How To Manage Generative AI (GenAI) Security Risks!

How To Manage Generative AI (GenAI) Security Risks!

Maintaining regulatory compliance has become an increasingly difficult problem for organizations worldwide in today’s dynamic cybersecurity and data privacy environment. Companies constantly develop innovative solutions to guarantee ongoing compliance without sacrificing operational effectiveness. This is primarily due to the growing threat of malicious entities and the ever-expanding digital ecosystem. This is where generative AI (GenAI) shines with its disruptive power.

In recent years, leveraging AI, particularly generative AI, for continuous compliance has gained a lot of ground. Generative AI systems, like OpenAI’s GPT-3.5 and recent versions, can comprehend, evaluate, and produce writing that resembles a person based on their input. The capacity to automate and streamline numerous compliance-related processes, including policy documentation, risk assessment, and even staff training, is incredibly promising.

In this blog, we will give you a brief overview of generative AI, discuss its benefits and risks, acquaint you with several use cases of its application, and provide you with an understanding of what compliance standards you can consider to safely and responsibly implement generative AI for continuous compliance.

What is Generative AI?

Generative AI is a subset of artificial intelligence that trains algorithms to produce new content that looks like humans produce it. Unlike traditional AI, created to discover patterns and adjust accordingly, GenAI develops new output using the data it taught. 

Large volumes of data are fed into a machine-learning model to help it recognize patterns and relationships. This trained model can produce original text, graphics, music, and more by extrapolating from the taught data. For instance, OpenAI’s GPT-3 is a generative AI model that can understand text inputs and produce coherent and contextually relevant text outputs, making it an effective tool for writing, discussion, translation, and other tasks.

History of Generative AI

The field of generative AI has been actively researched since the 1960s. However, it only became popular after Open AI launched ChatGPT, which put the technology in everyone’s hands. 

Joseph Weizenbaum launched the first AI chatbot, and it was named ELIZA. This was one of the early stages of Natural Language Processing (NLP), which sought to mimic human-like conversation by generating the responses from text inputs. The system was primitive and attempted to simulate a human conversation, but it opened up many opportunities for NLP.

Now, let’s check out which common compliance standards you can implement generative AI for in your company.

Compliance Standards That Help Implement Generative AI In Your Company

Organizations must consider the pertinent compliance criteria for generative AI to be used safely and responsibly. The following are three crucial compliance requirements to remember:

  1. General Data Protection Regulation (GDPR)

The GDPR is a European Union regulation that safeguards people’s data rights and privacy. Regardless of where they are physically located, it applies to all organizations that handle the personal data of EU people. Obtained user consent, data security, and transparency in data processing are all required for GDPR compliance.

  1. SOC 2

The American Institute of Certified Public Accountants (AICPA) created the SOC II set of auditing standards to emphasize security, accessibility, processing integrity, confidentiality, and consumer data privacy in compliance. This requires continuous monitoring and reporting on these aspects for SOC 2 compliance, especially Type 2 reporting, demonstrating a dedication to data protection.

  1. California Consumer Protection Act (CCPA)

CCPA is a state statute from California that gives users more control over their data. Organizations must abide by CCPA rules if they collect or sell personal data concerning California residents. Transparent data collection procedures, a consumer opt-out choice for data sharing, and improved privacy disclosures are all part of CCPA compliance.

Using generative AI for continuous compliance has several benefits. Let’s check out what these are in the next section.

Benefits of Generative AI

The implications of GenAI in improving compliance are huge. Here is how you can implement generative AI to continuously maintain data privacy in your organization: 

  1. Enhanced Data Privacy: While working with customers and other businesses, your employees are bound to come across sensitive and confidential information. 

With generative AI, you can produce synthetic data that closely resembles the original data without running the risk of disclosing confidential information. Compliance experts can then audit the synthetic data with the same statistics and facts as the original without worrying about handling private and pertinent company information. 

  1. Time and Cost Effectiveness: To ensure their organizations are compliant, compliance experts spend much time and energy gathering and analyzing data. However, this procedure can be costly and time-consuming. 

