4 threats of Generative AI and its mitigations 

Generative AI (GenAI) creates new content from training data, but attackers can misuse it. The four main GenAI threats are phishing, deepfakes, malware creation, and data manipulation. Each can be mitigated with a mix of encryption, strong access controls, user awareness, detection tools, and alignment to frameworks like the NIST AI Risk Management Framework.

Generative AI (GenAI) generates new text, images, audio, and video from large training datasets, and while it is a powerful tool, attackers can weaponize it. The four main GenAI threats are phishing, deepfakes, malware creation, and data manipulation. Each is mitigated by combining encryption, access controls, detection tools, user awareness, and structured frameworks such as NIST’s.

Key Takeaways

• Generative AI creates new content from training data; the same power that helps businesses can be misused by attackers.
• The four main GenAI threats are phishing (more convincing lures), deepfakes (synthetic audio and video), malware creation (AI-generated, evasive code), and data manipulation (poisoning, bias, fake content).
• Real incidents show the stakes, from AI-assisted business email compromise to deepfake voice fraud and AI-generated malware scripts.
• NIST frames GenAI risk around Integrity, Availability, and Privacy, and offers guidance in SP 800-53, SP 800-37, and the AI Risk Management Framework (AI 600-1).
• Core mitigations include strong encryption, access controls (MFA, RBAC), input validation, data minimization, monitoring, code signing, and user education.

What Is Generative AI, and Why Is It a Security Concern?

Generative AI, or GenAI, is a subset of artificial intelligence that generates new content, text, images, audio, and video, from a dataset of prior examples. It uses sophisticated algorithms and neural networks, trained on very large datasets (sometimes approaching the scale of the entire internet), to replicate human creativity. Because those models are so large, organizations often rely on third parties to provide and run them, and integrating third-party APIs adds architectural complexity around protocols, error handling, and data flow.

That same power, handling vast data, answering diverse queries, and continually learning, is what attackers exploit. GenAI lets threat actors produce realistic text, graphics, and audio, sharpening their attack vectors, helping them evade detection, and making it harder to tell authentic content from fabricated. The four threats below are where this shows up most.

The 4 Main Threats of Generative AI

Threat	How GenAI is misused	Core mitigations
Phishing	Crafts convincing, personalized fake emails and messages at scale	User awareness, email verification, MFA, reporting tools
Deepfakes	Generates realistic fake audio and video to impersonate people	Detection tools, biometric and multi-channel verification, encryption
Malware creation	Writes and mutates evasive, polymorphic malicious code	Patching, least privilege, EDR, behavioral detection, segmentation
Data manipulation	Poisons training data, injects bias, mass-produces fake content	Monitoring, data integrity checks, access controls, backups

1. Phishing

Phishing is a social engineering attack in which fraudulent emails, messages, or calls are made to look legitimate. GenAI makes phishing far more convincing: it can capture the tone and content of real emails, removing the clumsy grammar that once gave scams away. Successful phishing can expose account details and personal data, leading to identity theft, financial loss, or a foothold into a company’s network from which attackers exfiltrate data or deploy ransomware. Targeted variants include spear phishing (aimed at specific individuals using personal details), whaling (aimed at senior executives), and pharming (redirecting users to fraudulent sites via DNS manipulation).

A well-documented example of business email compromise: between 2013 and 2015, Evaldas Rimasauskas impersonated a trusted supplier and sent fake invoices, tricking Google and Facebook into paying out more than 100 million dollars. GenAI makes such deception easier to produce at scale.

Mitigation

Be alert to urgency and threats (‘your account will be closed’), generic greetings like ‘Dear Customer’, unknown senders, and any request for passwords or bank details (legitimate organizations do not ask this way). Do not click links or open attachments in suspicious messages; verify through a separate channel, report to IT or via your email’s phishing report option, and delete. If you did interact, change passwords immediately and monitor the affected accounts. Strong access controls and MFA limit the damage of a stolen credential.

2. Deepfakes

A deepfake is synthetic media, typically video, image, or audio, created with GenAI (often using generative adversarial networks, or GANs) to replicate a person’s face, voice, and speech patterns with high fidelity. Deepfakes power social engineering, fraud, and spear phishing, and can bypass traditional verification. They are increasingly used to trick people into authorizing transfers or downloading malware during what looks like a legitimate interaction. In one 2019 case, a deepfake voice mimicking a chief executive was used to authorize a fraudulent transfer of roughly 243,000 dollars; larger deepfake-enabled transfer frauds have followed since.

