Strategies to mitigate AI security and compliance risks
Published on: November 7, 2024
Last update: November 7, 2024
According to McKinsey, 65% of executives report that their organizations are exploring and implementing AI solutions. However, the rapid integration of AI usually overlooks critical security and compliance considerations, increasing the risk of financial losses and reputational damage due to unexpected AI behavior, security breaches, and regulatory violations.
At a time of rapid change fueled by AI, businesses can’t afford to treat security as an afterthought.
What are the top AI security and compliance concerns?
AI models are susceptible to adversarial attacks where malicious actors manipulate input data to deceive the system. Despite the growing awareness of AI security risks, many organizations still need to prepare. A survey by IBM Institute for Business Value found that 75% of executives believe that AI security is a top priority. Yet, PwC reports that 60% of organizations have experienced security incidents related to AI or machine learning.
Keeping up with changing security threats
The vast amounts of data required to train AI models create new attack surfaces for cybercriminals to exploit. For instance, AI-powered chatbots and virtual assistants may inadvertently expose sensitive information if not properly secured. The complex nature of AI algorithms can make it difficult to detect and trace security breaches, leaving organizations vulnerable to prolonged and undetected attacks.
Navigating complex data privacy and security regulations
There are also compliance issues. Many industries are subject to strict data privacy and security regulations, such as GDPR, the EU AI Act, or the Health Insurance Portability and Accountability Act (HIPAA) for the healthcare sector. AI systems often process large volumes of personal or sensitive data, challenging compliance with these regulations.
EY's research indicates that 60% of executives believe AI regulations will significantly impact their business operations. Deloitte further highlights this difficulty, with 70% of organizations needing help to keep up with these regulations.
Further, the "black box" nature of some AI algorithms can make it challenging to explain decision-making processes, which is a requirement in specific regulated industries. This lack of transparency can lead to unintended bias or discrimination, potentially resulting in legal and reputational risks for organizations.
The ethics of AI
Beyond security and compliance, the ethical implications of AI deployment are similarly important to consider. AI systems can perpetuate or amplify existing biases, leading to unfair or discriminatory outcomes.
This is particularly concerning in hiring, lending, or criminal justice, where AI-driven decisions can significantly impact individuals' lives. Organizations must also grapple with the ethical use of AI in data collection and analysis. The ability to gather and process vast amounts of personal data raises questions about privacy and consent that many businesses aren’t equipped to address.
Organizations require specialized knowledge and resources to manage AI security and compliance risks effectively. Still, as Accenture's 2024 risk survey shows, 80% of risk professionals perceive these risks as increasingly complex and severe.
Why cybersecurity is central to compliance
As organizations increasingly rely on AI and other advanced technologies, the risks associated with data breaches, cyberattacks, and unauthorized access have become more significant. Strong cybersecurity measures are necessary to protect sensitive information and comply with various regulations and industry standards.
One primary reason cybersecurity is central to compliance is the increasing emphasis on data privacy and protection. Regulations like GDPR and the California Consumer Privacy Act (CCPA) impose strict requirements on organizations to safeguard personal data.
Cybersecurity is also crucial for ensuring compliance with industry-specific regulations. For example, HIPAA requires healthcare organizations to protect patient health information (PHI)
At the same time, the Payment Card Industry Data Security Standard (PCI DSS) mandates specific security controls for entities that handle credit card data.
Adherence to these regulations often involves implementing cybersecurity measures to protect sensitive information and prevent unauthorized access. Failure to comply can result in significant penalties.
As organizations increasingly rely on AI and other advanced technologies, the risks associated with data breaches, cyberattacks, and unauthorized access have become more significant. Strong cybersecurity measures are necessary to protect sensitive information and comply with various regulations and industry standards.
Effective strategies for mitigating AI security and compliance risks
As organizations rapidly adopt AI technologies—especially large language models (LLMs) like those offered by AWS Bedrock—it's crucial to integrate security measures throughout the software development lifecycle (SDLC).
Organizations should seek out comprehensive security solutions that align with the secure software development lifecycle (SSDLC). This ensures security is embedded from inception through deployment. Here are key strategies to mitigate AI security and compliance risks.
Conduct risk assessments focused on AI and LLM applications
Understanding the unique risks associated with AI systems is the first step in safeguarding your organization. And in-depth security assessments tailored to AI and LLM applications are a great place to start. By conducting comprehensive risk assessments, you can understand your current security posture and provide actionable insights to enhance your defenses.
- Take advantage of white box assessments: White box assessments thoroughly evaluate your software development and deployment ecosystems, including CI/CD pipelines, cloud environments, and source control systems. These assessments go beyond standard checklists, offering a full-stack approach that considers each application component and its interdependent chains of risk.
- Leverage MlSecOps expertise: It’s critical to leverage expertise in machine learning security operations (MlSecOps) to identify vulnerabilities specific to AI and LLM applications. This proactive approach enables you to allocate resources effectively and address critical issues before they escalate.
Regular risk assessments are essential for identifying potential vulnerabilities in your AI systems. Prioritize mitigation efforts based on data sensitivity, model complexity, and regulatory requirements. This proactive approach allows you to allocate resources effectively and address critical issues before they escalate.
