Introduction: The Double-Edged Sword of Generative AI for Business Leaders

Artificial intelligence is revolutionizing how businesses operate – automating workflows, enhancing decision-making, and even bolstering cybersecurity through faster threat detection and smarter intrusion detection systems. Forward-looking CISOs and IT leaders recognize the immense potential of AI in cybersecurity, leveraging artificial intelligence for security tasks like anomaly detection and incident response. Yet, this same technology has introduced a double-edged sword: generative AI (GenAI) tools like ChatGPT and Copilots can just as easily become a source of new AI security risks and compliance headaches. Organizations are rapidly adopting GenAI – 71% of companies report using generative AI in at least one business function – to boost productivity and innovation. But without proper controls, every prompt to an AI assistant could turn into a data security nightmare, inadvertently exposing sensitive information or violating privacy regulations.

Business leaders now face a critical question: How can we embrace the benefits of AI security (and even use AI cyber security to our advantage) without opening the floodgates to AI security risks? On one hand, generative AI promises efficiency gains and competitive edge; on the other, it introduces GenAI risks like LLM data leakage, prompt injection attacks, and unintentional data breaches. This article explores these emerging risks and provides a roadmap for AI risk management and AI governance. We’ll look at real examples of AI-related data leaks, the growing patchwork of regulations (from GDPR to HIPAA and beyond), and how frameworks like NIST’s AI Risk Management Framework (AI RMF) and ISO/IEC 42001 can guide enterprises. Most importantly, we highlight pragmatic solutions – including real-time GenAI data loss prevention (DLP) and automated compliance enforcement – that allow organizations to harness AI’s power safely and compliantly.

If you’re a CISO, compliance officer, or enterprise AI program manager navigating the brave new world of ChatGPT and enterprise AI, read on. This Gartner-style guide will arm you with insights and actionable steps to secure your GenAI workflows, protect sensitive data, and turn your workforce into a “human firewall” against AI-enabled threats.

The Rise of Shadow AI and the New AI Security Risks

Generative AI’s accessibility has led to an explosion of unsanctioned usage in the workplace – often called “shadow AI,” akin to shadow IT. Employees eager to leverage tools like ChatGPT, Google Bard, or GPT-4 often do so without IT’s approval. They paste proprietary code into a public chatbot to debug an error, or upload customer data to get a draft report, not realizing that information may now reside on external servers outside the company’s control. This shadow AI phenomenon is widespread: a recent survey found 75% of organizations have already experienced at least one security incident caused by employees oversharing sensitive data with AI tools. In other words, AI security breaches are not theoretical – they are happening in real life, across industries.

High-profile incidents have underscored the stakes. In early 2023, Samsung engineers accidentally leaked sensitive source code by entering it into ChatGPT, prompting Samsung to ban internal use of external AI tools. Wall Street banks like JPMorgan and Goldman Sachs quickly restricted ChatGPT usage for fear that confidential financial data could leak and trigger regulatory action. Even Amazon warned employees to be cautious after discovering ChatGPT responses that resembled internal data. The message is clear: whether in banking, technology, or legal services, one careless AI prompt can result in a ChatGPT data breach or compliance violation that puts the entire enterprise at risk.

What makes GenAI risks so challenging is that they stem from normal user behavior (not rogue malware). Traditional security tools aren’t calibrated to catch an employee asking an AI a question. Consider a few scenarios exposing the new AI security risks:

Oversharing in Prompts: An unwitting analyst might paste a client’s personally identifiable information (PII) or a list of credit card numbers into a chatbot to “get help” analyzing it. The AI’s servers quietly store that data, creating a potential data leak. In fact, nearly half of employees in one study admitted to uploading sensitive corporate data to public AI tools – a recipe for trouble under privacy laws.

