Generative artificial intelligence (Gen AI) is a growing risk that’s making leaders (and their budget teams) take notice, with nearly ¾ of cybersecurity leaders planning to up their spend on security to combat the threats posed by Gen AI.

Capable of producing highly realistic content, these AI systems pose significant risks, including the creation of deepfakes, malicious code, and tools for advanced social engineering attacks. Gen AI’s ability to generate such content makes it a powerful tool for malicious actors, but it’s also highlighting that bigger budgets aren’t the only answer — the novel attack surfaces and approaches emerging from Gen AI are going to require a re-engineering of security strategy from A to Z.

Adaptive security is key: the kind of security that provides dynamic protection that can address evolving threats such as data privacy violations and the spread of AI-generated misinformation. 

We’ve covered the basics of Gen AI security. This guide explores how adaptive security enables organizations to implement actionable strategies for detecting and mitigating unique risks associated with generative AI.

Why Generative AI Risks Matter

What are the problems with Gen AI? Here are top risks:

• Data leakage
• Model hallucinations
• Prompt injection
• Data poisoning
• Malicious content generation
• Shadow AI use
• Compliance violations

Gen AI comes with its share of risks because it goes beyond predictive and pattern-finding machine learning: it produces its own content.

Gartner recently surveyed 249 senior enterprise risk executives and found that generative AI has become an emerging risk for enterprises, second only to third-party viability. Their concerns are well-founded — generative AI introduces significant risks from intellectual property leakage to employees pasting sensitive code into public tools, model hallucinations that generate false financial statements or legal advice, and data poisoning that undermines large language models (LLMs) altogether.

Unmanaged, these risks can lead to compliance violations (like GDPR or HIPAA), reputation damage, or service disruptions.

“None of your security tooling can be static—you have to have the concept of anomaly detection, or you’re always fighting the last threat.”

-Joshua Burgin, CPO, Upwind

And as reliance on AI grows, ethical and legal challenges like bias, privacy violations, and intellectual property issues also grow. Ensuring compliance with evolving regulations and managing legal risks is also essential for organizations using generative AI; firms should therefore proactively address these challenges to leverage the technologies responsibly and securely.

The Rise of Cloud-Based AI

The growing adoption of cloud-based generative AI services, via public APIs, integrated copilots, and external LLMs, is multiplying the threat surface for attackers to exploit. These tools have the ability to reach inside organizations through unsanctioned SaaS integrations, browser plugins, and embedded assistants. And that means cloud-based Gen AI can launch attacks easier than ever before, like:

• Mass-scale phishing using personalized emails generated in seconds
• Real-time social engineering through AI chatbots or impersonation
• Code generation attacks, where LLMs produce or inject insecure scripts into CI/CD pipelines
• Content pollution or misinformation that slips past traditional filters due to its human-like quality.

Because these models are accessed via cloud APIs and embedded in SaaS products, traditional perimeter and workload controls often don’t see the activity. So fundamentally, Gen AI’s reality plus the dominance of the cloud mean adapting to attacks must include expanding visibility, integrations, and real-time behavior.

Gen AI Security Risks in Depth

Generative AI introduces distinct security risks. Here’s how they work and why existing controls often fail to catch them.

AI Risk	Description	Consequences	Mitigation Measures
Data Leakage	AI models may unintentionally output sensitive or private information memorized during training.	Exposure of trade secrets, private user data, or regulated content.	Limit training data exposure, use differential privacy, and monitor outputs for leakage.
Deepfakes, Phishing & Malicious Code	Generative AI is used to create convincing deepfakes, realistic phishing content, and even generate or modify malware.	Identity theft, fraud, reputation damage, and more effective cyberattacks.	Develop detection tools, educate users, and monitor AI misuse.
Misinformation & Hallucinations	AI can generate believable yet false information (misinformation) or fabricate nonsensical content (hallucinations).	Public confusion, political manipulation, loss of trust, and spread of disinformation.	Strengthen content verification, train models with vetted data, and use human-in-the-loop moderation.
Adversarial Prompts	Attackers exploit prompt injection techniques to manipulate LLM behavior in unintended ways.	Model misuse, data leaks, or triggering of harmful outputs.	Implement prompt filtering, apply continuous red-teaming, and monitor model interactions.
IP & Compliance Issues	Legal and regulatory uncertainty around training data, content ownership, and compliance with evolving laws.	Copyright infringement, legal liability, regulatory penalties.	Use licensed datasets, track model provenance, and consult legal teams regularly.
Bias & Auditability	Models can reflect and amplify biases in training data; lack of auditing can make systems opaque and vulnerable.	Discrimination, reputational damage, regulatory non-compliance.	Enforce regular audits, track inputs/outputs, monitor for bias, and engage third-party assessments.

