Author: styrk

Navigating the EU AI act: Why enterprises must prioritize AI model security

Posted on by styrk

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:

Ensuring transparency and accountability:

Organizations must ensure that their AI systems are transparent and accountable, particularly those classified as high-risk.

Protecting fundamental rights:

AI systems must not violate fundamental rights, including privacy and data protection.

Mitigating risks:

Enterprises must implement measures to mitigate risks associated with AI systems, including adversarial attacks.

Wide applicability:

The EU AI Act applies not only to companies within the EU but also to those outside the EU if their AI systems are used or their outputs are utilized within the EU. This includes U.S. companies and others with no physical presence in the EU but whose AI technologies are integrated into products or services used by EU-based companies.

Risk-based classification:

AI systems are categorized based on risk levels, ranging from unacceptable risk (prohibited) to high-risk, limited risk, and minimal risk. High-risk systems, such as those used in critical infrastructure or biometric identification, require stringent compliance, including transparency and conformity assessments.

Severe fines:

Non-compliance with the EU AI Act can result in significant fines. For prohibited AI systems, fines can reach up to 7% of worldwide annual turnover or €35 million, whichever is higher. High-risk systems face fines up to 3% of turnover or €15 million.

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.

Explainability and Bias in AI: A Security Risk?

Posted on by styrk

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.

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.

Approaches to Enhance Explainability:

Model Transparency:

Using interpretable models or providing explanations for complex models helps users understand AI decisions.

Post-Hoc Explanations:

Techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) can be used to explain the outputs of black-box models.

Human-AI Collaboration:

Encouraging collaboration between AI systems and human experts ensures that AI decisions are validated and understood.

Strategies to Mitigate Bias:

Diverse Training Data:

Ensuring that the training data is representative of all relevant demographics helps reduce bias.

Bias Detection Tools:

Using tools to regularly check for bias in AI models can help identify and correct prejudiced outcomes.

Continuous Monitoring:

Implementing continuous monitoring systems to track AI decisions and outcomes ensures ongoing fairness and equity.

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.

Historical data reuse: Unleashing the potential of unstructured data while preserving privacy

Posted on by styrk

Businesses and organizations generate vast amounts of unstructured data every day. This data often contains valuable insights that can inform future business decisions, improve efficiency, and drive innovation. However, much of this data remains untapped due to concerns surrounding privacy and data security. Organizations are reluctant to utilize or share historical data because it often contains sensitive or personal information, which, if mishandled, could lead to legal and reputational risks.

This is where Styrk’s Cypher, a solution to identify and mask sensitive data from unstructured data sources (such as PDFs, Word documents, text files, and even images), steps in. Cypher ensures that organizations can safely reuse historical data without compromising privacy or security.

The challenge: Valuable data trapped by privacy concerns

For years, organizations have amassed huge volumes of unstructured data, including legal contracts, customer communications, medical records, financial reports, and more. Often, these documents contain personally identifiable information (PII), financial data, or other sensitive content that is subject to strict data privacy regulations.

Because of these privacy concerns, historical data is often shelved or deleted to avoid compliance issues. Organizations face significant obstacles when it comes to extracting the valuable insights locked away in this data, especially without compromising privacy or inadvertently exposing sensitive information.

Take the example of a healthcare provider organization wanting to conduct a study on past patient outcomes. The organization possesses decades of medical records, filled with valuable data, but it cannot reuse or share them without risking the exposure of patient identities and medical information. Manually anonymizing large datasets is time-consuming, prone to human error, and requires significant expertise in data security.

The solution: Cypher for historical data reuse

Cypher offers a powerful solution to this dilemma by enabling organizations to safely reuse historical unstructured data. By identifying and masking sensitive information automatically, Cypher helps organizations maintain compliance with privacy regulations while leveraging the information contained in their historical data.

Cypher’s advanced algorithms can process and analyze a wide range of unstructured file types—be they text-heavy PDFs, word documents, or scanned image files. By recognizing patterns associated with sensitive data (like names, addresses, Social Security numbers, or credit card information), Cypher can accurately detect and mask such information across large datasets. This process allows organizations to reuse their historical data with full confidence that no sensitive data will be inadvertently disclosed.

