Generative AI (GenAI) is transforming the way enterprises operate at an unprecedented speed, introducing capabilities that are reshaping many workflows - starting from internal applications that make employees more productive to external-facing assistants that improve customer service and are available 24/7 on demand. Large organizations across many different industries are already leveraging GenAI and have established roadmaps for implementation to streamline manual processes, reduce operational costs, and deliver better customer experiences.
With GenAI becoming more and more integrated into critical parts of enterprise infrastructures, it is just as important to ensure that these systems are trusted and secure. To fully unlock the benefits of GenAI, minimizing the potential risk surface should be a top priority in order to protect proprietary company data and ensure the safety for external users.
In this article, we’ll explore how enterprises can leverage GenAI, the risks that are involved, and the most effective ways how AI applications can be safeguarded for secure and reliable use.
Generative AI (GenAI) is transforming the way enterprises operate at an unprecedented speed, introducing capabilities that are reshaping many workflows - starting from internal applications that make employees more productive to external-facing assistants that improve customer service and are available 24/7 on demand. Large organizations across many different industries are already leveraging GenAI and have established roadmaps for implementation to streamline manual processes, reduce operational costs, and deliver better customer experiences.
With GenAI becoming more and more integrated into critical parts of enterprise infrastructures, it is just as important to ensure that these systems are trusted and secure. To fully unlock the benefits of GenAI, minimizing the potential risk surface should be a top priority in order to protect proprietary company data and ensure the safety for external users.
In this article, we’ll explore how enterprises can leverage GenAI, the risks that are involved, and the most effective ways how AI applications can be safeguarded for secure and reliable use.
Generative AI (GenAI) is transforming the way enterprises operate at an unprecedented speed, introducing capabilities that are reshaping many workflows - starting from internal applications that make employees more productive to external-facing assistants that improve customer service and are available 24/7 on demand. Large organizations across many different industries are already leveraging GenAI and have established roadmaps for implementation to streamline manual processes, reduce operational costs, and deliver better customer experiences.
With GenAI becoming more and more integrated into critical parts of enterprise infrastructures, it is just as important to ensure that these systems are trusted and secure. To fully unlock the benefits of GenAI, minimizing the potential risk surface should be a top priority in order to protect proprietary company data and ensure the safety for external users.
In this article, we’ll explore how enterprises can leverage GenAI, the risks that are involved, and the most effective ways how AI applications can be safeguarded for secure and reliable use.
Internal enterprise use-cases for GenAI
Many organizations are starting their GenAI journeys by exploring internal use cases, focusing on improving productivity, decision-making, and collaboration. By allowing the use of AI within controlled environments, enterprises can reap the benefits of increased efficiency for their employees while keeping the security risks to a minimum. Let’s take a look at some of the most common internal enterprise use cases for GenAI:
Knowledge base retrieval and insights:
Enterprises with large amounts of internal documentation, such as wikis, reports, and research papers, can benefit greatly from search and retrieval capabilities powered by GenAI. Internal RAG (retrieval-augmented generation) assistants quickly access large knowledge bases to provide employees with relevant information and reduce the time spent searching for data or waiting for answers. Employees can access personalized and context-relevant insights based on their roles or previous queries. Below you can see three examples of internal RAG chatbots from NVIDIA:
Employee onboarding and development:
Onboarding assistants powered by GenAI are revolutionizing how enterprises ramp up new employees and develop them down the line. GenAI can help in creating personalized learning paths based on individual employee demographics and areas of expertise, providing relevant content and practice exercises. Additionally, companies can use LLM (Large language model) technologies to simulate real-world training scenarios, tailoring the whole onboarding experience to the employee’s role and specific tasks.
Process automation and increased productivity:
One of the most common applications of GenAI is automating repetitive and time-consuming tasks. Whether it's document classification, email filtering, or data extraction from large datasets, GenAI systems can handle tasks that traditionally required human intervention, being significantly more productive. This enables employees to focus on higher-level, strategic work rather than mundane tasks, increasing efficiency and satisfaction of employees.