Mitigation

Watch for tell-tale signs: odd facial shadows, unnatural skin tones, irregular blinking, blurry edges, and, in audio, mismatched lip-sync or a robotic quality. Deploy AI-powered detection tools; use encrypted communication platforms and secure storage for media and voice data; require biometric or multi-channel verification for sensitive requests; apply digital watermarking to authenticate proprietary media; monitor for misuse of company media; educate employees; and have a crisis plan for deepfake incidents.

3. Malware Creation

Malware is any software designed to harm, exploit, or disrupt systems. GenAI lowers the technical bar to create it, letting even inexperienced actors produce malicious code, and can automate sophisticated, polymorphic malware that constantly changes its structure to evade detection. AI-assisted malware can use adversarial techniques to slip past endpoint detection and response (EDR), firewalls, and antivirus, and can help craft ransomware or exploit scripts. Common malware types include viruses, worms, Trojans, ransomware, spyware, adware, rootkits, keyloggers, botnets, and fileless malware.

Supply-chain incidents show the potential damage. The Kaseya attack (July 2021) saw the REvil group exploit a flaw in Kaseya’s VSA software to push ransomware to around 1,500 businesses and demand 70 million dollars. The SolarWinds attack (2020) inserted malicious code into Orion software updates, affecting roughly 18,000 organizations including US government agencies. AI accelerates the creation of exactly this kind of threat.

Mitigation

Use a multi-layered defense: patch software and operating systems promptly; apply least privilege; use behavioral detection to catch malware that evades signatures; test and audit with simulated attacks; run continuous endpoint monitoring with EDR; use application allow-listing; segment networks to contain spread; adopt cloud-based security; keep automated, tested backups for recovery; and educate users, since human error is a primary malware vector.

4. Data Manipulation

GenAI can be used to distort data and perception. Models can inherit bias from training data, producing skewed or discriminatory outcomes, and can be turned to producing convincing fake reviews, testimonials, and other content that manipulates opinion or damages a brand. A specific technical risk is data poisoning, where attackers inject malicious data into a model’s training set so it produces incorrect or harmful outputs. Large language models also raise privacy concerns, since their training data, often scraped from the web, can include personal information (PII) gathered without consent.

Historic examples include Microsoft’s Tay chatbot, which users manipulated into posting offensive content within hours of launch, and the 2020 Twitter Bitcoin scam, where attackers hijacked high-profile accounts to promote a cryptocurrency fraud, damaging trust in the platform’s integrity and account security.

Mitigation

Watch for unusual activity, such as unexpected data changes or spikes in access to sensitive information, and review logs for unauthorized edits. Cross-check data against trusted sources and analyze metadata for signs of tampering. If manipulation is found, isolate affected systems, investigate the source and scope, notify stakeholders, restore from verified clean backups, run a full security audit to close the exploited gap, and strengthen monitoring and authentication. Review legal and regulatory obligations throughout.

NIST Guidance on GenAI Risk

NIST frames threats to GenAI systems around three principles, and offers structured guidance for managing them:

• Integrity: Ensuring data and systems stay accurate and trustworthy. GenAI integrity risks include data manipulation, deepfakes and fake news, model tampering, and data poisoning (injecting harmful data into training so the model produces wrong outputs).
• Availability: Ensuring services and data are accessible when needed. Risks include denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks that disrupt or halt AI-dependent operations. Securing AI models and their databases keeps critical tasks resilient.
• Privacy: Protecting personal information (PII) and meeting laws like GDPR and CCPA. GenAI privacy risks include data leakage, where sensitive data is unintentionally exposed, and prompt or system-context extraction, where models inadvertently surface confidential data embedded in prompts or prior interactions.

NIST also publishes guidance organizations can apply directly:

• NIST SP 800-53 (Security and Privacy Controls) provides a structured control set across families such as access control, system and communications protection, and incident response.
• NIST SP 800-37 (Risk Management Framework) guides assessing, implementing, and continuously monitoring security controls across a system’s lifecycle.
• NIST AI 600-1 (the Generative AI Profile of the AI Risk Management Framework) addresses GenAI-specific risks, including data privacy, model bias, transparency, accountability, and ethics.