Key benefits of Cypher in historical data reuse

Unlocking hidden value:

With Cypher’s masking technology, organizations can safely access historical data that was previously off-limits due to privacy concerns. Whether it’s decades-old contracts, customer feedback, or archived financial data, these documents contain rich information that can be used for trend analysis, decision-making, and forecasting.

Automated detection and masking:

The solution eliminates the need for manual review by leveraging AI to automate the detection of sensitive data. Cypher scans unstructured data at scale, identifying PII and other confidential information that must be masked, drastically reducing the time and effort required to prepare data for reuse.

Preservation of data integrity: 

While Cypher effectively masks sensitive information, it maintains the structure and integrity of the underlying data. This ensures that historical data remains valuable for analysis, research, and reporting purposes, even after sensitive elements have been removed.

Scalability:

Cypher’s ability to process large volumes of data means that organizations can tackle historical data of any size. Whether a company is dealing with hundreds or millions of files, Cypher’s scalable solution can handle the task efficiently.

Real-world example: A financial institution’s data dilemma

Consider a financial institution that has been operational for over 50 years. The company possesses an enormous archive of customer transaction records, loan agreements, and financial reports stored as unstructured data. These documents contain vast amounts of business intelligence that could offer insights into market trends, customer behavior, and operational improvements.

However, many of these files contain sensitive information such as account numbers, personal addresses, and financial details that must be protected. Historically, the institution has been unable to fully leverage this data for fear of violating privacy laws and exposing customers’ personal information. By implementing Cypher, the financial institution can securely process these files. Cypher scans the archive, identifies sensitive data, and applies masking techniques to anonymize it. The institution can then reuse its historical data to conduct deep-dive analysis, predictive modeling, and market research—all without risking compliance violations or customer trust.

Historical data reuse in a privacy-conscious world

As organizations seek to derive more value from their data, the ability to safely reuse historical information is becoming a critical competitive advantage. Privacy makes it possible for companies to unlock the full potential of their unstructured data while ensuring that sensitive information is fully protected.

With Cypher’s automated detection and masking capabilities, businesses across industries—from healthcare and finance to legal and government—can confidently reuse their historical data, gaining new insights and making more informed decisions, all while staying compliant with ever-evolving privacy regulations.

In an era where data is the lifeblood of business strategy, Cypher provides the key to unlocking the value of historical data without sacrificing privacy and security. By ensuring that sensitive information is identified and protected, Cypher empowers organizations to confidently reuse their data for innovation and growth.

Enhancing fairness in AI models: An HR-centered use case on bias identification and mitigation

Posted on by styrk

Rapid advancement of AI in recent years has made it easier for AI to enter numerous domains across organizations including finance, healthcare, law enforcement, and human resources (HR). However, as AI gets integrated into organizational operations, concerns arise about potential biases leading to unfair outcomes. 
Real-world examples of AI bias, such as towards gender or race, emphasize the importance of responsible AI that adheres to AI regulation compliances like Equal Employment Opportunity Commission (EEOC) guidelines, National Institute of Standards and Technology (NIST) AI risk management, and others to ensure fairness and equity.

The challenge: Ensuring AI fairness in HR operations

The challenges faced by HR teams in integrating hiring practices with AI systems underscore the need for AI accountability. Although the potential advantages of quicker and more precise evaluations are clear, HR managers are rightly concerned about ensuring AI fairness and preventing negative impacts in the hiring process. 

To combat biases, organizations must adhere to regulatory compliance standards set by the EEOC, which enforces laws prohibiting employment discrimination based on race, color, religion, sex, national origin, age, or disability. The EEOC AI regulation has also issued guidance on the use of AI and AI algorithmic bias to ensure fair and equitable treatment of all individuals in employment practices. 

In a notable and recent example, Amazon experimented with an AI recruiting tool that was intended to streamline the hiring process by efficiently screening resumes. However, the tool developed a bias against women because it was trained on resumes submitted to Amazon over a decade—a period during which the tech industry was predominantly male. As a result, the AI system downgraded resumes that included the word “women’s” or came from all-women’s colleges*. Despite the neutral nature of the underlying algorithms, the training data’s inherent bias led to discriminatory outcomes. 

