Global rise in generative AI attacks
Generative artificial intelligence is transforming how organizations work—boosting productivity and enabling new services. It is also enlarging the attack surface for cyber threats. Gartner’s latest findings indicate that attacks centered on generative AI are growing worldwide, and threatening actors are expanding their techniques across all modalities. As AI is integrated deeper into business processes, the potential payoff for attackers increases, making defensive strategy more complex and urgent.
In the latest Gartner survey, roughly 62% of organizations reported experiencing at least one incident related to generative AI in the past year. The attacks span a spectrum from short, AI-assisted fraud attempts to long-running campaigns that manipulate data, subvert governance controls, or disrupt critical operations. The result is a new kind of risk that blends traditional cybersecurity with AI risk management, calling for a holistic approach to security, governance, and resilience.
How attackers leverage generative AI
Adversaries are exploiting generative AI in several ways. Phishing messages and business email compromise become more persuasive when AI-generated text, voices, and even images mimic real colleagues or customers. AI-assisted malware can tailor payloads to specific targets, increasing the likelihood of successful intrusions. Synthetic data and content are used to bypass basic validation checks, seed misinformation campaigns, and manipulate decision-making processes within organizations.
Prompt injection and model manipulation are growing concerns. Attackers attempt to coax AI systems to reveal sensitive data, circumvent safety rules, or extract credentials by framing requests in a way that exploits the model’s behavior. In cloud and enterprise environments, AI-driven automation can accelerate reconnaissance, vulnerability discovery, and lateral movement, pressing security teams to keep pace with rapid tooling and evolving tactics.
Sector and regional impact
While no industry is immune, sectors handling sensitive data—finance, healthcare, and government services—face heightened risk because the potential consequences of AI-enabled attacks are severe. Regions with high digital adoption and faster AI deployment report levers of risk that require both technical safeguards and policy-level responses. The global nature of these threats means coordination among vendors, customers, and regulators will become a defining feature of cybersecurity planning in the near term.
Defensive strategies for a generative AI threat landscape
To address this evolving threat, organizations should pursue a layered, risk-based strategy that combines people, process, and technology. Strong governance is foundational: inventory all AI-enabled tools, classify data by sensitivity, and enforce strict access controls and model risk management. Establish auditable policies for AI use, including how data is collected, stored, and processed by AI systems.
On the technical side, security teams should integrate AI-aware monitoring into existing defenses. This includes enhanced email security, anti-phishing controls, and anomaly detection that can identify AI-generated content, synthetic data, or unusual model interactions. Data protection measures—encryption, tokenization, and strict data loss prevention (DLP)—remain essential, but must be paired with model governance and endpoint protections to cover the AI-specific kill chain.
Incident response and resilience are increasingly critical. Develop and exercise playbooks that consider AI-driven attack vectors, such as rapid credential harvesting, synthetic content campaigns, or manipulated dashboards. Regular red-teaming, threat-hunting, and tabletop simulations help teams validate controls against AI-enabled tactics and ensure rapid containment when incidents occur.
What Gartner advises for organizations and vendors
Gartner emphasizes that mitigating generative AI risk requires cross-disciplinary collaboration across security, risk management, data science, and business leadership. Organizations should adopt a comprehensive AI risk management framework that mirrors traditional cybersecurity programs but accounts for AI-specific threats, including data governance, model risk, and supply chain integrity. Vendors and customers alike should demand transparency in AI tooling, including clear disclosures about data flows, model capabilities, and safety controls.
As AI tools become more pervasive, the market will increasingly reward solutions that blend governance with practical protection. Investments in AI-aware security operations, intelligent governance dashboards, and trustworthy AI design practices will be differentiators for companies seeking to reduce exposure to AI-enabled attacks while maintaining innovation velocity.