Code assistants and co-pilots:
AI-powered code assistants and co-pilots are becoming widely adopted by developers within many enterprises and organizations. GenAI assistants, like Microsoft Copilot, help streamline the software development process by suggesting code snippets, automating repetitive coding tasks, and identifying potential syntax errors before they can reach production. By providing real-time suggestions and auto-completing code based on context, these assistants can significantly reduce the time of development, enhance code quality, and free up developers to focus on more complex, creative, or architectural tasks.
Many organizations are starting their GenAI journeys by exploring internal use cases, focusing on improving productivity, decision-making, and collaboration. By allowing the use of AI within controlled environments, enterprises can reap the benefits of increased efficiency for their employees while keeping the security risks to a minimum. Let’s take a look at some of the most common internal enterprise use cases for GenAI:
Knowledge base retrieval and insights:
Enterprises with large amounts of internal documentation, such as wikis, reports, and research papers, can benefit greatly from search and retrieval capabilities powered by GenAI. Internal RAG (retrieval-augmented generation) assistants quickly access large knowledge bases to provide employees with relevant information and reduce the time spent searching for data or waiting for answers. Employees can access personalized and context-relevant insights based on their roles or previous queries. Below you can see three examples of internal RAG chatbots from NVIDIA:
Employee onboarding and development:
Onboarding assistants powered by GenAI are revolutionizing how enterprises ramp up new employees and develop them down the line. GenAI can help in creating personalized learning paths based on individual employee demographics and areas of expertise, providing relevant content and practice exercises. Additionally, companies can use LLM (Large language model) technologies to simulate real-world training scenarios, tailoring the whole onboarding experience to the employee’s role and specific tasks.
Process automation and increased productivity:
One of the most common applications of GenAI is automating repetitive and time-consuming tasks. Whether it's document classification, email filtering, or data extraction from large datasets, GenAI systems can handle tasks that traditionally required human intervention, being significantly more productive. This enables employees to focus on higher-level, strategic work rather than mundane tasks, increasing efficiency and satisfaction of employees.
Code assistants and co-pilots:
AI-powered code assistants and co-pilots are becoming widely adopted by developers within many enterprises and organizations. GenAI assistants, like Microsoft Copilot, help streamline the software development process by suggesting code snippets, automating repetitive coding tasks, and identifying potential syntax errors before they can reach production. By providing real-time suggestions and auto-completing code based on context, these assistants can significantly reduce the time of development, enhance code quality, and free up developers to focus on more complex, creative, or architectural tasks.
Many organizations are starting their GenAI journeys by exploring internal use cases, focusing on improving productivity, decision-making, and collaboration. By allowing the use of AI within controlled environments, enterprises can reap the benefits of increased efficiency for their employees while keeping the security risks to a minimum. Let’s take a look at some of the most common internal enterprise use cases for GenAI:
Knowledge base retrieval and insights:
Enterprises with large amounts of internal documentation, such as wikis, reports, and research papers, can benefit greatly from search and retrieval capabilities powered by GenAI. Internal RAG (retrieval-augmented generation) assistants quickly access large knowledge bases to provide employees with relevant information and reduce the time spent searching for data or waiting for answers. Employees can access personalized and context-relevant insights based on their roles or previous queries. Below you can see three examples of internal RAG chatbots from NVIDIA:
Employee onboarding and development:
Onboarding assistants powered by GenAI are revolutionizing how enterprises ramp up new employees and develop them down the line. GenAI can help in creating personalized learning paths based on individual employee demographics and areas of expertise, providing relevant content and practice exercises. Additionally, companies can use LLM (Large language model) technologies to simulate real-world training scenarios, tailoring the whole onboarding experience to the employee’s role and specific tasks.
Process automation and increased productivity:
One of the most common applications of GenAI is automating repetitive and time-consuming tasks. Whether it's document classification, email filtering, or data extraction from large datasets, GenAI systems can handle tasks that traditionally required human intervention, being significantly more productive. This enables employees to focus on higher-level, strategic work rather than mundane tasks, increasing efficiency and satisfaction of employees.
