Gartner Highlights AI, Deepfakes, and Supply Chain Attacks as Top Cybersecurity Threats for 2026-2027

Executive Summary

At its Security & Risk Management Summit, the influential research firm Gartner presented its 2026-2027 ThreatScape, identifying four critical areas of cybersecurity where threat actors currently possess a significant advantage over defenders. These areas, requiring urgent attention from CISOs and security leaders, are: AI application compromise, identity impersonation using deepfakes, software supply chain attacks, and prompt injection. Gartner warns that the rapid adoption of AI is creating new, poorly understood attack surfaces and that traditional security controls are often inadequate to address these evolving threats. The report calls for a strategic shift towards adaptive security, better governance of AI, and a focus on hardening the entire software development lifecycle.

Regulatory Details

While this is a trend report and not a regulation, the insights from Gartner heavily influence enterprise security strategy and spending, effectively setting a de facto standard for due diligence.

The Four Critical Threats

1. AI Application Compromise: As enterprises rush to integrate AI, they are deploying custom-built agents and third-party AI tools, often with weak security controls. Gartner warns that these applications are becoming prime targets. Attackers can exploit them to access sensitive training data, poison models to produce malicious outputs, or use the AI's privileged access to pivot into other corporate systems.

2. Identity Impersonation with Deepfakes: The quality and accessibility of deepfake technology have reached a point where it can be used to bypass biometric authentication (e.g., liveness checks), create convincing CEO fraud scams, and subvert identity verification in hiring processes. This threat undermines trust and traditional identity-based security controls.

3. Software Supply Chain Attacks: This remains a top threat, and Gartner predicts that the use of Generative AI in software development will exacerbate the problem. AI tools that suggest or automatically incorporate open-source components can introduce vulnerabilities at an unprecedented scale and speed. Attackers will continue to target CI/CD pipelines, open-source repositories, and code dependencies.

4. Prompt Injection: This attack targets the language interface of AI models. Attackers can craft malicious inputs (prompts) that cause the AI to ignore its original instructions and perform unintended actions. This could range from revealing sensitive system information to executing malicious code, effectively turning the AI into an insider threat.

Affected Organizations

According to Gartner, these threats are universal and affect all organizations, regardless of size or industry. However, early adopters of AI and organizations with complex software supply chains are at the most immediate risk.

Compliance Requirements

To address these threats, Gartner outlines several strategic requirements for security leaders:

• AI Governance: Establish a formal governance framework for the use of AI. This includes creating an inventory of all sanctioned and unsanctioned AI tools, defining acceptable use policies, and assigning clear ownership for AI security.
• Attack Surface Management: Security teams must actively map and manage the new attack surfaces created by AI applications. This includes threat modeling AI systems and integrating security into the MLOps (Machine Learning Operations) lifecycle.
• Supply Chain Hardening: Organizations must build and maintain trusted component registries, implement Software Bill of Materials (SBOM) for all applications, and harden their CI/CD pipelines against compromise.
• Identity and Access Management (IAM) Evolution: IAM programs must evolve to manage the identity and access of non-human actors, including AI agents and bots, treating them with the same rigor as human user accounts.

Impact Assessment

The primary impact of these trends is that they render many traditional, signature-based, and perimeter-focused security controls less effective. Organizations that fail to adapt will face:

• Increased risk of sophisticated social engineering and fraud.
• Breaches originating from poorly secured AI applications.
• Compromise through vulnerable software dependencies introduced by AI-powered development tools.
• Loss of control over AI models, leading to data leakage or malicious actions.

Compliance Guidance

Gartner's recommendations provide a tactical roadmap for CISOs:

1. Prioritize Visibility: You cannot protect what you cannot see. The first step is to use tools and processes to discover all instances of AI and open-source software being used across the enterprise.
2. Foster Cross-Functional Collaboration: AI and supply chain security are not just IT problems. Security leaders must work closely with legal, procurement, and development teams to establish a holistic governance strategy.
3. Invest in Adaptive Technologies: Shift budget towards technologies that offer adaptive, behavior-based detection, such as advanced EDR, User and Entity Behavior Analytics (UEBA), and SaaS Security Posture Management (SSPM).
4. Train for the New Threats: Update employee security awareness training to include modules on deepfake detection, prompt injection risks, and secure coding practices for AI development.