• Generative Access Control Failures: Modern workplace GenAI tools (e.g. Microsoft 365 Copilot integrated with SharePoint) could hallucinate or pull in data from documents a user normally shouldn’t access. For example, a Copilot might inadvertently include a snippet from a confidential financial report in a draft email because it “saw” that data during its analysis. This is a new twist on access breaches – the AI isn’t hacking the file storage, but it might bypass network security and segmentation by aggregating data it shouldn’t.

  
  

• AI Plugins and Integration Loopholes: Many GenAI platforms allow third-party plugins (connecting to calendars, CRM systems, databases). A misconfigured plugin or API integration can expand the attack surface – the AI could pull records from a sensitive database and include them in a response. Without proper oversight, these shadow IT integrations create unseen vulnerabilities.

  
  

• Prompt Injection Attacks: Malicious actors can exploit the way AI systems interpret instructions. In a prompt injection attack, an outsider could input a cleverly crafted prompt to an AI system (say, a public-facing chatbot or even an internal AI assistant) that tricks it into revealing confidential info or performing unauthorized actions. Unlike traditional hacking, this doesn’t involve breaking in through firewalls – it manipulates the AI’s instructions, essentially hacking the logic. This emerging threat means security teams must treat AI models and prompts as part of the threat landscape.

  
  

• Insider Misuse with AI: A disgruntled insider could intentionally use AI tools to exfiltrate data in sneaky ways. For instance, an employee might ask an AI image generator to encode secret data within an image (steganography) or use an AI text summarizer to compress a database of customer records into a seemingly innocent paragraph, then take it out of the company. These methods fly under the radar of legacy DLP systems because they don’t trip traditional filters.

Each of these scenarios shows how enterprise data security can be compromised via AI without any malware involved – the “breach” happens through legitimate AI queries and outputs that evade legacy controls. Because GenAI interactions are conversational and often encrypted web traffic, your endpoint security solutions, email filters, and perimeter intrusion detection systems likely won’t catch an employee typing confidential figures into a chatbot. From the security team’s perspective, it might look like just another HTTPS request to an allowed SaaS service.

This shift calls for a rethinking of corporate security. It’s not enough to rely on policies that say “don’t input sensitive data into AI” – those are often ignored or misunderstood by users chasing productivity. Building a human firewall (cybersecurity awareness) is certainly part of the solution: organizations should train and regularly remind employees about the dangers of ChatGPT data leaks and shadow AI. Security awareness programs in regulated industries (finance, healthcare, etc.) now include guidance on AI usage, much like phishing awareness. However, even well-intentioned humans make mistakes, especially under deadline pressure or if they think a tool is safe. That’s why technology safeguards must step up to fill the gap.

In summary, Generative AI risks present a clear and present danger. Business leaders can’t afford to ignore these AI security risks, especially as attackers pivot to exploit them. The next section examines how the evolving regulatory and compliance landscape is putting additional pressure on organizations to get AI governance right.

Navigating Compliance: Regulations and Frameworks for AI Governance

For enterprises in regulated sectors – from banking and insurance to healthcare, consulting, and the public sector – the emergence of AI usage has rung alarm bells for compliance officers. Existing data protection and IT security regulations still apply to AI, even if the technology is new. In fact, regulators have begun explicitly focusing on AI. In Europe, the GDPR (General Data Protection Regulation) has already been invoked to question AI data handling practices. Italy’s Data Protection Authority famously banned ChatGPT temporarily in 2023 over GDPR violations, citing an absence of legal basis for collecting personal data and a failure to prevent minors from using the service (reuters.com). GDPR fines can be steep – up to €20 million or 4% of global turnover – and a company that lets employees funnel EU customer data into an unvetted AI could easily find itself in hot water. Turkey’s KVKK (Personal Data Protection Law) mirrors GDPR in many ways, meaning Turkish firms also face multi-million lira penalties for exposing personal data via AI. In the US, sectoral laws like HIPAA (healthcare) and PCI DSS (payment card industry) mandate strict controls on sensitive data – and an employee’s ChatGPT query containing patient records or credit card numbers would be a clear policy violation, if not a legal one.

