Top 7 AI Security Threats and How to Protect Your Business

Artificial Intelligence (AI) is creating an attack surface that's evolving rapidly. As AI technologies create new vulnerabilities, organizations need to take a proactive approach to cybersecurity.

If you use Generative AI (GenAI) within your organization, you will know all about its enormous potential. However, the more AI tools you integrate into your business operations, the more risk you will introduce into your environment. 

A great example is Slack AI's prompt injection vulnerability, which researchers discovered before threat actors were able to find and exploit it. The add-on service for Salesforce's messaging platform, which uses conversation data to provide generative tools like summarizing conversations and answering queries, was found to be vulnerable to prompt injection attacks that could extract data from private Slack channels. 

In this case, cybercriminals could potentially trick the AI system because of the AI's inability to distinguish between legitimate and malicious instructions. They could also create convincing phishing links within the chatbot's responses by manipulating it. 

Although this vulnerability has been patched, concerns remain about hidden malicious instructions in uploaded files such as PDFs. And, of course, there is always the risk of uploading malware into AI systems with any type of file without adequate protections.

This security incident highlights broader challenges in securing AI systems integrated into widely used platforms. It also emphasizes the need for robust incident response plans.

According to Darktrace's State of AI in Cybersecurity, as many as 78% of CISOs surveyed stated that AI-driven cyber threats had already had a significant impact on their organization. 

Although most cybersecurity professionals felt more prepared for AI-powered threats than they did the year before, 45% still felt their organizations were unprepared. This is mainly because of challenges like skills and knowledge gaps concerning AI-powered countermeasures and a shortage of security professionals to manage tools and alerts properly.

Still, no organization wants such security risks lurking within its enterprise infrastructure. As such, it's critical to understand these AI security risks and deploy corresponding security measures and frameworks to mitigate them. 

The top 7 AI security risks and how organizations can defend themselves:

Data Poisoning

Whenever threat actors insert malicious code into the data that's pushed into AI systems, that's a data poisoning attack. Cybercriminals can manipulate training data by skewing the model's learning process and tampering with its functionality, leading to biased, inaccurate, or even harmful model behavior. Tampering with an AI system's decision-making ability is dangerous and can have far-reaching consequences. 

For example, by adding malicious inputs into datasets, hackers can make a facial recognition system misclassify certain faces or make a self-driving car's object detection system misidentify stop signs. 

A single breached device can insert corrupted updates in federated learning setups, undermining model effectiveness or altering results. Likewise, in man-in-the-middle attacks, hackers can tamper with inputs to AI systems in real time and have a devastating impact. 

Although data poisoning has serious consequences, most organizations never know it happened until they fall victim to a security incident. So, if you're not ready for such AI-driven security events, it's time to regroup, fortify your security posture, and update incident response plans. 

Risk Mitigation Tips:

• Apply anomaly detection and statistical analysis to threat detection protocols and identify suspicious data points within training datasets.
• Always train on diverse, vetted datasets from trusted sources.
• Continuous monitoring of model performance. This approach helps security teams detect unusual behavior or degradation.
• Only use secure data storage systems with robust and tightly controlled access and encryption.
• Use strong data validation and quality checks at the ingestion stage and across cleaning pipelines.

Adversarial Attacks

Cyber threats like adversarial attacks aim to slightly alter input data to intentionally mislead AI models. Whenever this happens, the machine learning algorithms are compromised. Their decision-making process is affected, and they can produce erroneous predictions or classifications.

For example, cybercriminals can make minor alterations to an image of a cat to make an image recognition model classify it as a dog. Although this example may sound like a minor hiccup, these types of security threats can have severe consequences in applications like autonomous vehicles or fraud detection.

Although potential threats like adversarial attacks and data poisoning may sound similar, both are distinct machine-learning models. Adversarial attacks target a trained model directly by feeding it manipulated inputs. Data poisoning attacks, on the other hand, target the training data itself, poisoning the model's learning process.

Risk Mitigation Tips:

• Train machine learning algorithms on both clean and adversarial examples to improve and make them more robust. 
• Apply input preprocessing and defensive distillation.
• Use input sanitization techniques and feature squeezing to reduce the impact of malicious perturbations.
• Use ensemble modeling techniques, combining predictions from multiple models to increase resilience.