Traditional security tools weren’t built to detect or respond to the speed, scale, and versatility that they meet head-on in Gen AI. These risks don’t follow predictable patterns or known signatures. Instead, they exploit logic, identity, and context. “Being adaptive” means shifting from static rules and one-time scans to real-time, behavior-aware, and policy-enforced systems that can, like Gen AI, learn and evolve with usage.

How to Go Beyond Traditional Defenses and Adapt to Gen AI

In practice, adaptive Gen AI security means:

Monitoring Outputs as well as Inputs

Tools must detect hallucinated and manipulated responses and not stop at blocking bad queries. Traditional DLP and API firewalls are all about input, so sensitive data can’t get into resources and assets. But GEn AI misuse increasingly happens on the output side. 

Use content inspection tools that can scan Gen AI outputs for fake legal advice, exposed secrets, etc. Integrate LLM response filtering with internal use policies (banning outputs that mimic customer data structures). And implement approval workflows for Gen AI content used in customer-facing capacities.

Runtime-Aware Context

Posture tools need to account for how and where Gen AI tools are used, as well as whether or not they’re present. That means that runtime tools should show which users, endpoints, or services are invoking Gen AI tools, how often, and why.

What can you do? Deploy eBPF sensors for runtime insights. Track Gen AI calls at the process level and correlate them with identity and data access paths. And enforce guardrails based on live use.

Dynamic Identity Controls

Teams need tools that can enforce access policies based on behavior, intent, and real-time context. They should be able to block an LLM from accessing sensitive internal data, for instance. Static RBAC fails when users interact with GEn AI, like when an intern uses an LLM plugin that escalates through a misconfigured OAuth integration. Adaptive systems should be able to evaluate behavioral intent and context apart from the basics of group membership.

To protect assets, apply identity-aware access rules that adapt to session risk scores. Disable Gen AI-related privileges, like API generation, in high-risk sessions. Integrate SaaS and cloud IAM with Gen AI usage monitoring.

Real-Time Anomaly Detection

Gen AI misuse won’t trip signature-based alert systems. Detection geared to thwart Gen AI must detect abnormal use patterns and strange access times, places, and users. It should know what baseline behavior looks like for any given resource and flag changes from this baseline.

Implement baseline modeling for Gen AI usage frequency, output types, and data touchpoints. Alert on sudden spikes in usage. Use natural language processing detection to spot suspicious prompts.

Continuous Policy Enforcement

SaaS and cloud-based Gen AI tools evolve fast. Security tools should, too. Security policies should update in near real-time to reflect new capabilities, attack vectors, and usage models. 

How? Integrate CSPM and SSPM tools with change management pipelines so newly added Gen AI features are flagged immediately. Define Gen AI use zones so that only trusted models can interact with sensitive systems. Use policy-as-code frameworks to audit Gen AI usage boundaries. 

The Future of Generative AI: Trends and Predictions

Generative AI is rapidly evolving, with key trends shaping its future. For example, a major shift occurring in the AI space is the move from centralized to decentralized AI models, allowing for distributed training across internet-connected devices. Further, AI systems are incorporating multimodal capabilities for supporting text, images, and audio, enhancing the versatility and autonomy of learning models. 

What if future deepfake identities pass Know Your Customer (KYC) rules? Background checks? Internal HR onboarding?

What if attackers train LLMs on internal employee email leaks, company infrastructure maps, and industry-specific tools? 

What if AI agents trained on endpoint and behavioral logs simulate real employee activity?