Beyond these laws, new frameworks are emerging to guide AI governance and risk management. Two notable ones are the NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001:

• NIST AI RMF (1.0) – Published by the U.S. National Institute of Standards and Technology in 2023, this framework provides a voluntary but comprehensive approach to managing AI risks. It emphasizes trustworthiness characteristics (like transparency, fairness, security, and privacy) and outlines core functions: Map, Measure, Manage, and Govern AI risks. For business leaders, NIST’s framework offers a blueprint to identify how AI applications could fail or be misused, and what controls are needed.It encourages organizations to incorporate AI risk management into their existing enterprise risk processes (nist.gov). For example, under AI RMF, a bank using an AI chatbot would map out potential harms (e.g. leaking client data), measure the likelihood/impact, and implement controls such as DLP or monitoring to manage the risk in line with its tolerance. The framework’s guidance aligns with many compliance obligations, helping enterprises prove to auditors and regulators that they are taking a structured approach to AI oversight.

  
  

• ISO/IEC 42001:2023 (AI Management System) – This brand-new international standard (the world’s first AI-specific management system standard) provides requirements and guidance for establishing an Artificial Intelligence Management System (AIMS) in organizations. In essence, ISO 42001 is to AI what ISO 27001 is to information security. It sets out a structured way to manage AI risks and opportunities, ensuring organizations balance innovation with governance. Key themes include ethical AI use, transparency, reliability, and continuous improvement of AI processes. Importantly for regulated industries, ISO 42001 emphasizes aligning AI practices with legal and regulatory requirements – essentially it helps demonstrate AI compliance. For instance, a healthcare provider implementing ISO 42001 would establish policies for how patient data can be used in AI model training, access controls for AI systems, documentation for algorithm decisions, and ongoing audits – all of which would support HIPAA compliance and build trust in their AI. Implementing such a framework can also serve as evidence of due diligence if regulators come knocking about the organization’s AI activities.

Other bodies and regulations are on the horizon too (the EU’s forthcoming AI Act, new guidance from regulators in finance and telecom, etc.), but the key point is: AI governance is now a board-level issue. Forward-thinking organizations are already creating internal AI councils or designating a Chief AI Ethics or Risk Officer to oversee responsible AI use. Whether you follow NIST, ISO, or your local regulator’s guidelines, there are common best practices: conduct AI risk assessments, inventory your AI systems and data flows, evaluate third-party AI providers for security, and enforce policies that limit how sensitive data can be used in AI. Documentation and transparency are critical – for example, keeping logs of all AI interactions and decisions can aid in audits and investigations.

Crucially, the regulators don’t expect you to avoid AI altogether (in fact, Italian authorities let ChatGPT resume service after OpenAI implemented new privacy controls). Rather, they expect “responsible AI use” – meaning you take reasonable measures to protect data, ensure privacy, and prevent harm when deploying AI. This is where technology solutions come into play. We’ll next discuss how cybersecurity tools are evolving to address generative AI, and how they fit into a compliant AI governance strategy.

From Traditional DLP to GenAI DLP: Evolving Your Security Toolkit

Many organizations already use Data Loss Prevention (DLP) tools as part of their cybersecurity stack. Traditional DLP solutions monitor and control data transfers – scanning emails and messages for sensitive info, blocking unauthorized uploads or downloads (like to USB drives or personal cloud apps), and enforcing encryption. However, legacy DLP was not built for the semantic, freeform nature of AI conversations. Conventional DLP might flag a Social Security number in a spreadsheet attachment, but it struggles to interpret an employee’s prompt like: “Please summarize the attached client list with contact details” – which might indirectly expose personal data to an AI. The old keyword-based or regex-based rules can miss context or get overwhelmed by the volume of AI interactions. Clearly, we need a new approach: Real-time GenAI DLP.