Code assistants and co-pilots:
AI-powered code assistants and co-pilots are becoming widely adopted by developers within many enterprises and organizations. GenAI assistants, like Microsoft Copilot, help streamline the software development process by suggesting code snippets, automating repetitive coding tasks, and identifying potential syntax errors before they can reach production. By providing real-time suggestions and auto-completing code based on context, these assistants can significantly reduce the time of development, enhance code quality, and free up developers to focus on more complex, creative, or architectural tasks.
Why most enterprises start with internal use-cases
While GenAI continues to prove its value in internal enterprise applications, organizations are often more reluctant when it comes to external-facing applications. Customer-facing applications introduce additional risks, such as exposure to malicious users, sensitive company and customer data leaks, and regulatory compliance concerns.
Even though they are not risk-free, internal environments are more controlled and less exposed to outside threats and risks of data breaches. Enterprises can also establish more robust access controls and safeguard proprietary information more effectively when GenAI is used internally. This is why most organizations currently prioritize the internal adoption of GenAI, in order to gain operational efficiency while keeping the risk manageable.
As shown above, the large majority of enterprises are allowing the use of GenAI application in their environments even though they acknowledge its inherent risks and security concerns, according to Zscaler.
While GenAI continues to prove its value in internal enterprise applications, organizations are often more reluctant when it comes to external-facing applications. Customer-facing applications introduce additional risks, such as exposure to malicious users, sensitive company and customer data leaks, and regulatory compliance concerns.
Even though they are not risk-free, internal environments are more controlled and less exposed to outside threats and risks of data breaches. Enterprises can also establish more robust access controls and safeguard proprietary information more effectively when GenAI is used internally. This is why most organizations currently prioritize the internal adoption of GenAI, in order to gain operational efficiency while keeping the risk manageable.
As shown above, the large majority of enterprises are allowing the use of GenAI application in their environments even though they acknowledge its inherent risks and security concerns, according to Zscaler.
While GenAI continues to prove its value in internal enterprise applications, organizations are often more reluctant when it comes to external-facing applications. Customer-facing applications introduce additional risks, such as exposure to malicious users, sensitive company and customer data leaks, and regulatory compliance concerns.
Even though they are not risk-free, internal environments are more controlled and less exposed to outside threats and risks of data breaches. Enterprises can also establish more robust access controls and safeguard proprietary information more effectively when GenAI is used internally. This is why most organizations currently prioritize the internal adoption of GenAI, in order to gain operational efficiency while keeping the risk manageable.
As shown above, the large majority of enterprises are allowing the use of GenAI application in their environments even though they acknowledge its inherent risks and security concerns, according to Zscaler.
External enterprise use-cases for GenAI
While many organizations begin with internal GenAI use cases, there is great potential for external applications that are able to significantly enhance customer experience, marketing, and sales efforts. However, external-facing use cases often involve more security risks as they interact directly with customers and are accessible to malicious actors. Let’s look at some of the most common customer-facing enterprise use cases for GenAI:
Customer service and support:
GenAI-powered customer support assistants are transforming how enterprises manage customer inquiries. AI chatbots and virtual assistants can handle routine tasks such as answering questions, troubleshooting product issues, and processing customer requests like order tracking or returns. These assistants provide real-time responses to customers, improving service speed and reducing the workload on human agents. Additionally, they are available 24/7 and on-demand, offering a seamless customer experience and increasing satisfaction. For example, Klarna’s AI assistant is doing the equivalent work of 700 human agents and will drive an estimated $40 million profit improvement for the fintech company in 2024.
Product recommendation and personalized e-commerce:
E-commerce companies are leveraging GenAI technologies to enhance the shopping experience by providing dynamic, real-time product recommendations based on customer browsing history, preferences, and past purchases. This way the shopping experience can become more tailored and personalized for each customer, increasing both engagement and sales. AI also helps with predictive inventory management by analyzing purchasing patterns and customer demand, enabling businesses to stock the right products at the right time.
