Key takeaways

• Generative AI accelerates both business innovation and cybercriminal tactics.
• Phishing, deepfakes, and AI-driven impersonation attacks are now mainstream threats, while exposed LLMs increase the attack surface.
• AI can strengthen defenses through automation, detection, and predictive analytics.
• Regulatory pressure is increasing, and leaders must act on AI governance now.
• Securing generative AI is a strategic, ethical, and reputational imperative, not just a technical task.
• Invicti extends security testing to cover AI risks with AI-aided DAST, LLM detection, and LLM vulnerability scanning.

The promise and peril of generative AI

Generative AI has quickly moved from a novelty to an essential driver of productivity. Enterprises now use it to accelerate software development, streamline operations, and generate business insights at scale. Yet the same technology also increases exposure to new and fast-moving risks.

Executives often face a difficult trade-off: prioritize speed-to-market and efficiency or step back to define and enforce controls that protect resilience. Too often, promises of business efficiencies take priority. The challenge for 2025 is finding a balance between innovation and security because, in practice, leaders cannot afford to ignore either side of that equation.

How generative AI has reshaped the cyber threat landscape

Generative AI is not only reshaping how businesses operate but also how attackers plan and execute their campaigns. Its ability to generate convincing content, mimic human behavior, and automate complex tasks has created new avenues for cybercrime while amplifying old ones. The following areas highlight where the impact is already being felt most.

Advanced phishing at scale

AI systems can craft highly convincing phishing messages in multiple languages, complete with contextually accurate details. Unlike earlier spam campaigns, these emails are nearly indistinguishable from genuine communication and can not only bypass traditional filters but also fool even experienced recipients.

Voice cloning adds another layer of risk. Attackers can impersonate executives or customer service agents in real time, creating situations where employees or customers may unknowingly hand over credentials or authorize payments.

Deepfake exploitation

Deepfake videos are no longer just entertainment curiosities. Criminals use them to manipulate reputations, amplify misinformation, and enable business email compromise attacks. In regulated industries such as finance and healthcare, a single deepfake incident can trigger both financial loss and compliance fallout. Governments and media organizations are also struggling to adapt to the challenge of fabricated but convincing video and audio evidence.

Expanded attack surfaces

Generative AI adoption often increases an organization’s attack surface, starting with the ubiquitous support chatbots. Sectors like healthcare, utilities, and transportation are particularly attractive targets because they manage sensitive data and critical infrastructure. At the same time, the AI systems themselves become new entry points. Compromising a generative AI pipeline can expose proprietary data, intellectual property, or sensitive customer information.

Expert cybersecurity insights: Turning AI into a defensive asset

The risks of generative AI are real, but the same technology can strengthen defense when applied responsibly. Security teams are already leveraging AI in several ways:

• Threat hunting and anomaly detection: AI models excel at identifying unusual patterns in network and system activity, often catching early signs of attacks that human analysts would miss.
• DDoS mitigation: AI-driven systems can analyze traffic in real time and automatically filter out malicious requests while keeping legitimate users online.
• Reducing human error: By automating repetitive, error-prone tasks, AI addresses one of the leading causes of breaches.
• Predictive threat modeling: Moving from reactive to proactive defense, AI helps organizations anticipate likely attack paths and prepare responses before incidents occur.
• Smarter security testing: AI-aided heuristics can enhance testing methodologies by improving coverage and efficiency while reducing false positives.

Real-world deployments show tangible gains. Security operations centers using AI-enhanced tools report faster triage, reduced false positives, and improved efficiency across teams.

Invicti spotlight: Securing the AI attack surface

The Invicti application security platform takes full advantage of generative AI benefits while also addressing the emerging attack surfaces:

• AI-aided DAST augments dynamic scanning with AI-powered analysis for more efficient and accurate results.
• LLM detection identifies where large language models are in use across applications and infrastructure.
• LLM vulnerability scanning uncovers risks specific to LLM implementations (like prompt injections) as well as “traditional” vulnerabilities that can be exploited via the new LLM avenues.

By combining proof-based scanning with AI-specific enhancements and security checks in its DAST, Invicti helps enterprises reduce both traditional and emerging risks in one platform.

Key challenges for security leaders

Under pressure from efficiency promises, security leaders face several hurdles when adopting and managing generative AI, including:

• Privacy and compliance risks: Generative AI models often require large datasets, which can expose organizations to violations of privacy laws or intellectual property misuse.
• Shadow AI: Employees adopting AI tools without approval or using permitted tools in unsanctioned ways create blind spots for security teams, leading to unmonitored data flows and potential leaks.
• Ethical and reputational risks: Unsupervised AI outputs may introduce bias, misinformation, or inappropriate content, undermining trust.
• Regulatory momentum: Frameworks such as the EU AI Act, NIST AI Risk Management Framework, and DHS guidelines are setting new compliance expectations that enterprises must meet.

Actionable strategies and best practices

To balance innovation with resilience, organizations should focus on a structured approach to AI governance and security. Recommended best practices:

• Establish AI governance programs aligned with industry standards and regulations.
• Make LLM security testing a permanent part of your application security program.
• Balance innovation with compliance by involving cross-functional stakeholders early on in AI deployments.
• Enhance visibility into shadow AI use across business units through discovery tools, education, and policy enforcement.
• Adopt continuous AI monitoring to flag anomalies in real time.
• Use red-teaming and sandboxing to test AI systems against adversarial attacks.
• Educate boards and executives on AI-related risks so that AI security becomes a board-level concern.

Ethical considerations and forward thinking

Generative AI is not only a technical challenge but also an ethical one. Trustworthy AI systems require transparency, fairness, and accountability. Enterprises that fail to address these concerns risk long-term damage to their brand and relationships.

Another growing issue is sustainability. Large-scale AI models consume significant energy, raising questions about the environmental cost of innovation. Leaders must weigh efficiency gains against these impacts.

Long-term resilience will depend on embedding security into AI from the start. A secure-by-design mindset ensures that as AI evolves, its foundations remain robust against manipulation and misuse.

Conclusion: Generative AI as both risk and opportunity

Generative AI is reshaping cybersecurity in 2025. It can accelerate both innovation and criminal tactics, creating new risks while offering powerful tools for defense. The mandate for the C-suite is clear: pursue growth but also maintain resilience.

AI-driven innovation must be implemented in a way that is secure, ethical, and trustworthy. The organizations that achieve this balance will gain a competitive edge by building not only faster but also safer, reaping the benefits without many of the risks.

For enterprises seeking to strengthen defenses across both traditional applications and emerging AI-driven systems, Invicti’s DAST-first application security platform unifies dynamic testing, API security, LLM scanning, and more under ASPM to help organizations align AI innovation with resilience. 

See how Invicti can help you minimize LLM security risks in your applications