A GenAI-aware DLP or “LLM firewall” works like a smart intermediary that guards every AI interaction. Instead of only scanning files or network packets, it intercepts AI prompts and responses in real time, using natural language understanding to determine if sensitive data is present or if the content violates policy. For example, if an employee tries to feed a confidential design document or source code snippet into an AI assistant, the GenAI DLP would detect the sensitive nature (e.g. code patterns or client identifiers) and block or redact the transmission before it ever leaves the endpoint. Similarly, if an AI’s response contains something that looks like a credit card number, an API key, or personal address, the system can filter or scrub that output before showing it to the user. This is a much more dynamic, context-aware form of DLP than the past generation.

Key capabilities of modern GenAI DLP solutions include:

Shadow AI Detection: Continuously monitor which AI and SaaS tools employees are using. This addresses the SaaS security and shadow IT risks – giving security teams visibility into unsanctioned apps or unusual AI activity. For instance, if someone starts using a new AI writing app or an obscure LLM API, the system should flag it for review.

• Real-Time Prompt Interception: Act as a prompt interceptor that analyzes text being sent to AI platforms (in web browsers, chat apps, IDEs, etc.). Using machine learning and context analysis, it can spot things like personal data, financial records, or classified keywords in the prompt. If something is deemed sensitive or policy-inappropriate, the action can be to warn the user, block the prompt, or mask the sensitive portion. This endpoint data loss prevention at the AI interface is crucial – once data enters the AI’s cloud, it’s too late to pull it back.

  
  

• Policy Enforcement and Compliance Automation: Tie the DLP rules to your compliance requirements. For example, if GDPR or data privacy compliance rules say no personal data can be transferred to an unapproved processor, the system ensures that by blocking any prompts containing personal data unless the AI tool is on an approved list. It can also enforce internal policies like “no uploading source code to public AI” or “executives shall not use AI without encrypted channels”. Automated enforcement relieves the burden on employees to make judgment calls – it creates guardrails so they innovate safely. Some systems, like Sorn Security’s platform, even ensure every AI prompt is logged and compliant with regulations like GDPR, HIPAA, KVKK, and more.

  
  

• Anomaly Detection and Threat Monitoring: Advanced AI security tools incorporate threat detection logic to identify suspicious patterns in AI usage. For instance, an employee normally sends a few short prompts a day, but suddenly one day they attempt to paste 20 pages of data into ChatGPT – that spike in volume could indicate a possible data dump. Or if a user who never used an AI tool before suddenly tries to access an admin-only AI system, that could indicate compromised credentials being used by an attacker. The system should raise instant alerts on such anomalies. This functions like an intrusion detection system for your AI layer, catching misuse or potential insider threats in real time.

  
  

• Integration Across Communication Channels: GenAI is popping up everywhere – web browsers, messaging apps.(Slack, Teams integrating AI bots), IDEs for coding assistants, etc. Security solutions need to cover all these channels to provide unified protection. This might involve browser extensions, API integrations with chat platforms, and endpoint agents to cover local applications. The goal is a unified view of all AI interactions enterprise-wide, so nothing slips through the cracks. As an example, “Sorn Security monitors employee AI use across Slack, Teams, and browsers — blocking risky uploads and ensuring every prompt stays compliant”.

  
  

• Audit Logging and Traceability: For compliance and forensic analysis, the system should log all allowed and blocked AI interactions. This builds an audit trail to show regulators or internal auditors that no sensitive data was improperly shared, or if there was an attempt, it was prevented and recorded. Maintaining logs also supports AI governance transparency – you can answer questions like who used which AI tool and what data was involved. Sorn Security’s platform, for instance, tracks every AI interaction with full transparency to verify data integrity and compliance with frameworks like NIST AI-RMF and ISO 42001.

By evolving DLP into this context-aware, AI-savvy protector, organizations create a safety net that allows them to confidently leverage GenAI. Instead of resorting to outright bans (which hurt productivity and may just drive users to find workarounds), companies can enable AI use with guardrails in place. In regulated industries like banking or healthcare, this is a game-changer: you can let your teams use powerful AI tools to automate work – drafting reports, analyzing trends, coding – without compromising customer data or violating compliance. It’s about striking that balance between innovation and control.

