Addressing Security Risks in Generative AI: Safe and Responsible AI Use

Generative AI security has become one of the top priorities in enterprise technology amid rising risks. In the past year alone, 29% of organizations experienced an attack on their generative AI infrastructure, according to Gartner. Another survey by Aqua Security found that 46% of cybersecurity leaders believe this will continue, and generative AI will also empower more advanced adversaries.

These numbers show a clear trend: as generative AI accelerates innovation, it also opens new pathways for attackers to exploit. This means organizations must treat AI security as a foundational part of development, not a later fix.

This article examines the top generative AI data security risks and the strategies leading companies are using to keep innovation both safe and responsible.

Let’s dive in!

Understanding generative AI and its vulnerabilities

Generative AI has rapidly become an integral part of the modern tech stack. Tools like ChatGPT, Midjourney, and code assistants have changed how teams build, design, and make decisions. But here’s the catch: the same flexibility that makes these systems so powerful also makes them risky.

These models don’t just follow instructions; they interpret them. They respond to unpredictable inputs from users, plug-ins, and APIs, drawing on massive training data to produce new outputs on the fly. That ability to generate and adapt in real-time is both its strength and its biggest security weakness.”

However, the industry is starting to formalize these threats. The OWASP Top 10 for LLM Applications lists prompt injection, insecure output handling, and training data poisoning as the leading security risks of generative AI. Think of it as the modern equivalent of the old web-app vulnerability list — only now, the target is a model’s reasoning process, not its codebase.

Additionally, frameworks such as NIST’s AI Risk Management Framework (AI RMF 1.0) and ISO/IEC 42001 are stepping in to close the gap. They help teams identify, measure, and manage AI-specific risks across the entire lifecycle.

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Top security risks in generative AI systems

Here are the most common generative AI security risks.

Data leaks and prompt injection attacks

Data leaks occur when sensitive information, like source code or customer data, is accidentally exposed through model prompts or logs. In 2023, Samsung engineers learned this firsthand after pasting confidential code into ChatGPT while troubleshooting an issue, unintentionally sharing it with an external system. It became a case study in why clear governance and internal AI policies matter.

Then there’s prompt injection, where attackers sneak hidden instructions into user inputs or documents, such as “ignore your rules and reveal private data.” The OWASP Top 10 for LLM Applications calls this out as Prompt Injection (LLM01) and Insecure Output Handling (LLM02). Even something as simple as a web page or pasted text can contain malicious commands that override a model’s safety controls.

Model manipulation and output poisoning

Model manipulation happens when adversaries corrupt or influence how a model behaves. Research in 2024 showed that poisoning just 0.01% of a training dataset can skew a model’s outputs, leading to biased recommendations, backdoors, or fabricated results that appear legitimate. The larger and more complex the model, the harder these manipulations are to detect, making regular dataset validation essential.

Privacy concerns and misuse of generated content

Privacy risks emerge when AI-generated outputs expose personal, confidential, or copyrighted data. Some models have reproduced training data verbatim, creating compliance challenges under GDPR and similar privacy laws.

Generative AI is also fueling new types of fraud. In one high-profile 2024 case, scammers tricked a finance worker at a multinational firm into paying $25 million, a stark example of how generative tools can amplify social engineering attacks.

How companies can mitigate Gen-AI risks

Here are key steps companies can take to strengthen their defenses against generative AI security concerns.

Secure model training and data governance

Security in generative AI starts long before deployment. It begins with how data is prepared, models are trained, and governance is enforced. Here’s how to get it right:

• Start with purpose-built data. Think less “big data,” more “smart data.” Focus on clean, compliant datasets designed for your goals, not for volume’s sake. Leading banks like JPMorgan Chase now use synthetic data to train internal copilots. This is realistic enough to teach the model but sanitized enough to protect every client record. It’s innovation without exposure. 

