Here is our evaluation of the top 7 GenAI security tools on the market today (Aug 2024), so you can pick what is best for your business:
Aporia – Best for providing end-to-end security for any GenAI application
Lakera Guard – Best for cyber security threats
Calypso AI – Best for governance of generative AI models
Lasso security – Best for contextual data protection (CDP)
WhyLabs – Best for continuous monitoring
Protect AI – Best for easy customization
LLM Guard – Best for its cost-effective CPU inference
Here is what makes each security tool unique in its own way, so you can find the best one for your business.
| Best For | Key features | |
| Aporia | Providing end-to-end security for any GenAI application | * Security guardrails (prompt injections, data leakage, and more) * Reliability guardrails * Real-time issue resolution * Lightning-fast latency * Multimodal support |
| Lakera Guard | Cyber security threats | * Prompt injection prevention * Data leakage prevention * Red teaming simulations |
| Calypso AI | Governance of generative AI models | * Generative AI security scanner * Generative AI governance tool * Compliance and cost management |
| Lasso security | Contextual data protection (CDP) | * Contextual data protection * Real-time detection and alerting * Seamless integration |
| WhyLabs | Continuous monitoring | * Real-time security guardrails * Continuous monitoring * Integrative compatibility |
| Protect AI | Easy customization | * AI Security Posture Management (AI-SPM) * Enterprise-level scanning * End-to-end security monitoring |
| LLM Guard | It’s cost-effective CPU inference | * Built-in sanitization and redaction * Data leakage prevention * Prompt injection resistance |
Imagine a world where your marketing copy writes itself, your software code debugs independently, and customer service runs 24/7 without a single human agent.
This isn’t science fiction—it’s the reality of generative AI (GenAI) in 2024. But this incredible capability comes with a hidden threat: a new breed of security issues that could cripple your business if you’re not prepared.
As organizations increasingly integrate GenAI into their systems, they become vulnerable to numerous novel threats, including data breaches, model poisoning attacks, and misinformation.
According to IBM’s 2023 X-Force Threat Intelligence Index report, there was a 26% increase in AI-related security incidents in 2023, highlighting the growing concern around the potential misuse and exploitation of GenAI.
To navigate this new age of cybersecurity, businesses must adopt robust GenAI security tools. These tools are essential for safeguarding sensitive data, ensuring compliance, and maintaining trust in AI-driven processes.
We will explore the top GenAI security tools available in 2024, covering how to protect your organization from emerging threats. Moreover, we will also delve into the history of GenAI, its rapid penetration in the enterprise sector, and why GenAI security is crucial for modern cybersecurity strategies.
The journey of generative AI (GenAI) began in the 1950s with rule-based systems designed to generate patterns. These early models laid the groundwork for future advancements by demonstrating the potential of algorithms to create new data. By the 1980s, neural networks started gaining traction, with pioneers like Geoffrey Hinton developing techniques such as Boltzmann Machines to generate data through interconnected nodes.
The true turning point for GenAI came in the 2010s with the advent of deep learning and Generative Adversarial Networks (GANs), introduced by Ian Goodfellow in 2014.
This innovation, advancements in deep learning, and increased computational power propelled GenAI into the mainstream. The exponential growth in data availability and the development of more sophisticated algorithms further accelerated GenAI’s capabilities, enabling it to produce high-quality outputs across various modalities.
There is no debate around the fact that ChatGPT significantly heightened awareness of GenAI’s capabilities. According to a Gartner poll, 45% of executive leaders reported that ChatGPT’s attention prompted them to increase their investments in AI technologies. This surge in interest has led to more organizations exploring and integrating GenAI solutions into their operations.
The adoption of generative AI (GenAI) has surged dramatically in recent years, transforming both enterprise and consumer-facing products. According to a 2023 survey by PwC, 73% of U.S. companies have integrated AI into various aspects of their business, with over half (54%) specifically implementing GenAI solutions.
The widespread adoption of GenAI underscores its potential to reshape the business landscape, making it an indispensable tool for modern enterprises.
In 2024, cybersecurity emerges as a particularly compelling theme, driven by two key factors. Firstly, the ever-evolving threat landscape necessitates that organizations maintain heightened vigilance and sustained investment in security measures. This dynamic environment ensures cybersecurity remains a critical priority for businesses across sectors.
The rising prominence of generative AI presents challenges and opportunities in cybersecurity. This rapid AI adoption is a double-edged sword: on the one hand, it empowers malicious actors to uncover vulnerabilities and refine their attack methodologies with unprecedented sophistication.
Let’s look at known GenAI security issues and their potential consequences.
Adversaries can corrupt the training data or the model itself, leading to compromised outputs that can be malicious or misleading.
Example: Instruction Tuning Manipulation
A recent paper, `Learning to Poison Large Language Models During Instruction Tuning, illustrated a novel data poisoning attack during the instruction tuning of LLMs, where adversaries inserted backdoor triggers into training data. This manipulation led to compromised model outputs for specific queries.
For instance, poisoning just 1% of the instruction tuning samples resulted in a performance drop rate of around 80% in various tasks. This highlights the vulnerability of LLMs to subtle yet impactful data poisoning attacks.
Adversarial Attacks involve minor alterations of inputs to deceive the AI into making incorrect decisions, which can have serious implications in critical applications like healthcare or autonomous driving.
Example: Manipulating Outputs with Adversarial Prompts
Adversarial attacks on LLMs involve crafting inputs that trigger the model to produce undesired or harmful outputs. For example, researchers have demonstrated that subtly altering the input text could manipulate a language model to output unsafe content or leak private information. These attacks are particularly challenging because they exploit the model’s inherent weaknesses in handling unexpected inputs. Such attacks can have severe implications in critical applications like healthcare, where incorrect decisions can jeopardize patient safety.