Equally important is that these solutions align with the earlier-mentioned frameworks. For example, the NIST AI RMF talks about monitoring AI systems and layered risk mitigations; a GenAI DLP is a technical control that fulfills those recommendations in practice. Likewise, ISO 42001 requires ongoing risk treatment – deploying an AI DLP tool is a concrete risk treatment for data leakage risk.

Now, let’s zoom in on how one such solution works in practice and solves the challenges we’ve outlined.

How Sorn Security’s Real-Time GenAI DLP Solves the Challenge

Sorn Security has developed a purpose-built solution to address the very GenAI security and compliance issues we’ve been discussing. In a nutshell, it provides real-time protection against shadow AI and sensitive data loss across the enterprise. By deploying Sorn Security’s platform, CISOs and AI program managers gain a 24/7 AI security sentinel that watches over every GenAI interaction within the organization.

Here’s how Sorn Security’s approach directly tackles the challenges:

• Comprehensive Visibility & Shadow AI Monitoring: Sorn Security gives enterprises full visibility into how employees are using AI – from popular tools like ChatGPT, Bard, and Claude, to niche AI apps or APIs. It automatically detects unauthorized AI tools or unsanctioned usage, effectively shining a light on shadow AI in your environment. For example, if someone tries a new AI Chrome extension that hasn’t been approved, Sorn will flag it. This helps IT teams enforce SaaS compliance – ensuring only vetted, secure AI services are used, and identifying any risky behavior early.

  
  

• Real-Time Prompt Interception and Data Loss Prevention: Acting as a smart AI firewall, Sorn Security intercepts prompts and file uploads to AI models in real time. It uses semantic analysis to spot if a user is about to send confidential information. If an engineer inadvertently tries to paste customer account numbers or source code into ChatGPT, Sorn’s Prompt Interceptor will block that action on the fly. The user can be alerted with a gentle reminder of policy (“This content is sensitive and was not sent to the AI”). In this way, Sorn stops sensitive data before it ever reaches the AI model, preventing those irreversible leaks and preserving data privacy compliance.

  
  

• Automated Policy Enforcement: With Sorn, organizations can codify their AI usage policies into automated rules. The platform enforces AI policies across Slack, Teams, web browsers and more, so that no matter where an employee interacts with GenAI, the same rules apply. For instance, if company policy says “no customer PII in any AI prompt unless encrypted,” the system will universally uphold that. This automation relieves managers from manually policing AI usage and ensures consistency. Crucially, it keeps every prompt compliant with GDPR, HIPAA, KVKK, PCI DSS and other regulations by design. Compliance officers can thus sleep easier knowing a technical control is actively preventing violations.

  
  

• Anomaly Detection & Incident Alerts: Sorn Security continuously monitors AI use for unusual or risky patterns, much like a security camera. It can spot anomalies like large data dumps, repeated attempts to bypass restrictions, or access to unapproved LLMs. The moment such an event is detected, Sorn can alert the security team or log it as a policy violation. Early detection means you can respond to potential incidents before they escalate into full-blown breaches. For example, if a normally low-usage employee suddenly tries to export an entire client database via an AI tool, Sorn’s alert allows the team to intervene, investigate the user’s intent (could be malicious or just careless), and mitigate any damage. This proactive stance turns your AI usage from a blind spot into a monitored part of your attack surface management.

  
  

• Alignment with Frameworks and Audit Support: Everything Sorn’s platform does is built with compliance in mind. It logs every AI interaction with full detail – what was input, what was output (or blocked) – creating an audit trail that can be referenced for incident investigations or regulatory audits. These logs can demonstrate that, for instance, “Employee X attempted to input 5 customer records into ChatGPT on date Y, but the data was blocked – no leak occurred.” Such evidence is invaluable for GDPR’s accountability principle or during ISO 42001 certification audits. Additionally, Sorn’s solution maps to controls recommended by NIST AI RMF and ISO – helping organizations stay aligned with best practices. In effect, Sorn Security operationalizes those high-level frameworks into day-to-day security practices.