Top Strategies to Protect Against GenAI Threats

• Strong input validation: Check incoming data so malicious or malformed inputs cannot damage your system.
• Encryption: Encrypt sensitive data so it stays protected even if attackers gain access; only holders of the decryption key can read it.
• Protect credentials: Store secrets in secure vaults, rotate keys regularly, and enforce access controls over API keys and tokens.
• Redundancy and failover: Keep backup systems so services stay available even if one component is compromised.
• Stress testing: Simulate abnormal traffic and worst-case load to find architectural weaknesses before attackers do.
• Code signing with privacy audits: Digitally sign software to verify its authenticity and integrity, and run regular privacy audits over user data.
• DDoS mitigation: Use firewalls and traffic-analysis tools to catch abnormal patterns and keep services available.
• Data minimization: Collect and retain only necessary data, reducing exposure in a breach.
• Strong access controls: Enforce multi-factor authentication (MFA) and role-based access control (RBAC) so only authorized users reach sensitive data.

How Encryption Consulting Helps

As GenAI-driven threats evolve, Encryption Consulting’s Encryption Advisory Services help organizations respond. Through data discovery, we locate structured and unstructured sensitive data and provide strategies to secure it. Our risk assessments identify vulnerabilities and offer tailored mitigations, and we build data protection frameworks, governance, and monitoring to strengthen security and support compliance. We also help integrate the encryption, access control, and key management that underpin defense against AI-driven attacks. Backed by ISO/IEC 27001:2022 and SOC 2 certified practices.

Frequently Asked Questions

What are the main threats of generative AI?

The four main threats are phishing, deepfakes, malware creation, and data manipulation. GenAI makes phishing messages more convincing, generates realistic deepfake audio and video for fraud and impersonation, helps create evasive malware even for low-skilled attackers, and enables data manipulation such as poisoning training data or mass-producing fake content. Each threat has established mitigations, and frameworks like the NIST AI Risk Management Framework help organizations manage them systematically.

How does generative AI make phishing more dangerous?

Generative AI removes the clumsy grammar and generic wording that used to give phishing away. It can mimic the tone and style of legitimate emails and personalize messages at scale using scraped details, making spear phishing and business email compromise far more convincing. This raises the success rate of attacks that steal credentials or trick finance teams into fraudulent payments. Mitigations include user awareness, verifying requests through separate channels, MFA, and phishing-reporting tools.

What is a deepfake and how do you defend against it?

A deepfake is synthetic audio, video, or imagery created with generative AI to convincingly impersonate a real person, often used in fraud and social engineering, including voice-based transfer scams. Defenses include AI-powered detection tools, watching for visual and audio artifacts (odd blinking, blurry edges, mismatched lip-sync), requiring biometric or multi-channel verification for sensitive requests, using encrypted communications, watermarking proprietary media, and training employees to recognize and report suspected deepfakes.

Can generative AI create malware?

Yes. Generative AI can lower the skill needed to write malicious code and can automate the creation of polymorphic malware that constantly changes to evade signature-based detection. AI-assisted malware may use adversarial techniques to bypass EDR, firewalls, and antivirus, and can help craft ransomware or exploit scripts. Defending against it requires layered controls: prompt patching, least privilege, behavioral detection, EDR, network segmentation, tested backups, and user education.

What does NIST recommend for generative AI security?

NIST frames GenAI threats around three principles, Integrity, Availability, and Privacy, and offers structured guidance. SP 800-53 provides security and privacy controls, SP 800-37 defines a risk management framework for assessing and monitoring controls, and NIST AI 600-1 (the Generative AI Profile of the AI Risk Management Framework) addresses GenAI-specific concerns like data privacy, model bias, transparency, accountability, and ethics. Together they give organizations a systematic way to manage AI risk.

How can organizations protect themselves from generative AI threats?

Key protections include strong input validation, encrypting sensitive data, safeguarding credentials in secure vaults with key rotation, building redundancy and failover, stress testing against attacks, using code signing with privacy audits, mitigating DDoS with firewalls and traffic analysis, minimizing collected data, and enforcing strong access controls like MFA and RBAC. Combined with user education and alignment to NIST guidance, these measures address the main GenAI threats without sacrificing productivity.

Secure Your Organization Against AI-Driven Threats

Generative AI raises the stakes for security, privacy, and trust, but the right encryption, access controls, and monitoring keep your organization resilient. Explore Encryption Consulting’s Encryption Advisory Services to assess your exposure and build defenses against AI-driven threats.