GenAI models can inadvertently perpetuate or even amplify existing biases present in the training data, leading to unfair or unethical outcomes.
Traditional cybersecurity frameworks primarily focus on protecting data and network integrity but fail to address these AI-specific vulnerabilities. The dynamic and self-learning nature of GenAI systems necessitates a new approach to security that includes continuous monitoring and real-time intervention.
One prominent example of bias in LLMs is Google’s hate-speech detection algorithm, Perspective. The model exhibited bias against black American speech patterns, flagging common slang used by this community as toxic. This bias arose because the training data lacked sufficient examples of the linguistic patterns typical of black American speech, leading to unfair and discriminatory outcomes.
This incident underscores the importance of diverse and representative training data to mitigate bias in AI systems.
The consequences of GenAI security breaches can be severe and far-reaching:
Let’s uncover some of the best GenAI security tools available for seamless integration in your systems today.
Aporia’s leading platform is designed to ensure AI systems’ security, reliability, and ethical compliance. Founded in 2019 by Liran Hason and Alon Gubkin, Aporia has quickly established itself as a leader in AI security and is trusted by Fortune 500s globally.
The Aporia platform provides real-time Guardrails to mitigate risks such as hallucinations, data leakage, and company compliance violations, ensuring AI applications perform reliably and securely across various modalities, including text, and audio.
Aporia integrates seamlessly with popular AI models and systems, including GPT-X and Claude, and offers extensive customization options to tailor the tool to specific business needs. It supports integration with AI gateways like Portkey, Litellm, and Cloudflare, ensuring broad compatibility and ease of deployment.
Aporia stands out as a comprehensive solution for AI observability and security, providing robust features to ensure AI systems are secure, reliable, and compliant. Its real-time capabilities and extensive customization options make it a valuable tool for businesses aiming to deploy trustworthy AI applications.
Lakera Guard is an AI-powered threat intelligence software developed by Zurich-based AI security firm Lakera. Launched in 2021, Lakera Guard is designed to protect generative AI applications from various cyber threats, including prompt injections, data leakage, and other vulnerabilities.
Lakera Guard focuses on securing large language models (LLMs) integral to applications like chatbots, virtual assistants, and data analysis tools.
Lakera Guard is popular for its robust feature set and low latency solutions to secure LLMs.
CalypsoAI is a AI security platform founded in 2018, specializing in the protection and governance of generative AI models. The platform is designed to provide robust, scalable, and model-agnostic security solutions, ensuring the safe deployment and operation of large language models (LLMs) across various industries.
CalypsoAI stands out as a comprehensive AI security and governance solution, offering advanced features to protect against AI-specific threats.
Lasso Security, founded in 2023, specializes in cybersecurity for large language models (LLMs) within the generative artificial intelligence domain. The company offers a suite of security solutions designed to protect organizations from external cyber threats and internal vulnerabilities associated with using LLMs.
Lasso Security offers important features to protect against various AI-specific threats.
WhyLabs is an LLM security platform established in 2019, focusing on securing large language models (LLMs) against various cyber threats, including data leakage, prompt injections, and misuse. Recognized as a leader in AI observability and security, WhyLabs is trusted across sectors such as healthcare, finance, and e-commerce for its robust capabilities.
WhyLabs LLM Security is a crucial integration in securing AI applications, providing the necessary tools for organizations to deploy reliable, trustworthy LLMs with built-in safeguards against various security threats.
Protect AI, founded in 2022 by Ian Swanson, Daryan Dehghanpisheh, and Badar Ahmed, is a comprehensive platform designed to secure AI and machine learning (ML) systems. The company focuses on addressing unique security challenges associated with AI/ML applications, offering tools to detect, manage, and mitigate risks throughout the AI lifecycle.
Protect AI offers comprehensive tools, seamless integration, and customization options for organizations aiming to deploy secure and reliable AI applications.
LLM Guard is a comprehensive security toolkit designed to fortify the security of Large Language Models (LLMs). The primary purpose of LLM Guard is to ensure safe and secure interactions with LLMs by offering features such as sanitization, detection of harmful language, prevention of data leakage, and resistance against prompt injection attacks.
The tool is recognized for its robust security measures and cost-effective CPU inference, offering a substantial cost reduction with 5x lower inference expenses on CPU compared to GPU. LLM Guard supports various AI applications, including text, images, audio, and video, ensuring broad applicability across different use cases.
LLM Guard is vital for securing LLM applications, providing advanced features such as seamless integration and cost-effective CPU inference.
As we conclude this exploration of GenAI security in 2024, it is evident that the rapid adoption of GenAI has transformed the business landscape, offering unprecedented opportunities for innovation and growth.
However, this progress also brings new security challenges that demand robust solutions. By prioritizing security, organizations can harness the full potential of GenAI while mitigating the risks and ensuring a safe and trustworthy AI ecosystem.
As the GenAI landscape continues to evolve, ongoing vigilance and adaptation will be essential to maintaining security in this new age of technology.
GenAI risks include model poisoning, adversarial attacks, bias, data leakage, and prompt injections.
GenAI security safeguards sensitive data, ensures compliance, and maintains trust in AI-driven processes.
Aporia uses a combination of statistical analysis, anomaly detection, and custom policies to identify and override responses that deviate from expected patterns or contain factual errors.
GenAI presents new threats but also provides tools for enhanced defense and threat detection.
Consequences include financial losses, reputational damage, and societal harm.
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