  
  

• Enabling Safe Innovation: Perhaps most importantly, Sorn Security’s GenAI DLP empowers business leaders to let their teams use AI freely – without compromising security. Instead of saying “No AI at work” (which is neither practical nor forward-thinking), the message becomes “Yes, you can use AI to be more productive, because we have safety nets in place.” This is a huge benefit for enterprise culture and innovation. Developers can use AI coding assistants, analysts can use GPT for research, customer support can use AI to draft responses – all while the company’s data protection posture remains intact. Sorn essentially eliminates the trade-off between productivity and security in the context of AI. For risk-conscious organizations, this translates to competitive advantage: you adopt AI faster than peers who are still paralyzed by fear of the risks.

Real-world examples in regulated industries highlight the impact. A bank deploying Sorn Security can allow its wealth advisors to use GenAI for drafting policy summaries or market analyses, knowing client data won’t leak and that everything is logged for compliance (GDPR/PCI) checks. A healthcare provider can let doctors use an AI assistant to streamline paperwork, with confidence that no HIPAA-protected data will slip out unprotected. A telecommunications firm can permit its engineers to leverage AI for coding or troubleshooting without worrying about proprietary network data exposure. In each case, Sorn’s real-time controls act as an automatic compliance officer, enforcing rules and preventing mistakes.

In essence, Sorn Security’s solution is an embodiment of the “secure AI adoption” philosophy: give the business the tools it needs to innovate, while seamlessly enforcing the necessary security and privacy constraints under the hood. It’s the kind of balance Gartner analysts often cite as a hallmark of mature digital transformation – and it’s now achievable for AI.

Best Practices for Building an AI-Ready Security Posture

Adopting a platform like Sorn Security’s GenAI DLP is a big step toward securing AI, but technology works best in tandem with process and people. As you strengthen your AI security posture, consider these best practices (many of which map to controls in frameworks like NIST AI RMF and ISO 42001):

1. Conduct an AI Risk Assessment: Take inventory of all AI systems and services in use (or planned) within your organization. Map out the data flows – what data is going into or out of these AI tools? Identify sensitive data touchpoints and AI security risks (e.g. personal data exposure, financial data leakage, model vulnerabilities). This risk mapping exercise will highlight where controls like DLP, access restrictions, or encryption are needed most.

   
   

2. Update Policies and Educate Your Workforce: Develop clear AI governance policies that outline acceptable use of AI. For example, define which AI tools are approved, what types of data can/cannot be input, and guidelines for reviewing AI outputs. Communicate these policies to all staff and incorporate them into regular security awareness training. Building a human firewall means empowering employees to be the first line of defense – they should understand that copying a client’s file into a chatbot is akin to emailing it to an external party (and therefore potentially a data compliance violation). Make it practical with do’s and don’ts and real examples from your industry (like the Samsung incident).

   
   

3. Implement Technical Controls (Defense in Depth): Deploy solutions such as GenAI-aware DLP, endpoint security updates, and cloud access security brokers (CASBs) configured for AI SaaS usage. Ensure that any AI tool that processes your enterprise data has proper security measures – prefer enterprise versions of AI services that offer data protection assurances (for instance, OpenAI’s ChatGPT Enterprise with encryption and no training on your inputs). Integrate your AI security tools with your broader security operations center (SOC) workflows; for example, AI usage alerts should feed into your SIEM so they can be investigated like any other incident.

   
   

4. Embrace a “Zero Trust” Mindset for AI: Just as modern cybersecurity has moved to zero trust (never trust, always verify), treat AI interactions with a zero trust lens. Authenticate and authorize AI access the same way you would for a human or service account. If an AI system is pulling data from your databases, use least privilege – only allow the minimum data needed and log every query. Segment AI systems in your network architecture (for instance, run internal AI models in a segregated environment so they can’t accidentally touch sensitive production data unless explicitly allowed – akin to network segmentation). Assume that any AI service could be breached or could behave unpredictably, and plan controls accordingly.

