Category: use-cases

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.