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Generative AI (GenAI) Security Risks Assessment

Threats Posed by GenAI

GenAI has introduced significant risks and opportunities in the cybersecurity landscape:

Malicious Code and Exploits

  • Threat actors can use GenAI to write new malicious code or modify existing malware to evade detection more effectively. This leads to an increase in zero-day attacks, as GenAI enables the rapid creation of tailored malware1.

Identifying Vulnerabilities

  • GenAI can help threat actors analyze systems and software to identify vulnerabilities at a speed and scale previously unattainable. This allows for more sophisticated and targeted attacks1.

Detecting Targets

  • Hackers use GenAI for research to understand organizational structures and create target lists. GenAI helps them determine the best attack strategies, despite guardrails in tools like ChatGPT and Claude aimed at preventing such uses1.

Boosting Hacking Skills

  • GenAI can enable less skilled individuals to launch attacks, widening the scope of potential threats. This democratization of hacking skills increases the overall threat landscape1.

Risks in Enterprise Use of GenAI

  • The use of GenAI for coding can introduce vulnerabilities due to "AI hallucinations," where the AI generates incorrect or incomplete code that could end up in production if not reviewed by a skilled human. This can erode the foundational skills of programmers and create exploitable vulnerabilities1.

SaaS Browsing Threat Analysis 2025

Key Risks

  • GenAI Security Threats: Employees may unintentionally share sensitive information with GenAI tools, such as source code, customer PII, or financial data. This can lead to data leakage and other security breaches2.

  • Data Leakage Risks: Browsers can serve as attack vectors for exfiltrating internal files, emails, CRM data, and more. Employees may upload or paste sensitive information into external websites or SaaS apps, exacerbating these risks2.

  • SaaS Security Risks: Shadow SaaS applications can be exploited to exfiltrate data or infiltrate corporate networks. Potentially malicious SaaS apps pose significant threats to organizational security2.

  • Identity Vulnerabilities: Weak credential practices, such as password reuse, account sharing, and using personal passwords for work, can lead to identity fraud and account takeovers2.

  • Browsing Threats: Social engineering and phishing websites can extract sensitive credentials or internal documents. Malicious browser extensions can track user activity, steal credentials, and facilitate attacks2.

Risk Assessment and Mitigation

  • Organizations can use complimentary risk assessments to identify and analyze their risk profile across modern web and SaaS security. These assessments provide detailed and actionable insights to mitigate risks such as insecure GenAI use, data leakage, SaaS security gaps, identity vulnerabilities, and browsing threats2.

Identity Vulnerabilities in Cybersecurity

Identity & Access Management (IAM)

  • Strengthening IAM is a critical priority for cybersecurity leaders in 2025. This includes implementing zero-trust security models, risk-based authentication, and continuous identity verification to protect sensitive assets and reduce exposure to unauthorized access3.

Weak Credential Practices

  • Weak credential practices, such as password reuse, account sharing, and using personal passwords for work, are significant identity vulnerabilities. These practices can lead to identity fraud and account takeovers, highlighting the need for robust IAM policies2.

Deepfakes and Identity Verification

  • Deepfake technology is emerging as a threat, and organizations must invest in training and technology solutions to detect and mitigate deepfake-driven attacks. Establishing policies to verify suspicious activities and protect critical assets is essential3.

Mitigation Strategies

Operationalizing AI Security

  • Developing clear policies for secure AI deployment and assessing potential vulnerabilities are crucial steps. This includes establishing responsible AI governance to mitigate security and privacy risks while leveraging AI capabilities to enhance security operations3.

Secure Prompt Engineering

  • To safeguard generative AI applications, organizations should implement content moderation, secure prompt engineering, strong access controls, comprehensive monitoring, and regular testing. This defense-in-depth strategy helps protect against prompt injection attacks and other vulnerabilities5.

Vendor Security Management

  • Adopting a risk-based approach to vendor security assessments and establishing continuous monitoring processes can help address supply chain vulnerabilities. Collaboration with stakeholders is vital to ensure robust vendor security management practices3.

By understanding these risks and implementing the recommended mitigation strategies, organizations can better protect themselves against the evolving threats posed by GenAI, SaaS browsing, and identity vulnerabilities.