Perhaps one of the most talked-about avatars of AI—Generative AI (GenAI)—is making waves across industries by using training data to generate text, images, videos, and code —all from simple prompts.
Businesses are eager to adopt GenAI for quick, high-quality content with minimal effort. But building in-house GenAI tools isn’t feasible or cost-effective for everyone. Relying on third-party solutions like OpenAI or Stability AI carries risks, such as errors and data security issues.
One of the biggest risks of using GenAI is the potential loss of intellectual property. GenAI models are trained on vast datasets to generate text, images, audio, and video based on patterns in the data. However, these models retain input data for continuous learning, which can inadvertently expose proprietary information, raising concerns about privacy and security.
While GenAI boosts efficiency by drafting emails, summarizing content, coding, debugging, and creating images—it also opens the door to misuse. For instance, contract workers might extract data from the company that they should not have access to.
Another issue is AI hallucination, where a large language model (LLM) generates illogical or completely incorrect outputs. In healthcare, for example, a model might misidentify a benign skin lesion as malignant, potentially leading to unnecessary treatments.
Like other cloud-powered technologies, GenAI is vulnerable to Distributed Denial of Service (DDoS) and Server-Side Request Forgery (SSRF) attacks. A DDoS attack can disrupt operations and cause financial loss, while SSRF attacks trick servers into sending requests to unintended locations, exposing internal systems and sensitive data. Cybercriminals can also manipulate models through prompt injections, leading to the creation of malicious content.
So, with all of these security risks, how do you know what to address first? And how can you prioritize security from day one to avoid deploying GenAI models vulnerable to attacks from both internal and external threats?
To make GenAI systems more reliable and resilient against threats, it is vital to conduct regular training sessions for data scientists, AI-ML developers, and other IT specialists on AI security best practices. According to a joint study by Amazon Web Services and IBM, while 82% of C-suite executives understand that trustworthy and secure AI is essential, only 24% effectively included a security component in their GenAI projects. Such concerns need to addressed before they escalate into data breach, fines, and loss of customer trust for businesses.
Organizations must encourage a security-by-design mindset across their AI project lifecycle. Teams must proactively identify all possible vulnerabilities and build a minimal viable security (MVS) blueprint.
The security-by-design principles to safeguard GenAI systems and LLMs include:
As in any other business project, cross-functional collaboration is vital to success of designing and implementing GenAI systems. AI and cybersecurity teams working in tandem can conduct joint threat assessments to ensure security is embedded from the start, focusing on allowing only authorized personnel to access sensitive information from the models.
To prevent the incorporation of poisoned datasets and models into GenAI systems, teams can employ techniques such as cryptographic hashing and digital signatures that verify the integrity of data and model files. Security professionals can also help AI teams in using secure data pipelines, continuous monitoring, and anomaly detection to prevent unauthorized retrieval of information and security threats on GenAI models. The practices enable GenAI systems to stay reliable and secure to give unbiased and accurate outputs.
Collaborative working and regular communication and between AI and cybersecurity personnel help to develop clear plans for incident response. An organization should ensure regular feedback loops between its AI and security teams for constant learning from new developments. It also helps to adjusting algorithms and testing patterns for model improvements.
Effective risk management in GenAI systems requires continuous attention to data security and privacy across the lifecycle—from development to decommissioning. This involves safeguarding datasets and ensuring compliance with data privacy regulations at every stage.
To protect sensitive information, administrators must anonymize personally identifiable information and implement secure coding practices, data encryption, and role-based access control Continuous system monitoring is essential to detect and respond to incidents promptly. Timely updates and patches are crucial to prevent downtime or vulnerabilities.
When retiring an AI system, proper decommissioning procedures are critical. Secure data deletion and dismantling of infrastructure are necessary to prevent information theft and unauthorized access.
The threat landscape is evolving, with cybercriminals exploiting GenAI system vulnerabilities to bypass authentication systems and firewalls. To thwart such attempts organizations must implement efficient monitoring mechanisms that catch anomalous behavior or potential risks in real time. They need to track data inputs and output, model performance metrics, and system logs for suspicious activity.
Threat modeling for GenAI and publicly or privately hosted LLMs helps to identify possible threats, assess their effect, and design mitigation strategies customized for different deployment scenarios. It helps GenAI teams anticipate and prevent adversarial attacks, data poisoning and theft of techniques used to build their model.
The core areas of consideration in building AI-specific threat modeling techniques are data integrity and security, model resilience, and potential fallout from a compromised AI model. Organizations can develop and run robust and secure GenAI systems by:
A security-first approach to any GenAI system is incomplete without adopting security frameworks related to data—the fuel that powers AI. While these are exhaustive across regions and keep evolving, some of the important ones for GenAI include NIST CSF, ISO 27001, and SOC 2. NIST Cybersecurity Framework (CSF) provides a flexible, risk-based approach for cybersecurity management.
ISO 27001 certified organizations can manage sensitive data, with emphasis on establishing, implementing, maintaining, and continually improving their information security management system (ISMS). SOC 2 is a voluntary compliance standard for service-based enterprises in the US. It focuses on data management based on five principles of security, availability, processing integrity, confidentiality, and privacy.
Companies developing GenAI models also must ensure compliance with industry-specific norms. For industries such as retail and healthcare in California, Central Consumer Protection Authority (CCPA) mandates that GenAI systems manage consumer data with clear disclosure and opt-out options, safeguarding consumer privacy and control over personal information.
In the US healthcare industry, Health Insurance Portability and Accountability Act (HIPAA) requires GenAI systems to protect sensitive patient information through stringent security and privacy controls, ensuring compliance with regulations that safeguard health data confidentiality and integrity.
While staying compliant with regulations, the best practices that all businesses can follow in optimizing their GenAI development are:
The race for deploying intelligent technologies is getting more intense for companies that are focused on operational efficiencies, connected supply chains, engaging customer experiences, and a measurable impact on business bottom line.
However, the likelihood of success is higher only for entities following a security-by-design approach. Organizations must embed security as a core component of their GenAI projects from the outset to prevent data breaches, misuse, and other vulnerabilities.
Opsin helps organizations implement security-by-design in their GenAI initiatives. From role-based access control to anonymization and redaction of sensitive data, Opsin ensures a secure rollout of GenAI applications from experimentation to production. By prioritizing these measures, businesses can safeguard current models and future systems, ensuring sustainable success in the ever-evolving AI landscape.
