In 2026, artificial intelligence has become an integral part of business processes, particularly in the financial sector. The rapid deployment of this technology in critical infrastructure demands stronger cybersecurity, as AI services increasingly become targets for cybercriminals.
Key threats: prompt injection and data leaks
One of the greatest risks is prompt injection—manipulating an AI model's behavior through malicious instructions in queries. This can lead to the disclosure of confidential information, bypassing of security restrictions, or the generation of harmful content. Furthermore, according to the ENISA Threat Landscape 2025 report, digital infrastructure accounted for about 27.7% of data breaches. In the context of AI, leaks occur due to improper access management, infrastructure vulnerabilities, or through the model responses themselves.
AI security platforms as a new standard of protection
To counter these threats, companies are implementing AI security platforms—integrated solutions for monitoring and protecting models throughout their entire lifecycle. According to Gartner forecasts, by 2028, more than 50% of enterprises will use such platforms. They provide prompt sanitization, model behavior monitoring, access management, and data loss prevention.
A common mistake is protecting only the AI model itself. Effective security requires a comprehensive approach across three levels:
- Data level: masking, anonymization, and encryption of information.
- Model level: filtering incoming queries and analyzing responses for anomalies.
- Infrastructure level: API protection, cloud environment security, and logging.
Integration into the overall security system
AI security should not exist in isolation. Specialized platforms must integrate with existing enterprise SIEM, DLP, and IAM systems. According to the Cisco Cybersecurity Readiness Index 2025, AI Fortification is one of the key pillars of cyber readiness. To build a resilient defense system, organizations are recommended to implement a Zero Trust architecture, conduct regular penetration testing, and follow standards such as NIST AI RMF 1.0.
Market implications
For businesses and the financial sector, the vulnerability of AI systems means that traditional cybersecurity perimeter defenses are no longer sufficient. Failure to secure AI models risks severe data breaches, loss of intellectual property, and regulatory penalties, making AI security a critical factor for operational continuity and customer trust.
Where to start
To protect enterprise AI systems, organizations should take the following practical steps:
- Deploy integrated AI security platforms to monitor models and sanitize prompts.
- Secure the system across three levels: data (encryption), model (query filtering), and infrastructure (API protection).
- Integrate AI defense tools with existing SIEM, DLP, and IAM systems.
- Implement a Zero Trust architecture, conduct regular penetration testing, and align with the NIST AI RMF 1.0 standard.
Prepared by a Software Ukraine member. Original publication.