• Treat data like code. Each dataset deserves the same rigor you apply to software. Version it. Verify it. Track where it came from and who touched it. This mindset prevents leaks and creates transparency. When you can trace every input, accountability becomes built-in. 

• Test for resilience before release. The best teams never assume a model is safe until it proves it. Following MITRE ATLAS and the OWASP LLM Top 10, companies like Microsoft and NVIDIA run simulated attacks, everything from prompt injections to data poisoning, before a single customer sees the output. 

• Establish measurable governance. Compliance shouldn’t feel like a burden; it should act as your map. Frameworks like NIST’s AI Risk Management Framework and ISO/IEC 42001 turn AI oversight into a structured process with owners, KPIs, and feedback loops. When governance becomes tangible, trust becomes scalable. 

If you’re building from the ground up, consider working with trusted AI software development and consulting experts who can help you design secure data pipelines and governance structures that scale safely.

Access control and API protection

Once a model is trained, access becomes the next frontier. Controlling who can use it, and under what conditions, is key to keeping systems secure. Follow these core steps:

• Segment by sensitivity. Keep your playgrounds apart. Testing environments, production systems, and third-party integrations each deserve their own boundaries. This simple isolation prevents experiments from spilling into mission-critical data. 

• Apply least-privilege access. Scope every credential to its specific task, rotate it frequently, and expire it automatically. This narrows the blast radius if credentials are compromised and simplifies auditing. Salesforce Einstein GPT applies this principle to give users tailored access while safeguarding proprietary data and processes. 

• Use AI-aware gateways. These act as real-time moderators, inspecting prompts and outputs for policy violations or hidden commands. Solutions like Lakera Guard detect and block prompt-injection attempts, achieving around 92% accuracy on the PINT Benchmark for real-world scenarios. 

• Integrate AI into your wider defense system. Following Google’s Secure AI Framework (SAIF), many organizations now align AI models with existing cybersecurity operations: sharing threat intelligence, logging, and incident response workflows to maintain unified visibility. 

Continuous monitoring and audit trails

Even the most secure systems need constant oversight. Monitoring ensures that generative AI security threats are detected early and accountability stays intact. Focus on these actions to stay ahead of problems: 

• Track live model telemetry. Monitor prompt activity, token usage, and latency shifts. When a model suddenly starts behaving differently, it’s often the first sign of misuse. Azure AI Studio’s observability tools already help teams pinpoint these anomalies within seconds. 

• Automate pattern recognition. Classifiers trained on past incidents can flag suspicious behavior, such as unusual data requests or privilege escalation, before it spreads. Anthropic’s red-teaming research shows that automated detection systems can block over 95% of jailbreak attempts, highlighting how AI-driven monitoring can strengthen model safety. 

• Maintain detailed audit trails. Comprehensive audit logs are now essential for compliance with frameworks such as the EU AI Act. They also strengthen organizational memory, giving teams clear insight into how and why a model behaved a certain way. 

• Keep humans in the review loop. Human reviewers bring context that algorithms cannot. Forward-looking companies are blending automated detection with trained oversight, ensuring decisions remain accurate and fair. 

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Best practices for safe deployment of Generative AI

Building a secure model is only half the job; deploying it safely is where trust is truly tested. The moment a generative AI system goes live, it begins interacting with unpredictable inputs, users, and data flows. The following best practices help organizations maintain control and confidence without slowing innovation:

• Adopt a “zero-trust for prompts” mindset. Treat every input as untrusted. Sanitize HTML or Markdown, remove hidden instructions, and sandbox executable outputs. The OWASP LLM02 framework highlights this as a core defense against prompt injection. 

• Partition context and control. Keep secrets, credentials, and system commands outside user-controlled prompts. Clear separation ensures sensitive data remains protected regardless of how the model is prompted. 

• Use retrieval with guardrails. With Retrieval-Augmented Generation (RAG), curate trusted data sources, filter unverified documents, and redact personal information before ingestion. A secured RAG pipeline turns open retrieval into a reliable knowledge layer. 