Conversational AI and voice assistants in healthcare:
AI assistants are set to revolutionize healthcare, offering a range of benefits from enhancing patient experience to improving workflow efficiency for medical professionals. These assistants are designed to assist with tasks like scheduling appointments, medication reminders, and even providing symptom checks. By automating administrative tasks, healthcare providers can reduce the workload on their staff, allowing them to focus on higher-value patient care activities. Additionally, voice assistants are used to support patients remotely, offering personalized health advice and answering medical queries in real-time, which can lead to better health outcomes.
While many organizations begin with internal GenAI use cases, there is great potential for external applications that are able to significantly enhance customer experience, marketing, and sales efforts. However, external-facing use cases often involve more security risks as they interact directly with customers and are accessible to malicious actors. Let’s look at some of the most common customer-facing enterprise use cases for GenAI:
Customer service and support:
GenAI-powered customer support assistants are transforming how enterprises manage customer inquiries. AI chatbots and virtual assistants can handle routine tasks such as answering questions, troubleshooting product issues, and processing customer requests like order tracking or returns. These assistants provide real-time responses to customers, improving service speed and reducing the workload on human agents. Additionally, they are available 24/7 and on-demand, offering a seamless customer experience and increasing satisfaction. For example, Klarna’s AI assistant is doing the equivalent work of 700 human agents and will drive an estimated $40 million profit improvement for the fintech company in 2024.
Product recommendation and personalized e-commerce:
E-commerce companies are leveraging GenAI technologies to enhance the shopping experience by providing dynamic, real-time product recommendations based on customer browsing history, preferences, and past purchases. This way the shopping experience can become more tailored and personalized for each customer, increasing both engagement and sales. AI also helps with predictive inventory management by analyzing purchasing patterns and customer demand, enabling businesses to stock the right products at the right time.
Conversational AI and voice assistants in healthcare:
AI assistants are set to revolutionize healthcare, offering a range of benefits from enhancing patient experience to improving workflow efficiency for medical professionals. These assistants are designed to assist with tasks like scheduling appointments, medication reminders, and even providing symptom checks. By automating administrative tasks, healthcare providers can reduce the workload on their staff, allowing them to focus on higher-value patient care activities. Additionally, voice assistants are used to support patients remotely, offering personalized health advice and answering medical queries in real-time, which can lead to better health outcomes.
While many organizations begin with internal GenAI use cases, there is great potential for external applications that are able to significantly enhance customer experience, marketing, and sales efforts. However, external-facing use cases often involve more security risks as they interact directly with customers and are accessible to malicious actors. Let’s look at some of the most common customer-facing enterprise use cases for GenAI:
Customer service and support:
GenAI-powered customer support assistants are transforming how enterprises manage customer inquiries. AI chatbots and virtual assistants can handle routine tasks such as answering questions, troubleshooting product issues, and processing customer requests like order tracking or returns. These assistants provide real-time responses to customers, improving service speed and reducing the workload on human agents. Additionally, they are available 24/7 and on-demand, offering a seamless customer experience and increasing satisfaction. For example, Klarna’s AI assistant is doing the equivalent work of 700 human agents and will drive an estimated $40 million profit improvement for the fintech company in 2024.
Product recommendation and personalized e-commerce:
E-commerce companies are leveraging GenAI technologies to enhance the shopping experience by providing dynamic, real-time product recommendations based on customer browsing history, preferences, and past purchases. This way the shopping experience can become more tailored and personalized for each customer, increasing both engagement and sales. AI also helps with predictive inventory management by analyzing purchasing patterns and customer demand, enabling businesses to stock the right products at the right time.
Conversational AI and voice assistants in healthcare:
AI assistants are set to revolutionize healthcare, offering a range of benefits from enhancing patient experience to improving workflow efficiency for medical professionals. These assistants are designed to assist with tasks like scheduling appointments, medication reminders, and even providing symptom checks. By automating administrative tasks, healthcare providers can reduce the workload on their staff, allowing them to focus on higher-value patient care activities. Additionally, voice assistants are used to support patients remotely, offering personalized health advice and answering medical queries in real-time, which can lead to better health outcomes.