By producing synthetic data that can be utilized for testing and validation, generative AI can assist in addressing some of these difficulties. This can lessen the need for correct data, reducing expenses and raising overall effectiveness.

  1. Improved Detection of Fraud: Compliance experts have the challenging and labor-intensive duty of spotting fraudulent activities. 

By producing massive volumes of synthetic data that may be used to train machine learning models to recognize patterns and anomalies that may signal fraudulent behavior, generative AI might lessen this burden. This increases the precision with which compliance personnels can identify fraudulent conduct, potentially lowering the risk of financial losses and reputational harm to the company.

  1. Greater Data Accuracy: Data analysis is essential for identifying possible hazards and non-compliance hotspots. 

Compliance experts can generate a lot of data with generative AI, which can then be used to train machine learning models to find patterns and anomalies that point to non-compliance. This may result in risk assessments that are more precise and useful, ultimately assisting organizations in adhering to legal and regulatory requirements responsibly. 

Despite the huge benefits of generative AI in continuous compliance, your path is flexible with risks. The following two sections will discuss the risks of using generative AI for continuous compliance and how to approach risk management.

Risks of Generative AI (Gen AI) 

When employing generative AI in compliance, experts must consider both the advantages and disadvantages. Here are some risks of implementing generative AI for continuous compliance: 

  1. Accuracy Relies on Data Quality

The quality of the data that generative AI uses to produce content is crucial. If the data is accurate or of low quality, the generated material might be accurate, posing a danger to compliance. To guarantee that the generated content satisfies the necessary accuracy and quality criteria, compliance experts must take care when choosing and validating the data used to train generative AI models.

  1. No Human Judgment

Compliance specialists are essential in locating and reducing potential compliance issues. While generative AI can help improve accuracy and streamline your workflow, human expertise and judgment should remain the same. Compliance professionals need to be aware that heavy dependence on generative AI may miss crucial details that call for human judgment. 

  1. Non-Adherence to Regulatory Compliance Laws

It is possible that generative AI systems were only sometimes created with compliance in mind, which could result in non-adherence and penalties if utilized improperly. Therefore, while adopting generative AI, compliance specialists need to be cautious and ensure it conforms with all applicable compliance rules and regulations. Guidelines under compliance standards like GDPR and SOC 2 are particularly important.

  1. Lack of Transparency

Understanding how generative AI models arrive at particular outputs might be difficult because of their complexity. It may be difficult to recognize possible compliance concerns and ensure compliance with rules due to this lack of transparency. Before adopting an AI model, compliance specialists must carefully examine its technique to ensure it complies with legal standards.

Best Practices for Ensuring Security When Using Generative AI for Continuous Compliance

Without further ado, let’s check out what best practices can help you safely implement the use of generative AI for continuous compliance in your organization.

  1. Create and Enforce an AI Use Policy in Your Organization

You should create an acceptable use AI policy for your company, regardless of whether you’re already implementing generative AI throughout the whole organization or are just thinking about the advantages. This guideline ought to clarify the following:

  • Which roles and departments are permitted to employ generative models in their work?
  • Which steps in the process can be automated or enhanced using generative AI?
  • Which internal apps and data are permitted to be accessible to these models, and how?

By establishing these guidelines, your leadership will have a clearer idea of the behaviors that need to be monitored and corrected, and your staff will have a better knowledge of what they can and cannot do using generative AI.

  1. Classify and Encrypt Data Before Integrating Generative AI

Before giving their data to chatbots or using it to train generative AI models, businesses should classify their data. You should choose the appropriate data for specific use cases and keep your other information away from AI systems.

Similarly, to prevent disclosing sensitive information, anonymize sensitive data in training data sets. You have to protect the most sensitive information within the company with strong security policies and controls. Encrypt data sets for AI models and any links to them.

  1. Try to Use First-Party Data, Otherwise Source Third-Party Data Responsibly

Knowing where your input data comes from is crucial when using generative models in business settings. You must ensure the inclusion of first-party data that your organization owns whenever possible. This will let you monitor the source of your data and decide whether it is suitable for inclusion in a generative model. 

