Navigating the Intersection of AI and Cybersecurity: Challenges and Solutions for Organizations

Understanding the Security Risks of AI Integration

The Growing Divide: AI Adoption vs. Security Preparedness

The Dangers of Unsecured AI Deployments

Prioritizing Security in AI Development

The Role of Managed Service Providers (MSPs) in AI Security

Ensuring a Secure Future for AI Implementations

About Acronis Threat Research Unit (TRU)

The Crucial Intersection of AI and Cybersecurity

Organizations today are rapidly embracing artificial intelligence (AI) as a cornerstone of their operations. Whether aimed at enhancing internal efficiency or delivering innovative customer-facing solutions, AI—particularly generative AI—has the potential to revolutionize the business landscape. However, amidst this enthusiasm lies a critical oversight: the understanding of cybersecurity risks associated with AI deployment. Many organizations are unprepared to secure their AI frameworks, exposing themselves to potentially devastating vulnerabilities.

AI Adoption Outpaces Security Preparedness

The enthusiasm for AI adoption is palpable. Recent studies reveal that 92% of technology leaders anticipate an increase in AI spending by 2025, reflecting a growing consensus that AI is essential for competitive edge. Yet, the security aspect lags significantly. According to the World Economic Forum (WEF), while 66% of organizations believe AI will significantly impact cybersecurity in the near future, only 37% have measures in place to assess AI security before deployment. Alarmingly, 69% of smaller businesses lack safeguards to secure AI models, such as monitoring training data or inventorying AI assets.

Research from Accenture further corroborates this disparity, finding that 77% of organizations lack foundational data and AI security practices, with only 20% expressing confidence in their ability to secure generative AI models. In practice, this means many enterprises are adopting AI technologies without adequate assurance that their systems and data are protected.

The Dangers of Insecure AI Deployments

The absence of security in AI deployments creates multiple risks, both in terms of compliance and cyber vulnerability. Here are some pressing concerns:

1. AI-Driven Phishing and Fraud: A significant 47% of organizations identify AI-enabled cyberattacks as their primary concern. Worryingly, 42% reported experiencing social engineering attacks within the last year.

2. Model Manipulation: Threats like AI worms can embed harmful prompts in models, allowing cybercriminals to hijack AI systems, exfiltrate sensitive data, or disseminate spam.

3. Deepfake Scams: Criminals are leveraging AI-generated voices, images, and videos for fraud. One notorious incident involved a voice deepfake impersonating Italy’s defense minister to deceive business leaders into transferring funds abroad.

The ease with which AI lowers the barrier for attackers makes these threats faster, cheaper, and harder to detect.

Building Security into AI from the Start

Organizations must adopt a security-first mindset to safely harness AI’s full potential. Instead of retrofitting security measures post-incident, companies should incorporate natively integrated cybersecurity solutions from the onset. This proactive approach can help organizations:

• Embed Security into AI Development Pipelines: Standard practices such as secure coding, data encryption, and adversarial testing should be integrated at every development phase.

• Continuously Monitor and Validate Models: Regular testing for manipulation and data poisoning is essential to safeguard AI systems.

• Unify Cyber Resilience Strategies: A comprehensive approach ensures that security is integrated across all operational domains—endpoints, networks, cloud environments, and AI workloads.

Both WEF and Accenture indicate that organizations with integrated security strategies are better positioned to thrive in the AI era. Notably, only 10% of companies are in what Accenture terms the “Reinvention-Ready Zone,” which combines mature cyber strategies with integrated monitoring and response capabilities. Firms in this category are 69% less likely to experience AI-powered cyberattacks.

The Role of MSPs and Enterprises

For managed service providers (MSPs), the rise of AI presents both opportunities and challenges. Clients increasingly expect AI-powered tools while also placing their security in the hands of their MSPs. According to the Acronis Cyberthreats Report H1 2025, over half of all attacks on MSPs during this period stemmed from phishing attempts, many of which are AI-driven.

To stay competitive, MSPs must deliver integrated protection spanning cloud, endpoint, and AI environments, safeguarding both their operations and those of their clients.

Conversely, enterprises must find a balance between ambition and caution. While AI can enhance operational efficiency, creativity, and competitiveness, it must be deployed responsibly. Making AI security a board-level priority, establishing robust governance frameworks, and ensuring the cybersecurity team’s training on emerging threats are essential tasks moving forward.

The Future of AI Deployments is Tied to Security

Generative AI is here to stay and will increasingly become embedded in business processes. However, hastily implementing these systems without robust security measures is akin to constructing a skyscraper on unstable ground.

By adopting integrated, proactive security measures, organizations can unlock AI’s transformative potential without exacerbating their vulnerability to threats such as ransomware and fraud.

About TRU

The Acronis Threat Research Unit (TRU) consists of cybersecurity experts focused on threat intelligence, AI, and risk management. They research emerging threats, provide valuable security insights, and support IT teams through guidelines, incident response assistance, and educational workshops.

Explore the latest TRU research to stay ahead in the AI and cybersecurity landscape. Sponsored and written by Acronis.