Security and safety risks of GenAI in the enterprise
The integration of GenAI into enterprise environments can bring significant value, but it also introduces several security and safety risks that need to be addressed and governed continuously. We’ve grouped enterprise GenAI risks in 2 categories, even though many of them can overlap.
Internal risks of GenAI in the enterprise include:
RAG poisoning: Attackers may manipulate the data that the AI retrieves, resulting in malicious outputs or unauthorized access to sensitive data. This risk applies to both internal and external use-cases of GenAI in enterprises. Below you can see how RAG chatbots can be injected with poisoned data:
Off-topic usage: Employees might use GenAI for unintended purposes, which could unnecessarily drain resources and lead to a denial of service.
Hallucinations and RAG imprecisions: AI systems may generate incorrect or irrelevant information and content, leading to poor decision-making based on false data and potential harm of end-users.
Harmful content/toxicity: There is a risk that GenAI could generate offensive or inappropriate content, harming employee safety and damaging internal culture.
Shadow AI: Unauthorized use of GenAI applications in an enterprise environment can lead to sensitive company and user information being leaked.
External risks escalate because of their exposure to customers and competitors:
Sensitive data or business context leakage: GenAI may inadvertently reveal confidential business strategies or proprietary information, if not properly secured.
User data leakage: Without robust safeguards, customer or partner personal information could be exposed, leading to privacy breaches.
Jailbreaks: Malicious users may exploit vulnerabilities in AI systems to make them behave in unintended, dangerous ways - resulting in reputational damage for the company.
Competitor mentioning: AI might unintentionally reference competitor products or guide users to websites and resources of competitors.
Social engineering/phishing: Attackers could use AI to generate convincing phishing messages, tricking other, unsuspecting users into revealing sensitive data and information.
In order to ensure the secure and effective deployment of GenAI assistants and chatbots for internal and external use, enterprises must address the mentioned risks with the right security and safety measures specifically for LLMs and GenAI. The security concerns of AI go beyond those of traditional cybersecurity, whereas the non-deterministic nature of generative AI requires a proactive and continuous security approach with the right toolset.
The integration of GenAI into enterprise environments can bring significant value, but it also introduces several security and safety risks that need to be addressed and governed continuously. We’ve grouped enterprise GenAI risks in 2 categories, even though many of them can overlap.
Internal risks of GenAI in the enterprise include:
RAG poisoning: Attackers may manipulate the data that the AI retrieves, resulting in malicious outputs or unauthorized access to sensitive data. This risk applies to both internal and external use-cases of GenAI in enterprises. Below you can see how RAG chatbots can be injected with poisoned data:
Off-topic usage: Employees might use GenAI for unintended purposes, which could unnecessarily drain resources and lead to a denial of service.
Hallucinations and RAG imprecisions: AI systems may generate incorrect or irrelevant information and content, leading to poor decision-making based on false data and potential harm of end-users.
Harmful content/toxicity: There is a risk that GenAI could generate offensive or inappropriate content, harming employee safety and damaging internal culture.
Shadow AI: Unauthorized use of GenAI applications in an enterprise environment can lead to sensitive company and user information being leaked.
External risks escalate because of their exposure to customers and competitors:
Sensitive data or business context leakage: GenAI may inadvertently reveal confidential business strategies or proprietary information, if not properly secured.
User data leakage: Without robust safeguards, customer or partner personal information could be exposed, leading to privacy breaches.
Jailbreaks: Malicious users may exploit vulnerabilities in AI systems to make them behave in unintended, dangerous ways - resulting in reputational damage for the company.
Competitor mentioning: AI might unintentionally reference competitor products or guide users to websites and resources of competitors.
Social engineering/phishing: Attackers could use AI to generate convincing phishing messages, tricking other, unsuspecting users into revealing sensitive data and information.
In order to ensure the secure and effective deployment of GenAI assistants and chatbots for internal and external use, enterprises must address the mentioned risks with the right security and safety measures specifically for LLMs and GenAI. The security concerns of AI go beyond those of traditional cybersecurity, whereas the non-deterministic nature of generative AI requires a proactive and continuous security approach with the right toolset.
