The advent of artificial intelligence (AI) has revolutionized nearly every sector of society. However, the promising capabilities of AI come with significant challenges, especially in cybersecurity.
Since its inception, Mantel Group has helped large organizations with their security challenges with projects ranging from advisory and vulnerability remediation to cloud hardening, DevSecOps and compliance. Securing machine learning workflows has become essential to defending these organizations against emerging threats, one of which is adversarial AI.
What is Adversarial AI?
Simply put, adversarial AI refers to a set of techniques used to deceive AI systems. By exploiting vulnerabilities in AI models, attackers can trick these systems into making wrong decisions in favor of the attacker’s motives. These techniques are surprisingly popular these days. In 2022 alone, 30% of all AI cybersecurity incidents used adversarial techniques, according to a study conducted by Microsoft. Despite the danger, most organizations are woefully ill-prepared, and a Microsoft study found that nearly 90% of businesses lack strategies to deal with adversarial AI attacks. This is particularly concerning given that all AI-related adversarial attacks are categorized as “major” incidents to an organization’s cybersecurity and can cause significant damage to both reputation and revenue. That’s what you should do.
Current Gaps in Cybersecurity Solutions
Despite advances in cybersecurity solutions, significant gaps still exist that leave organizations vulnerable to adversarial AI. Cybersecurity has traditionally focused on protecting networks, devices, and software applications from threats. But AI brings a new dimension to cybersecurity, requiring a new approach to defense. Many existing solutions ignore AI-specific considerations such as authentication, separation of duties, input validation, and denial of service mitigation. Without addressing these concerns, AI/ML services can remain vulnerable to adversaries of varying skill levels, from novice hackers to state-sponsored attackers.
Overcoming Challenges: Key Elements of Secure AI
Building robust defenses against adversarial AI requires building security into the fabric of AI systems. There are four key factors to consider:
- Identifying Bias: AI systems should be designed to identify biases in data and models without being influenced by these biases in the decision-making process. To achieve this, the system’s understanding of biases, stereotypes, and cultural constructs must be continuously learned and updated. By identifying and mitigating biases, AI systems can be protected against social engineering attacks and dataset tampering that exploit these biases.
- Malicious input identification: One common adversarial AI strategy is to introduce maliciously crafted inputs designed to mislead the AI system. Machine learning algorithms must therefore have the ability to distinguish between malicious input and benign “black swan” events and reject training data that adversely affects results.
- ML Forensics Capabilities: Transparency and accountability are the cornerstones of ethical AI. To achieve this goal, AI systems must have built-in forensics capabilities that provide users with clear insight into the AI’s decision-making process. These capabilities act as a form of “AI intrusion detection”, allowing us to track exactly when a classifier made a decision, what data influenced it, and whether it is reliable.
- Protect sensitive data: AI systems often need access to large amounts of data, including highly sensitive data. AI must be designed to recognize and protect sensitive information even when humans are unaware of its sensitivity.
Importantly, protecting ML models from adversarial AI is not a one-time task, but an ongoing process that spans the entire ML model lifecycle, from development to deployment to under attack.
At Mantel Group, we understand the nuances of AI and cybersecurity. We can assist your company in each of these areas to help you become more resilient to adversarial AI attacks. Our custom solutions are designed to meet your specific needs and help make you more resistant to adversarial AI attacks. We believe in building AI systems that are not only smart, but safe.
Securing machine learning workflows against adversarial AI is more than necessary. It is our duty to protect our business and our customers. Incorporating these elements into an AI system creates a strong foundation for protecting AI from adversarial threats.