Gen AI’s future is boundless, and that’s not necessarily a good thing. In the immediate future, organizations will have to adapt to Gen AI advances, learning to detect LLM-written content and alerting employees that messages within official channels might be AI forgeries. Will there be a zero-trust strategy for language? 

It’s inevitable.

Output from LLMs should never be assumed to be trusted or safe, and monitoring outputs and verifying generated content is the next hurdle to beginning to adapt to the kinds of exploits that Gen AI is capable of.

In the immediate future, Gen AI will likely become a point of awareness in CNAPPs and SIEMs, which will increasingly log, analyze, and redact sensitive outputs. Security teams will begin to understand they need to run simulated Gen AI attacks, and LLM policy engines will increasingly serve as Gen AI posture management — restricting the kinds of prompts and responses allowed in production.

Upwind Stays Adaptive to GenAI Threats

As Gen AI accelerates the speed and sophistication of attacks, from ultra-personalized phishing to dynamic, evasive malware, static defenses fail. Upwind delivers adaptive security because it includes real-time visibility, behavioral detection, and automated responses at runtime. Whether spotting suspicious behavior generated by rogue LLM agents or tracing abnormal file access from synthetic identities, Upwind’s runtime-powered platform gives you the context you need to pivot.

It all leads to responsiveness before Gen AI threats escalate. Schedule a demo to see how Upwind can help you stay ahead of the curve.

FAQs

What are key generative AI risks for cloud-native teams?

Cloud-native teams face unique risks from Gen AI due to their reliance on automation, APIs, and distributed infrastructure. Generative models can be exploited to introduce vulnerabilities or sensitive data across these types of dynamic environments. How? Common approaches are:

• Prompt injection in internal tools
• LLM output leakage risks exposing secrets or business logic
• Automated recon maps cloud architecture from exposed repos and CI logs
• Synthetic identities bypass weak access controls
• AI-generated IaC may include insecure defaults
• Supply chain risks from malicious AI-written code impact dependencies

Are AI risks covered in major compliance audits?

While traditional compliance standards like SOC 2, GDPR, and HIPAA focus on data security and privacy, they often overlook AI-specific risks such as data bias, model transparency, and ethical concerns in automated decision-making. However, some audits are beginning to incorporate AI assessments, particularly in industries like finance and healthcare:

• SOC 2 may tag LLM data leakage under confidentiality or processing integrity
• ISO 27001 covers broad risk assessment
• HIPAA/GDPR focuses on personal information, and AI outputs must be trained to keep sensitive data safe
• NIST AI RMF provides voluntary guidance for AI specifically
• FedRAMP/PCI DSS hasn’t codified LLM-specific controls, but does monitor cloud use and data flows

As AI adoption grows, regulatory bodies are expected to refine standards to include AI governance. In the meantime, businesses should proactively include AI risks in their internal audits and risk management frameworks to future-proof their compliance efforts.

How do AI phishing threats differ from traditional ones?

AI-powered phishing threats are more sophisticated due to their ability to automate and personalize attacks at scale. While traditional phishing uses generic messages (often riddled with typos), AI-driven phishing campaigns use perfectly tailored, highly contextual and error-free messages; this content is developed by analyzing publicly available data, mimicking writing styles, and adapting in real-time. AI phishing can include:

• Very specific personalization like job titles, coworker names, and even writing style
• Voice deepfakes that sound real
• Adaptive bait, with variants designed to avoid filters and test their own success
• Multilingual fluency for global impact

AI phishing threats are, therefore, harder to detect and defend against.

What role do CNAPPs play in mitigating AI threats?

Cloud-native application protection platforms (CNAPPs) help mitigate AI-related security threats by providing visibility and control over cloud-native environments. That happens in 3 main ways:

1. Visibility
2. Configuration monitoring
3. Identity enforcement

They integrate security tools to protect AI models throughout the development lifecycle, continuously monitoring for risks like misconfigurations and data leaks. CNAPPs monitor APIs, spot IAM drift, and enforce policies across clouds, so complicated environments are more secure from AI-generated code that must, at the very least, comply with least privilege and secure defaults.