AI Security Radar 2024: Cyber Solutions for a Trustworthy AI

Unlike traditional IT systems, AI relies on statistical decision-making, introducing unique challenges in cybersecurity. These include risks such as poisoning, where attackers manipulate data or models to alter an AI’s decision-making; oracle attacks, which compromise models through careful analysis of inputs and outputs; and evasion, where small perturbations in inputs cause significant errors in outputs.

Moreover, the emergence of AI-focused regulations and standards such as the AI Act, and guidelines from OWASP (Open Web Application Security Project) and NIST (National Institute of Standards and Technology), highlights the need for specialized security solutions.

In response to these new threats and requirements, a new breed of cybersecurity measures must be applied. These measures could be built internally if you have sufficient resources and expertise but it’s rarely the case. Therefore a new market is emerging for solutions dedicated to AI systems security. These promise to offer businesses time-saving tools and access to expertise beyond their internal capabilities, ensuring compliance and enhancing security in the AI-driven landscape.

Wavestone’s AI Security Solutions Radar offers a visual panorama of market-leading cybersecurity solutions for AI. Our team analyzed the market through open source scouting and closed communities discussions and we directly interviewed vendors.

This hands-on approach allowed us to establish eight categories of cybersecurity offers:

1. AI Data Protection & Privacy – Keeping AI related data private and compliant
2. Ethics Explainability & Fairness – Making sure AI decisions are fair, transparent, and effective
3. AI Risk Management – Providing a complete overview and control of AI risks
4. Secure Chat/Large Language Models (LLM) Firewall – Keeping data and models confidential when used by others
5. Machine Learning Secure Collaboration – Adding security checks to protect Machine Learning models from attacks and prevent unexpected actions.
6. Machine Learning Detection & Response (MDLR) – Offering comprehensive protection, including detecting changes in models and data.
7. Anti-Deep Fake – Countering a growing societal concern with an increasingly negative business impact on companies.
8. Model Robustness & Vulnerability Assessment – Relying on AI to provide the assessment for a diversity of exploitable vulnerabilities.

Each category gathers solutions addressing comparable security needs and requiring similar technical specifications.

Many companies have decided to embrace the potential in the AI Security market. Many companies have decided to embrace the potential in the AI Security market with different market approaches.

We anticipate the AI security market to foster consolidation, enhanced customer maturity, and regulatory alignment.

• Increased funding and strategic partnerships among AI security firms and research institutions will support the development of sophisticated solutions.
• Customer maturity will drive AI security providers to tailor and enhance their solutions.
• Emerging legal standards, rooted in current regulations, will guide the market towards uniformity, offering clearer insights into its trajectory.

The imperative for both clients and providers is to prioritize the creation of dependable AI systems through targeted, case-specific initiatives – until the market becomes more defined.

• Today’s priority is trust. Amidst the rush to adopt AI, the paramount question is ensuring the reliability and integrity of these systems.
• The time is just right for AI security. While AI’s use for security may not yet be mature, securing AI use-cases and related infrastructures is critical now more than ever, as their business needs scales up.
• We recommend tailored investments on a project-by-project basis, rather than broad commitments to a single platform. Keep in mind that the AI security sector is expected to undergo rapid changes with new entrants, mergers, and collaborations.