Model Inversion and Data Leakage

Model inversion may occur when AI models, particularly large language models (LLMs), inadvertently retain or expose sensitive details from their training data or user inputs. Hackers can also analyze model outputs to infer private information, including personally identifiable information. 

For example, when employees used AI applications like OpenAI's ChatGPT for work-related tasks, Samsung faced a potential data leak. This is because staff inadvertently shared proprietary code and internal meeting notes with the chatbot. If a cybercriminal managed to manipulate the chatbot to leak the data, it could have had a disastrous impact on the company.

Risk Mitigation Tips:

• Data minimization techniques, for example, should be used only with the data necessary for training. 
• Protect sensitive data by applying anonymization and pseudonymization techniques to avoid a potential data breach and regulatory compliance violations. 
• Use differential privacy techniques during AI development, model development, and training. This approach helps protect sensitive information by adding "noise" to the training data.
• Keep a close eye on model outputs and keep an eye out for potential privacy breaches. 
• Use automation whenever possible to optimize security systems and minimize the security team's workload.  

Model Extraction / Theft

Malicious actors often attempt to reverse-engineer the target machine's AI model through repeated queries or model extraction cyberattacks. For example, they can recreate a proprietary machine learning model via its Application Programming Interface (API).

Once the cybercriminals understand the AI model's weaknesses, they can potentially get access to private attributes, copyrighted material, or even trade secrets.

Risk Mitigation Tips:

• Continuously monitor for abnormal query patterns and conduct regular assessments.
• Limit API access, apply user authentication, and employ rate-limiting to avert excessive querying.
• Add AI model watermarks to help identify unauthorized copies whenever possible and employ federated learning.

AI System Supply Chain Attacks

Cybercriminals can inject malicious code or data into third-party AI tools, libraries, or pre-trained models to compromise an AI system's integrity. Whenever this happens, it can lead to backdoors (especially in pre-trained models or open-source tools), vulnerabilities, or compromised model performance. 

It’s also not just about the AI tools the organization uses to get the job done. Companies must also strictly control what AI tools employees use within the enterprise network. All it takes is one malicious tool to add chaos to the infrastructure. 

Risk Mitigation Tips:

• Thoroughly vet third-party components and vendors and implement comprehensive security checks across the AI supply chain.
• Always use trusted and verified sources for pre-trained models and libraries.
• Regularly scan code, audit dependencies, and search for vulnerabilities.
• Consistently update libraries and dependencies to mitigate risk.
• Employ model provenance techniques to track the origin and integrity of AI models.
• Prioritize risk management across the organization and look for supply chain attacks.

Unauthorized Model Access

Whenever businesses build their own AI and machine learning models, there is always the risk of insider threats. Suppose an employee has unauthorized access to model files or endpoints. In that case, there's always a risk of them copying or deploying the model elsewhere or for the benefit of a competitor. As such, organizations must take insider threats seriously and take steps to mitigate risks.

Risk Mitigation Tips:

• Apply role-based access controls (RBAC).
• Create awareness and mitigate AI risks by enforcing strict access control and authentication protocols across the organization.
• Encrypt all training datasets, machine learning models, and AI models, whether at rest or in transit.

Bias and Discrimination

Whenever businesses use AI, there's always a risk of bias and discrimination. If the training data contains certain biases, the AI model can perpetuate or even amplify these biases. Although this is not exactly an AI security risk, it does come with the risk of legal ramifications. 

For example, an AI model in the banking sector can excessively deny loans to specific demographic groups. This makes it essential to take steps to mitigate the risk of bias and discrimination during the development process. 

Risk Mitigation Tips:

• Only work with diverse and representative training datasets.
• Regularly audit AI models for biased discriminatory patterns.
• Use fairness-aware AI algorithms and tools to mitigate the risk of bias in the AI model's predictions.
• Incorporate diverse perspectives during development.

Conclusion

The top 7 AI security risks above are just a touch of the iceberg. Organizations should regularly train staff and stakeholders to be alert for AI-driven social engineering attacks, including phishing campaigns and deepfakes. 

Security teams must also use thoroughly vetted AI security tools with advanced threat detection capabilities and limited false positives. Whenever an organization can't proactively fortify and manage its security posture to defend against AI-driven threats, it'll help to partner with an established Managed Security Services (MSS) provider. This approach provides organizations immediate access to much-needed resources, including leading security professionals and AI-powered security tools.