Develop a comprehensive AI security program
Implementing effective data governance is essential but must be part of a broader security strategy that addresses the complexities of AI and LLM applications. It’s important to have comprehensive security programs tailored to your unique needs.
- AI security strategy capability: An AI security strategy service allows you to holistically measure your security maturity across various domains within the SSDLC. This provides a detailed analysis of your security controls and policies, offering multiple options and timelines to improve metrics and report progress over time.
- Compliance alignment: Seek expertise that helps you navigate the complex regulatory landscape associated with AI technologies. This includes ensuring your data governance policies comply with regulations like GDPR, the EU AI Act, and industry-specific standards such as HIPAA.
By embedding security considerations into every stage of AI development and deployment, you can maintain data quality, security, and privacy throughout the data lifecycle.
Implement advanced security engineering for AI systems
AI models, particularly LLMs, present unique challenges that require specialized security engineering solutions. Consider bespoke security engineering services to enhance your AI systems' security posture.
- Monitoring systems for MlSecOps: Monitoring systems for MlSecOps enable real-time detection of anomalies and potential threats within AI applications. You should implement an approach that transcends standard model governance frameworks by providing a comprehensive solution that addresses the full stack of technologies comprising AI applications.
- Security automation: By integrating threat intelligence, data lakes, and security operations automation into your infrastructure, you can maintain the integrity and reliability of your AI models. We recommend leveraging AWS security services and AWS Bedrock's features to strengthen your defenses.
In the end, the goal is to ensure your AI systems are protected against adversarial attacks and unauthorized manipulations.
Integrate continuous monitoring and evaluation into AI workflows
Continuous monitoring is critical for maintaining the security and compliance of AI systems in a rapidly evolving threat landscape. You can achieve:
- Real-time visibility: Real-time visibility into system performance and security enables prompt detection and response to security incidents.
- Adaptive solutions: Effective monitoring systems are designed to adapt to the dynamic nature of AI systems, ensuring ongoing compliance with regulations and standards.
By integrating key sources and monitoring solutions into your AI workflows, you can stay ahead of emerging threats.
Adopt a holistic security approach for AI and LLM applications
Addressing AI security requires more than isolated solutions—it demands a holistic security program that considers the entire technology stack of AI and LLM applications. We advocate for comprehensive security program development that integrates all aspects of your AI systems.
- Full-stack security program development: Design and execute security programs that cover everything from risk assessments and engineering to strategic planning and implementation. This should include pre-planning for audits and incorporating best practices in AI security.
- Integration with SSDLC: Embrace an approach that ensures security is embedded throughout the SSDLC, reducing vulnerabilities and enhancing overall system integrity.
When you embrace a full-stack security approach, you minimize vulnerabilities and position your organization as a leader in secure AI adoption.
By embedding security considerations into every stage of AI development and deployment, you can maintain data quality, security, and privacy throughout the data lifecycle.
Manage third-party risks
AI systems often rely on third-party vendors and services, introducing additional security risks. It’s essential to manage these risks effectively through:
- Vendor assessments: Evaluate third-party security practices, ensuring they meet your security standards.
- Vendor management programs: Comprehensive programs include clear contractual obligations for data protection and compliance, regular audits, and monitoring of vendor access to your systems and data.
Your security is only as strong as your weakest link. By effectively managing third-party risks, you strengthen your overall security posture.
Benefits of mitigating AI security and compliance risks
Taking a proactive stance on AI security and compliance positions your organization as a leader rather than a follower. Below are some ways your organization will benefit.
Avoidance of legal and regulatory penalties
As AI technologies evolve, so do the legal and regulatory frameworks surrounding them. Adhering to regulations like the EU AI Act helps avoid costly penalties and strengthens legal defensibility. This way, you position your organization to:
- Adapt quickly to new regulations without disrupting operations.
- Avoid expensive legal battles and regulatory investigations.
- Maintain a competitive edge in markets where compliance is a key differentiator.
Remember: The cost of non-compliance often far outweighs the investment required to implement adequate security and compliance measures.
Operational efficiency and financial protection
Effective governance, risk management, and compliance (GRC) practices don't just mitigate risks—they can also streamline your operations. By implementing comprehensive AI security and compliance measures, you can:
- Reduce downtime caused by security breaches or compliance issues.
- Improve decision-making processes through better data management and analysis.
- Optimize resource allocation by identifying and addressing inefficiencies.
The benefits are equally compelling from a financial perspective. You protect your organization's bottom line by avoiding fines, penalties, and legal disputes related to AI misuse or data breaches. Consider the potential costs of a significant security incident or compliance violation—not just in immediate financial losses but also long-term damage to your reputation and market position.
Enhanced brand reputation and consumer trust
Your brand's reputation is intrinsically linked to your ability to protect sensitive information and use AI ethically. By implementing stringent security measures and ensuring AI compliance, you demonstrate a commitment to responsible innovation. This can significantly enhance your brand in the marketplace, setting you apart from competitors perceived as less trustworthy or technologically savvy.
In addition, when customers feel confident that their data is secure and AI systems are being used ethically, they're more likely to engage with your organization.
Ultimately, the goal isn't just to avoid pitfalls—it's to harness AI's full potential while maintaining the highest security standards. When building an effective AI strategy, security should come first.
