Generative Exploits and Social Engineering
AI-assisted phishing and business email compromise are becoming more convincing, scalable, and multilingual. Generative models can mimic tone, fix grammar, and adapt lures to roles, which may raise click-through rates compared with traditional phish. Voice cloning and chatbots can also extend attacks across phone, chat, and social channels, blurring channel-based defenses. While not perfect, these tools often lower attacker effort and increase campaign iteration speed.
AI lowers the effort and raises the believability of social engineering at scale.
Automated Reconnaissance and Evasion
Large language models and automation frameworks can reasonably sift open-source data to build target profiles and likely credential patterns. Code-generation tools may help less skilled actors assemble evasive loaders, polymorphic scripts, and infrastructure-as-code for rapid deployment. AI can also aid in discovering exposed secrets or misconfigurations by correlating clues across public repos and package registries. Defensive controls still matter, but detection may need more behavior-based and anomaly-aware methods to keep pace.
AI speeds recon and helps attackers iterate on evasive payloads and infrastructure.
Model Attacks and Data Poisoning Risks
As organizations adopt AI, the systems themselves may become targets through prompt injection, jailbreaks, and model hijacking. Training pipelines could be influenced by data poisoning that nudges outputs toward attacker goals without obvious anomalies. Model stealing and membership inference can leak proprietary behavior or sensitive training data under certain conditions. These risks vary by model type and deployment pattern, but secured pipelines, red teaming, and monitoring can materially lower exposure.
The AI stack—data, model, and pipeline—introduces new attack surfaces that need explicit protection.
Deepfakes, Fraud, and Influence Ops
Synthetic media tools are already capable of producing persuasive but imperfect images, audio, and video. Financial fraud scenarios may involve real-time voice cloning in call centers or account recovery flows, especially where knowledge-based authentication persists. Influence operations can combine bots, LLM-written narratives, and tailored microcontent to seed and amplify misleading stories. Detection remains an arms race, so layered controls and human-in-the-loop review become increasingly valuable.
Synthetic media and LLM-driven content can escalate fraud and influence operations across multiple channels.
Putting This Knowledge to Work
Security teams can adapt by strengthening identity assurance, adding phishing-resistant MFA, and limiting high-risk actions through just-in-time approvals. Content and model safety reviews, including AI red teaming and prompt-injection defenses, can be integrated into SDLC and MLOps gates. Data loss prevention, egress filtering, and robust audit trails help contain misuse of internal models and sensitive prompts. Investing in user training updated for AI-era threats, plus continuous monitoring, will likely offer compounding benefits.
Combine stronger identity, safeguarded AI pipelines, and continuous monitoring to meaningfully reduce AI-enabled risk.
Helpful Links
NIST AI Risk Management Framework (AI RMF): https://www.nist.gov/itl/ai-risk-management-framework
OWASP Top 10 for LLM Applications: https://owasp.org/www-project-top-10-for-llm-applications/
MITRE ATLAS (Adversarial Threat Landscape for AI Systems): https://atlas.mitre.org/
ENISA report on AI cyber risks: https://www.enisa.europa.eu/topics/csirt-cert-services/ai-cybersecurity
Microsoft’s AI red team guidance: https://learn.microsoft.com/security/ai/red-team-operations-for-ai-systems