This use case underscores the critical issue faced by many HR organizations: How can AI be leveraged to improve efficiency in hiring while maintaining AI fairness and avoiding AI bias? Will it be possible for the AI solution to deliver faster, more accurate evaluations of applicant qualifications than experienced HR specialists while adhering to AI fairness and AI bias standards?

The solution: Bias identification and mitigation using Styrk’s Trust

To ensure AI models do not introduce adverse impacts, it is essential to identify and address AI biases. This is where Styrk’s Trust module comes into play. Trust is designed to assess and mitigate AI bias in customers’ AI models using a robust methodology and a comprehensive set of fairness metrics.

Comprehensive data analysis:

Trust considers a wide range of parameters, including training data, categorical features, protected, and privileged/unprivileged features. This holistic approach ensures that all potential sources of AI bias are considered.

Bias detection:

Using state-of-the-art algorithms, Trust identifies various types of AI bias that may be present in the AI model.

Tailored mitigation strategies:

Trust doesn’t just identify bias in AI models but it also proposes mitigation strategies. Two key approaches it employs are:

  • Disparate impact removal: This technique is used to adjust the dataset or model to minimize bias in AI, ensuring that protected groups are not adversely impacted.
  • Reweighing: The model applies different weights to data points, giving more importance to underrepresented groups to balance the outcomes.
Pre- and post-mitigation analysis:

Trust provides pre- and post-mitigation graphs for key metrics, offering a clear visualization of the model’s performance improvements, before and after bias mitigation.

Fairness metrics evaluation:

Metrics provided by Trust such as balanced accuracy, the Theil index, disparate impact, statistical parity difference, average odds difference, and equal opportunity difference, are used to evaluate and ensure fairness of the AI models. These metrics offer a clear, visual representation of the improvements made in AI fairness and AI bias reduction.

Real-world impact: Benefits of using Trust in HR processes

Applying Trust to AI-supported applicant review system could yield significant benefits:

Faster evaluations:

By ensuring the AI model is free from AI bias, HR managers can confidently use it to speed up the initial screening process, allowing HR specialists to focus on more nuanced aspects of candidate evaluation.

Improved accuracy:

With bias mitigated, the AI model can provide more accurate evaluations of applicant qualifications, potentially surpassing the consistency of human evaluators.

Fairness assurance:

The comprehensive metrics provided by Trust can demonstrate that AI-supported systems meet or exceed fairness standards, ensuring no adverse impact on protected groups.

Continuous improvement:

Regular use of Trust can enable organizations to monitor and improve AI models over time, adapting to changing workforce dynamics and evolving definitions of fairness.

In the quest for efficiency and accuracy, AI models play a crucial role in transforming HR processes. However, ensuring fairness and eliminating bias are paramount to building a diverse and inclusive workforce. Styrk’s Trust helps in AI bias identification and mitigation offering a comprehensive solution, providing organizations with the tools and insights needed to uphold ethical standards in AI-driven decision-making.

For more information on how Styrk can help your organization achieve fair and unbiased AI solutions, contact us today.

*AI recruiting tool that showed bias

Safeguarding X-ray Scanning Systems in Border Security

Posted on by styrk

Rapid advancements in the realm of artificial intelligence (AI) and machine learning (ML) have ushered in unprecedented capabilities, revolutionizing industries from healthcare to transportation and  reshaping approaches to complex challenges like anomaly detection in non-intrusive inspections. Yet with great technological progress comes the real threat of adversarial attacks, which compromise the reliability and effectiveness of these AI models.

Imagine a scenario where an AI-powered system creates synthetic data for computer vision at national borders. It creates an emulated X-ray sensor that can produce synthetic X-ray scan images similar to real X-ray scan images, and virtual 3D replicas of vehicles and narcotics containers. This set of images can be used to train the system to detect anomalies for application of global transport systems. For example, the system can be used in customs and border protection to identify narcotics and other contrabands in conveyances and cargo. However sophisticated this system, it is vulnerable if malicious actors exploit its weaknesses through adversarial attacks.

