulations.

Styrk’s core capabilities for LLM security

Not only can Styrk be used to protect sensitive data but it can also help safeguard AI models from prompt injection attacks or filtering out gibberish text. Some of Styrk’s  key capabilities include:   

The AI industry is rapidly growing and is already helping companies deliver more personalized and efficient customer experiences. Yet as businesses adopt generative AI into their operations, they must prioritize protecting their enterprise data, including sensitive customer data. Not only does Styrk enhance customer engagement, it enables regulatory compliance in a fast-moving landscape. Styrk prepares businesses to anticipate changes in AI and adjust their strategies and models accordingly. Contact us today to learn more on how Portal can help your business. 

*Generative artificial intelligence (AI) market size worldwide from 2020 to 2030