The integration of GenAI into enterprise environments can bring significant value, but it also introduces several security and safety risks that need to be addressed and governed continuously. We’ve grouped enterprise GenAI risks in 2 categories, even though many of them can overlap.
Internal risks of GenAI in the enterprise include:
RAG poisoning: Attackers may manipulate the data that the AI retrieves, resulting in malicious outputs or unauthorized access to sensitive data. This risk applies to both internal and external use-cases of GenAI in enterprises. Below you can see how RAG chatbots can be injected with poisoned data:
Off-topic usage: Employees might use GenAI for unintended purposes, which could unnecessarily drain resources and lead to a denial of service.
Hallucinations and RAG imprecisions: AI systems may generate incorrect or irrelevant information and content, leading to poor decision-making based on false data and potential harm of end-users.
Harmful content/toxicity: There is a risk that GenAI could generate offensive or inappropriate content, harming employee safety and damaging internal culture.
Shadow AI: Unauthorized use of GenAI applications in an enterprise environment can lead to sensitive company and user information being leaked.
External risks escalate because of their exposure to customers and competitors:
Sensitive data or business context leakage: GenAI may inadvertently reveal confidential business strategies or proprietary information, if not properly secured.
User data leakage: Without robust safeguards, customer or partner personal information could be exposed, leading to privacy breaches.
Jailbreaks: Malicious users may exploit vulnerabilities in AI systems to make them behave in unintended, dangerous ways - resulting in reputational damage for the company.
Competitor mentioning: AI might unintentionally reference competitor products or guide users to websites and resources of competitors.
Social engineering/phishing: Attackers could use AI to generate convincing phishing messages, tricking other, unsuspecting users into revealing sensitive data and information.
In order to ensure the secure and effective deployment of GenAI assistants and chatbots for internal and external use, enterprises must address the mentioned risks with the right security and safety measures specifically for LLMs and GenAI. The security concerns of AI go beyond those of traditional cybersecurity, whereas the non-deterministic nature of generative AI requires a proactive and continuous security approach with the right toolset.
How SplxAI helps enterprises deploy GenAI securely
By providing the first platform worldwide that fully automates the AI pentesting and red teaming process, SplxAI is committed to helping enterprises adopt GenAI and LLM technologies securely. AI red teaming, when performed manually, is a tedious and long process, which can take up to multiple weeks depending on the GenAI use case. Apart from automating AI red teaming and showing practitioners the complete risk surface of their AI apps, the SplxAI platform also provides actionable mitigation strategies that can be directly applied to make the assessed AI systems more robust and secure. In addition, we support initiatives that create AI Red Teaming industry standards in order to help enterprises build required processes. Here is a brief overview of all the capabilities our platform offers to enterprises looking to make their AI safe and trusted:
Automated LLM pentesting & red teaming: Test your AI system or chatbot for 20+ GenAI-specific security and safety risks. Make your attack scenarios domain-specific to your use-case by providing as many details as possible and create your custom attack test suite.
Dynamic mitigation strategies: Make your AI systems more resilient and robust over time by taking specific, actionable steps.
Continuous threat monitoring: Monitor adversarial activity of your productive AI systems in real-time and get alerts when someone tries an attack on your application. Make sure your GenAI apps operate within defined security parameters at all times.
Compliance framework mapping: Adhere to regulatory requirements specific to your AI use-case with our automated framework mapping. Based on the red teaming results of your app, our platform shows you where and why you are failing to be compliant with the most important AI compliance standards, like MITRE ATLAS, OWASP LLM Top 10, the EU AI Act, and 10+ more.
By providing the first platform worldwide that fully automates the AI pentesting and red teaming process, SplxAI is committed to helping enterprises adopt GenAI and LLM technologies securely. AI red teaming, when performed manually, is a tedious and long process, which can take up to multiple weeks depending on the GenAI use case. Apart from automating AI red teaming and showing practitioners the complete risk surface of their AI apps, the SplxAI platform also provides actionable mitigation strategies that can be directly applied to make the assessed AI systems more robust and secure. In addition, we support initiatives that create AI Red Teaming industry standards in order to help enterprises build required processes. Here is a brief overview of all the capabilities our platform offers to enterprises looking to make their AI safe and trusted:
Automated LLM pentesting & red teaming: Test your AI system or chatbot for 20+ GenAI-specific security and safety risks. Make your attack scenarios domain-specific to your use-case by providing as many details as possible and create your custom attack test suite.