If you need to use data your firm does not own, be sure you are getting authorization to access reliable third-party sources. Doing this will make you more likely to use high-quality data and avoid getting sued for utilizing data without authorization or verified for accuracy.

You should investigate how generative AI suppliers are sourcing their training data and the data your company decides to add to already-existing models. Businesses that refuse to explain these procedures in their documents should worry your organization and be shunned. If the vendor obtained data illegally and your organization utilizes or profits from that data without your knowledge, you could still be held accountable for any outputs that breach copyright laws or privacy rights.

  1. Train Employees on Relevant Generative AI Data Model Usage

The most important preventative step against the danger of cyberattacks linked to generative AI is employee training. Employers must inform staff members about the dangers of generative AI use if they want to use it responsibly.

Organizations can establish rules for using generative AI in the workplace by creating a security and acceptable use policy. Organization to organization will differ in the specifics, but generally speaking, it is best practice to demand human monitoring. AI-generated content shouldn’t be blindly trusted; human editors and reviewers should always be involved.

AI use and security policies should also clearly state what information is and should not be included in chatbot inquiries. For instance, developers should never supply AI algorithms with PII, PHI, copyrighted information, or intellectual property.

  1. Invest in Appropriate Cybersecurity Tools That Address AI Risks

Strong cybersecurity safeguards are necessary for generative AI models and artificial intelligence technologies to secure the data they contain. Many generative AI models lack inherent cybersecurity infrastructure, regrettably, or the configuration of these features is so complicated that most users fail to activate them.

You should set up your network security tools appropriately and consider any generative AI models you deploy as part of your network’s attack surface to safeguard input and output data from cybersecurity threats. We advise investing in cybersecurity technologies like the ones listed below, which are made with AI and other contemporary attack surfaces in mind if you’re not already using them to safeguard your company network:

  • Data encryption and data security tools
  • Identity and access management tools
  • Vulnerability assessment and penetration testing
  • Cloud security posture management (CSPM)
  • Threat intelligence and data loss prevention (DLP)
  • Extended detection and response (XDR)
  1. Regularly Audit Your External Vendors To Maintain Complete Adherence To Compliance Requirements

Organizations can anticipate more compliance obligations pertaining to generative AI technology due to the increasing usage of corporate AI. Compliance regulations are a dynamic field.

Businesses should keep a careful eye on any changes to compliance requirements pertaining to the usage of AI systems that may affect their business. Review the vendor’s security controls and vulnerability assessments regularly when employing AI products purchased from a third-party vendor as part of this procedure. This makes it less likely that any security flaws in the vendor’s systems will find their way into the company’s IT infrastructure.

Security and Compliance with Akitra!

Establishing trust is a crucial competitive differentiator when courting new SaaS businesses in today’s era of data breaches and compromised privacy. Customers and partners want assurances that their organizations are doing everything possible to prevent disclosing sensitive data and putting them at risk, and compliance certification fills that need.

Akitra offers an industry-leading, AI-powered Compliance Automation platform for SaaS companies. Using automated evidence collection and continuous monitoring, together with a full suite of customizable policies and controls as a compliance foundation, our compliance automation platform and services help our customers become compliance-ready and certified for security and compliance frameworks like SOC 1, SOC 2, HIPAA, GDPR, PCI DSS, ISO 27001, ISO 27701, ISO 27017, ISO 27018, NIST CSF, NIST 800-53, NIST 800-171, FedRAMP, CCPA, CMMC, and more such as CIS AWS Foundations Benchmark, etc. In addition, companies can use Akitra’s Risk Management product for overall risk management for your company, Trust Center, and AI-based Automated Questionnaire Response product to streamline and expedite security questionnaire response processes, delivering huge cost savings. Our compliance and security experts will provide customized guidance to navigate the end-to-end compliance process confidently. 

The benefits of our solution include enormous savings in time, human resources, and cost savings, including discounted audit fees with our audit firm partners. Customers achieve compliance certification fast and cost-effectively, stay continuously compliant as they grow, and can become certified under additional frameworks using a single compliance automation platform.

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