Understanding adversarial attacks

Adversarial attacks are deliberate manipulations of AI models through subtle modifications to input data. These modifications are often imperceptible to human eyes but can cause AI algorithms to misclassify or fail in their intended tasks. In the context of X-ray scan emulation and model classification, an adversarial attack could potentially introduce deceptive elements into images. For instance, altering a few pixels in an X-ray image might trick the AI into missing or misidentifying illicit substances, thereby compromising security protocols.

The stakes: Why AI model security matters

The implications of compromised AI models in security applications can be profound. Inaccurate or manipulated anomaly detection can lead to serious consequences; in the case of customs and border security, this could mean undetected smuggling of narcotics or other illegal items, posing risks to both public safety and national security. Here, safeguarding AI models from adversarial attacks is not just a matter of technological integrity but also a crucial component of maintaining public order and staying compliant with regulatory standards.

Challenges in securing AI models – and how Styrk offers protection

Vulnerability to perturbations:

AI models are susceptible to small, carefully crafted perturbations in input data that can cause significant changes in output predictions. Styrk can identify vulnerabilities of the AI model and propose mitigation mechanisms to safeguard from such perturbations.

Lack of robustness:

If not carefully monitored, measured, and mitigated, AI models typically lack robustness against adversarial examples, as they are often trained on clean, well-behaved data that does not adequately represent the complexity and variability of real-world scenarios. Styrk can help you identify the kind of adversarial attacks your model might be susceptible to and suggest relevant mitigation mechanisms.

Complexity of attacks:

Adversarial attacks can take various forms such as: evasion attacks; where inputs are manipulated to evade detection, poisoning attacks; where training data is compromised, or any other such attack, necessitating comprehensive defense strategies. Most defenses in the market are designed to protect against specific types of adversarial attacks. When new attack techniques are developed, defenses can become ineffective, leaving models vulnerable to unseen attack methods. In contrast, Styrk’s Armor presents a comprehensive suite that scans the model to identify vulnerabilities in the model. It also offers a single proprietary, patent pending defense for adversarial attacks on traditional AI/ML models that covers a wide range of attacks.

Resource constraints:

Organizations may face limitations in terms of computational resources, time, and expertise required to implement robust defenses against a wide range of adversarial threats in their AI models. Especially in such scenarios, Styrk’s Armor offers an auto-scalable vulnerability scanning tool that can be used to identify potential vulnerabilities in the model and its proprietary defense mechanism proposes the best mitigation strategy that is practical across a wide range of attacks.

Balancing LLM Innovation with Security: Safeguarding Patient Data in the Age of AI

Posted on by styrk

Large language models (LLMs) are revolutionizing healthcare, offering new possibilities for analyzing medical records, generating personalized treatment plans, and driving medical research. However, for healthcare institutions unlocking the potential of LLMs comes with significant challenges: patient privacy, security vulnerabilities, and potential biases within the LLM itself.

Challenges of LLMs in Healthcare

For any organization that deals with patient data, incorporating LLMs into workflows raises challenges – each of which needs tactical solutions:

Patient data privacy:

LLMs require access to patient data to function effectively. However, patient data often includes highly sensitive information such as names, addresses, and diagnoses, and requires protection during LLM interactions.

Security vulnerabilities:

Without effective safeguards in place, malicious actors can exploit vulnerabilities in AI systems. Malicious prompt injection attacks or gibberish text can disrupt the LLM’s operation or even be used to steal data.

Potential biases:

LLMs, like any AI model, can inherit biases from the data they are trained on. Left unmitigated, these biases can lead to unfair or inaccurate outputs, like patient care decisions, in healthcare settings.

Risk of toxic outputs:

Even with unbiased prompts, LLMs can potentially generate outputs containing offensive, discriminatory, or misleading language. A solution is required to identify and warn users about such potentially harmful outputs.

LLM Security: A Guardian for Secure and Responsible AI in Healthcare

To address these challenges, Styrk offers LLM Security, a preprocessing tool that acts as a guardian between healthcare professionals and the LLM. LLM Security provides critical safeguards, especially ensuring the secure and responsible use of LLMs in safely handling patient data.