Dynamic mitigation strategies: Make your AI systems more resilient and robust over time by taking specific, actionable steps.
Continuous threat monitoring: Monitor adversarial activity of your productive AI systems in real-time and get alerts when someone tries an attack on your application. Make sure your GenAI apps operate within defined security parameters at all times.
Compliance framework mapping: Adhere to regulatory requirements specific to your AI use-case with our automated framework mapping. Based on the red teaming results of your app, our platform shows you where and why you are failing to be compliant with the most important AI compliance standards, like MITRE ATLAS, OWASP LLM Top 10, the EU AI Act, and 10+ more.
By providing the first platform worldwide that fully automates the AI pentesting and red teaming process, SplxAI is committed to helping enterprises adopt GenAI and LLM technologies securely. AI red teaming, when performed manually, is a tedious and long process, which can take up to multiple weeks depending on the GenAI use case. Apart from automating AI red teaming and showing practitioners the complete risk surface of their AI apps, the SplxAI platform also provides actionable mitigation strategies that can be directly applied to make the assessed AI systems more robust and secure. In addition, we support initiatives that create AI Red Teaming industry standards in order to help enterprises build required processes. Here is a brief overview of all the capabilities our platform offers to enterprises looking to make their AI safe and trusted:
Automated LLM pentesting & red teaming: Test your AI system or chatbot for 20+ GenAI-specific security and safety risks. Make your attack scenarios domain-specific to your use-case by providing as many details as possible and create your custom attack test suite.
Dynamic mitigation strategies: Make your AI systems more resilient and robust over time by taking specific, actionable steps.
Continuous threat monitoring: Monitor adversarial activity of your productive AI systems in real-time and get alerts when someone tries an attack on your application. Make sure your GenAI apps operate within defined security parameters at all times.
Compliance framework mapping: Adhere to regulatory requirements specific to your AI use-case with our automated framework mapping. Based on the red teaming results of your app, our platform shows you where and why you are failing to be compliant with the most important AI compliance standards, like MITRE ATLAS, OWASP LLM Top 10, the EU AI Act, and 10+ more.
Conclusion
Enterprises are in the perfect position to gain tremendous value from adopting GenAI and integrating it into existing workflows. However, as stated in this article, the risks introduced by AI technologies go beyond traditional cyber threats and require new security and safety measures, which cannot be warranted by conventional security tools. To realize the true potential of GenAI while minimizing risks, companies must prioritize security at every stage of their AI journey. SplxAI offers the tools, expertise, and solutions to ensure that your GenAI applications remain secure, compliant, and effective, driving real business value without compromising safety.
Enterprises are in the perfect position to gain tremendous value from adopting GenAI and integrating it into existing workflows. However, as stated in this article, the risks introduced by AI technologies go beyond traditional cyber threats and require new security and safety measures, which cannot be warranted by conventional security tools. To realize the true potential of GenAI while minimizing risks, companies must prioritize security at every stage of their AI journey. SplxAI offers the tools, expertise, and solutions to ensure that your GenAI applications remain secure, compliant, and effective, driving real business value without compromising safety.
Enterprises are in the perfect position to gain tremendous value from adopting GenAI and integrating it into existing workflows. However, as stated in this article, the risks introduced by AI technologies go beyond traditional cyber threats and require new security and safety measures, which cannot be warranted by conventional security tools. To realize the true potential of GenAI while minimizing risks, companies must prioritize security at every stage of their AI journey. SplxAI offers the tools, expertise, and solutions to ensure that your GenAI applications remain secure, compliant, and effective, driving real business value without compromising safety.
Deploy your AI apps with confidence
Deploy your AI apps with confidence
Deploy your AI apps with confidence