   
   

5. Test and Audit Your AI Security Measures: Regularly audit AI activities and the effectiveness of your controls. Consider red teaming in AI – task your internal red team or an external firm to simulate prompt injection attacks, data poisoning, or attempts to exfiltrate data via AI, and see if your defenses catch them. Security playbooks should be updated to include response plans for AI-related incidents (e.g. what to do if an employee accidentally leaked data to an AI API – who to notify, how to work with the vendor to delete data, etc.). Running drills will ensure your team isn’t caught flat-footed when an AI incident occurs. Additionally, monitor the regulatory landscape (NIST, ISO, and local laws) for updates – compliance is evolving, so your program should too.

By following these best practices, organizations create a robust, AI-ready security posture that not only addresses current AI security risks but is resilient against future challenges. The goal is to make enterprise data security and data privacy compliance foundational to all AI initiatives, rather than an afterthought.

Conclusion: Embrace AI Innovation Securely and Responsibly

Generative AI is poised to remain a driving force in business transformation. Forward-looking leaders in banking, insurance, tech, government, and beyond know that completely blocking AI is not a sustainable strategy – the competitive advantages are too compelling. Instead, the winning approach is to embrace AI securely and responsibly. This means acknowledging the AI security risks (from AI governance gaps to AI security breaches) and proactively mitigating them through a combination of people, processes, and technology.

By implementing strong AI risk management practices – guided by frameworks like NIST AI RMF and ISO 42001 – and leveraging solutions such as Sorn Security’s real-time GenAI DLP and automated compliance enforcement, organizations can turn AI from a source of anxiety into a source of strength. Business leaders can confidently say “yes” to AI tools that boost efficiency, knowing that data protection, data security, and regulatory compliance are continuously enforced behind the scenes. The end result is a balance between innovation and control: your enterprise gains the upsides of AI (speed, scale, intelligence) without the downsides of uncontrolled AI security risks.

In today’s environment, where a single LLM data leakage or compliance misstep can lead to headlines and hefty fines, such prudence is not just wise – it’s non-negotiable. Fortunately, with the right safeguards, AI cyber security can be managed like any other business risk: systematically and effectively.

If you are a risk-conscious organization looking to secure your GenAI workflows and ensure compliance, the next logical step is to get expert guidance tailored to your environment. Contact the Sorn Security team to discuss how our real-time GenAI DLP and policy enforcement platform can help you harness AI’s potential safely. Our experts have helped enterprises in regulated industries implement AI securely – we’d love to chat about your use cases and challenges. Don’t let fear of AI risks hold your organization back. With Sorn Security as your partner, you can innovate with confidence, knowing that every AI interaction is monitored, protected, and compliant. Let’s work together to turn AI into a competitive advantage – safely and responsibly.

FAQ

Q1: What is Generative AI (GenAI) data leak prevention?
A: GenAI data leak prevention focuses on detecting and blocking sensitive information shared through AI tools such as ChatGPT or Microsoft Copilot. Real-time DLP solutions help enterprises control AI usage, maintain compliance, and prevent unintentional data exposure.

Q2: How does Sorn Security detect Shadow AI usage within enterprises?
A: Sorn Security monitors AI activity across cloud and SaaS environments, flagging unsanctioned tool use or risky prompts. It provides real-time visibility and policy enforcement to reduce compliance risks.

Q3: Why are traditional DLP systems not enough for GenAI security?
A: Legacy DLP tools rely on predefined rules and lack semantic understanding. Modern AI interactions require context-aware detection and adaptive controls to identify subtle data leakage patterns unique to GenAI systems.

Q4: How can companies ensure compliance when using AI tools?
A: Organizations should align AI governance with frameworks like NIST AI RMF, ISO 42001, and GDPR. Solutions like Sorn Security automate compliance checks and maintain auditable logs to meet global standards.