LLM Security boasts three key features that work in concert to protect patient privacy, enhance security, and mitigate bias:

De-identification for patient privacy:

LLM Security prioritizes patient data privacy. It employs sophisticated de-identification techniques to automatically recognize and de-identify sensitive data from prompts before they reach the LLM. This ensures that patient anonymity is maintained while still allowing the LLM to analyze the core medical information necessary for its tasks.

Security shield against prompt injection attacks & gibberish text:

LLM Security shields against malicious prompt injection attacks. It analyzes all prompts for unusual formatting, nonsensical language, or hidden code that might indicate an attack. When LLM Security detects suspicious activity, it immediately blocks it from processing the potentially harmful prompt, protecting the system from disruption and data breaches.

Combating bias for fairer healthcare decisions:

LLM Security recognizes that even the most advanced AI models can inherit biases from their training data. These biases can lead to unfair or inaccurate outputs in healthcare settings, potentially impacting patient care decisions. LLM Security analyzes the LLM’s output for language associated with known biases. If potential bias is flagged, then warnings prompt healthcare professionals to critically evaluate the LLM’s results and avoid making biased decisions based on the AI’s output. LLM Security empowers healthcare providers to leverage the power of AI for improved patient care while ensuring fairness and ethical decision-making.

Warning for toxic outputs:

Even unbiased prompts can lead to outputs containing offensive, discriminatory, or misleading language. LLM Security analyzes the LLM’s output for signs of potential toxicity. If such a prompt is detected, then healthcare professionals are alerted, encouraging them to carefully evaluate the LLM’s response and avoid using any information that may be damaging or misleading.

The Future of AI in Healthcare: Innovation with Responsibility

By implementing Styrk’s LLM Security, organizations can demonstrate a strong commitment to leveraging the power of LLMs for patient care while prioritizing data security, privacy, and fairness. LLM Security paves the way for a future where AI can revolutionize healthcare without compromising the ethical principles that underpin patient care.

Protecting Traditional AI models from Adversarial Attacks

Posted on by styrk

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:

Adding imperceptible noise:

Imagine adding minuscule changes to an image, invisible to the human eye, that completely alter how an AI classifies it. For instance, adding specific noise to a picture of a cat might trick a facial recognition system into identifying it as a dog.

Crafting adversarial inputs: 

Attackers can create entirely new data points that an AI model has never seen before. These examples are specifically designed to exploit the model’s weaknesses and force it to make a wrong prediction.

Poisoning:

In some cases, attackers might try to manipulate the training data itself. By injecting perturbations into the data used to train an AI model, they can influence the model’s behavior from the ground up.

Extraction:

Attackers can try to steal or replicate the underlying model by querying it extensively and analyzing the responses. This attack tries to reverse-engineer the AI model, effectively “stealing” its intellectual property, leading to intellectual property theft.

Inference:

In some cases, attackers try to extract sensitive information from the model’s output. They try to analyze the model’s response to various inputs; attackers can infer confidential data, such as personal user information or proprietary data used in the training model.

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.

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

Making LLMs Secure and Private

Posted on by styrk

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:   

Compliance monitoring:

Styrk provides a compliance and reporting dashboard that enables organizations to track the flow of sensitive information through AI systems. Data visualization makes it easier to identify data breaches, adhere to regulatory standards, and, ultimately, mitigate risk. 

Blocks prompt injections: 

Styrk’s Portal is equipped with mechanisms to filter prompt injections, safeguarding AI systems from malicious attacks or manipulation attempts. By mitigating the risk of prompt-injection vulnerabilities, Portal enhances the security and resilience of AI-powered interactions, ensuring a safe and trustworthy user experience.

Data privacy and protection: 

Companies across various sectors can use Styrk’s Portal to protect sensitive customer information before it is processed by AI models. For example, Styrk deidentifies personally identifiable information (PII) such as names, addresses, and account details to prevent privacy risks.

Gibberish text detection:

Styrk’s Portal filters out gibberish text, ensuring that only coherent and relevant input is processed by AI models. Detecting gibberish text also helps in preventing any potential jailbreak or prompt injection attacks. This enhances the quality and reliability of AI-generated outputs, leading to more accurate and meaningful interactions.

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