AI security in 2025: How to defend your AI systems and strengthen cybersecurity

Artificial intelligence is no longer a future-forward experiment but is firmly embedded across enterprise ecosystems. From customer-facing chatbots to AI-assisted development pipelines, organizations are rapidly adopting generative AI (GenAI) and especially large language models (LLMs) to accelerate innovation. But with this adoption comes a rapidly expanding attack surface.

AI security demands attention on two fronts:

• Using AI to strengthen cybersecurity defenses through better vulnerability and anomaly detection, log triage, and predictive analytics.
• Protecting AI systems themselves from direct attacks and more sophisticated threats. This includes testing and protecting AI-based applications as well as AI models, APIs, training pipelines, and the data they rely on.

As AI adoption accelerates, enterprises must treat AI security not as an afterthought but as a foundational element of their security posture.

Why AI security matters now

The unique risks posed by AI systems start with “traditional” application vulnerabilities like SSRF being exposed via AI components, but they also go much further. Threats such as prompt injection, model manipulation, and data poisoning bypass many existing defenses and expose organizations to operational, financial, and regulatory risks. The unprecedented speed and scale of AI deployments drives an urgent need for security improvements in several ways:

• Growing attack surface: Integrating AI into IT systems introduces new pathways for attackers, fundamentally expanding enterprise risk exposure.
• Shadow AI and lack of visibility: Organizations commonly don’t have visibility into all the AI services that are running and exposed in their environments, creating blind spots and opportunities for attackers.
• Regulatory pressure: Frameworks like the EU AI Act and global data protection laws (GDPR, CCPA) are increasing compliance burdens, especially when AI mishandling leads to breaches or sensitive data exposure.

The new AI attack surface

In addition to serving as an additional vector and vehicle for many existing attack classes, AI introduces new risks that attackers are already exploiting:

• Prompt injection and model jailbreaking: Manipulating model inputs to trigger unauthorized actions or extract sensitive data.
• Data leakage and credential theft: Over 100,000 compromised chatbot credentials were reported on dark web marketplaces just between 2022 and 2023.
• Training data poisoning: Trojanized training datasets can bias or corrupt AI outputs, with dangerous consequences in regulated industries.
• Hallucination exploitation: Threat actors may exploit AI hallucinations by registering malicious domains, injecting misleading content into knowledge bases, or squatting on dependency names to serve up malicious code when these are hallucinated.
• Pipeline vulnerabilities: AI development pipelines extend beyond traditional SDLC boundaries, introducing new supply chain risks that require dedicated protections.

Using AI to enhance cyber defenses and application security

AI-driven tools are transforming how security teams detect and respond to threats. Behavioral analytics, anomaly detection, and predictive threat intelligence powered by AI can reduce false positives, automate incident response, and scale defensive coverage across hybrid and multi-cloud environments. For example, AI-assisted SOC teams could cut incident triage and reaction times, potentially allowing defenders to shift focus from repetitive tasks to high-value strategic analysis.

AI as a force multiplier for security testing

By embedding AI into dynamic application security testing workflows, security scanners can improve coverage, prioritize findings more intelligently, and reduce false positives through smarter validation. Accordingly, most vendors in the application security market are offering some type of AI-powered scanning, whether as the core scan technology or auxiliary features.

Invicti has taken a considered approach to AI-aided DAST by enhancing its proprietary, industry-leading scan engine with specific value-adding AI features to enhance scan accuracy, streamline triage, and accelerate remediation. These include smarter crawling, automated form filling, authentication, and more, giving customers a reliable way to keep pace with the growing scale and complexity of application and API environments. 

Extending existing security testing to new AI attack surfaces

Just as importantly, application security testing must evolve to address the risks introduced by AI-based applications. LLMs and AI agents create entirely new attack vectors that traditional approaches cannot cover. In its 2025 hype cycle report for AI and cybersecurity, Gartner predicts that more than half of successful attacks against AI agents through 2029 will exploit access control issues and prompt injection vectors. 

To close this gap, organizations need security testing that can actively probe AI-specific weaknesses in addition to traditional web and API vulnerabilities. Invicti’s DAST scanner extends proof-based scanning into the AI domain, supporting detection of LLMs and testing for high-impact vulnerabilities such as prompt injection, leakage, and LLM-mediated command injections, alongside traditional web and API issues. This allows security teams to identify and validate real, exploitable issues in AI-powered applications before attackers can take advantage of them.

The result is a security strategy where AI strengthens both sides of the equation: helping to secure AI-driven applications while also using AI to make the security scanners themselves more efficient and effective.

Top AI security challenges in 2025

Industry research highlights several persistent challenges:

• Skills gap: Over 30% of organizations cite a lack of AI security expertise as a major AI-related challenge.
• Reliance on legacy tools: Security tooling that can specifically address AI threats is still rare, with some surveys showing that AI-specific security tooling adoption remains limited, leaving most organizations struggling to adapt.
• Shadow AI: Organizations lack visibility into which AI models or services are active in their environments. This is especially important as ungoverned AI usage spreads across enterprises, with Gartner reporting that 79% of employees don’t follow organizational policy even when using enterprise-approved AI tools and 69% are at least suspected of using unsanctioned AI.

Even just these challenges illustrate why proactive AI security strategies must be a board-level priority.

Best practices for securing AI

Enterprises can mitigate AI security risks through a combination of frameworks, technical safeguards, and cultural shifts.

1. Adopt AI security frameworks: The NIST AI Risk Management Framework and OWASP Top 10 for LLMs provide practical starting points for governance and technical controls.
2. Test LLM security: Make LLM detection and vulnerability testing a permanent part of your application security program. 
3. Ensure tenant isolation: Multi-tenant AI systems must segregate models, data, and compute resources to prevent cross-contamination.
4. Sanitize inputs: Apply character limits, keyword filters, and validation rules to reduce the risk of prompt injection and data leakage.
5. Implement robust sandboxing: Test GenAI applications in realistic but isolated environments using adversarial prompts and edge cases.
6. Monitor AI pipelines continuously: Deploy real-time monitoring across training data, deployed models, and APIs to catch misconfigurations and vulnerabilities early.
7. Optimize prompt handling: Log and analyze user prompts, escalate suspicious activity for human review, and implement prompt preprocessing safeguards.
8. Balance innovation with compliance: Align AI deployment with regulatory requirements to avoid fines, breaches, and reputational damage.
9. Don’t neglect the basics: Enforce API authentication, encryption, and network hardening – AI-specific risks don’t eliminate the need for traditional cybersecurity hygiene.

Conclusion: Building trust through more secure AI

AI has the power to revolutionize enterprise productivity and resilience, but only if it is deployed securely. As new risks emerge, from poisoned datasets to cross-tenant exploits to vulnerabilities opened up through prompt injections, organizations must prioritize AI-specific security measures while reinforcing traditional defenses.

By adopting structured frameworks, strengthening visibility, and embedding AI security testing into DevSecOps and InfoSec processes, enterprises can ensure that innovation does not come at the cost of exposure. Ultimately, secure AI is not just a technical requirement: it’s a trust imperative.

Get a demo of LLM security testing on the Invicti Platform

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