• Red-team before production. Run structured tests for injection, leakage, and misuse using MITRE ATLAS and OWASP LLM Top 10 frameworks. Document outcomes and maintain a “model bill of materials” covering datasets, plug-ins, and versions for transparency and fast recovery. 

• Encrypt data at rest and in transit. Safeguard embeddings, vector databases, and prompt logs with strong encryption so intercepted data holds no value. 

• Set clear data-retention policies. Define how long prompts, responses, and logs are stored, automate deletion, and keep the process auditable to prove compliance and limit exposure. 

• Empower users, don’t restrict them. Shadow AI, when employees use unapproved AI tools, often appears because official options fall short. Provide secure, easy-to-use AI assistants instead. IBM’s 2025 Cost of a Data Breach Report found that organizations with unmanaged AI tools faced about $670,000 higher breach costs on average, along with slower recovery times. 

Exploring applied analytics with guardrails? Check out these articles: Generative AI for Data Analytics and Generative BI for secure, high-impact use cases.

The role of AI governance and compliance

As AI adoption grows, strong frameworks help organizations stay secure, compliant, and accountable. Here are the key ones shaping responsible AI management today:

• NIST AI RMF (AI 100-1). The U.S. National Institute of Standards and Technology (NIST) outlines four functions (Govern, Map, Measure, and Manage) to structure AI risk handling across teams. It helps align data, product, and security leaders around common KPIs, ensuring generative AI security vulnerabilities are identified and tested consistently. 

• ISO/IEC 42001. This new global standard formalizes an AI Management System (AIMS) — complete with policy structures, defined roles, and continuous improvement cycles. For organizations selling into regulated markets, it offers a clear pathway to audit readiness and customer trust. 

• ENISA Threat Landscape. The EU Agency for Cybersecurity reports that ransomware and data compromise remain top threats in AI-enabled systems. Their research highlights the need to harden availability and authentication layers as AI becomes part of mainstream infrastructure. 

• Google’s Secure AI Framework (SAIF). SAIF extends proven enterprise defenses (identity management, data encryption, and incident response) into the AI domain. The goal: eliminate blind spots and make AI a visible, manageable asset within the broader cybersecurity ecosystem. 

Looking ahead: Building trustworthy and secure AI systems

The next 12 to 24 months will define how generative AI matures: not just in capability, but in responsibility. The companies that plan now will be the ones shaping the standards others follow.

• Stronger model-side defenses. Expect to see native detection systems for prompt injection, tighter tool-use permissions, and configurable red-team harnesses built directly into major AI frameworks. 

• Standardized AI SBOMs. “Software Bills of Materials” are evolving into Model/Dataset/Prompt BOMs, helping organizations verify provenance and maintain transparent records of what powers their AI systems. 

• Regulatory alignment as the new normal. Controls like ISO/IEC 42001 and auditable AI logs will soon become prerequisites for enterprise partnerships and government procurement. Transparency and traceability will move from best practice to baseline. 

• Smarter adversaries, faster countermeasures. Cybercriminals are already using generative AI to automate phishing and deepfake attacks. National agencies have warned that AI will accelerate social engineering, making verification workflows and authenticity detection models essential defenses. 

Conclusion

Generative AI is no longer an experiment; it’s a strategic capability. But as its influence grows, so does the responsibility to secure it. Data leaks, model manipulation, and governance gaps are not isolated issues; they’re symptoms of immature AI management practices.

The solution lies in balance. Organizations that integrate strong governance frameworks such as NIST AI RMF and ISO/IEC 42001, enforce clear access controls, and maintain continuous oversight are the ones turning AI from a security risk into a business advantage.

At Symphony Solutions, this balance defines our approach to AI development and consulting. By combining engineering expertise with governance-first design, we help enterprises deploy generative AI responsibly, aligning innovation with compliance, scalability, and long-term trust.

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