Best GenAI Security Solutions
The best solutions to govern GenAI usage in the organization and prevent data loss.
Written by
Joel Witts
Technical Review by
Laura Iannini
Quick Summary
For organizations needing visibility and control over web-based GenAI activity, LayerX deploys as an extension across all major browsers with granular controls blocking, warning, or restricting actions per application and strong extension risk scoring.
If you need zero trust platform covering users, workloads, IoT, and partner access with browser isolation blocking paste and upload while allowing AI prompt interaction, Zscaler Zero Trust Exchange combines VPN, firewall, web, and AI controls in one console.
For enterprises spanning endpoint, cloud, and email channels needing behavior-based GenAI controls beyond simple URL blocking, Proofpoint DLP Transform uses same data classifiers across all channels with user behavior analytics providing actionable SOC intelligence.
GenAI security feels like it appeared overnight, except you’ve been dealing with the risks all along. Employees are pasting source code into ChatGPT, uploading customer data to Gemini, and training third-party AI models on your proprietary information. You need visibility into this activity and the ability to enforce policies without crushing productivity.
Where teams struggle is GenAI governance isn’t monolithic. You may need browser-level controls to stop data exfiltration, endpoint DLP to catch what users copy from files, behavioral monitoring to spot anomalies, or security testing if you’re building custom AI applications. Most organizations need multiple layers working together.
We evaluated 10 GenAI security solutions across browser controls, DLP platforms, zero trust approaches, and red teaming services. We examined how each handles shadow AI, policy enforcement, detection accuracy, and integration with existing security stacks. We also reviewed customer experiences to identify where implementations succeed and where they stumble.
Our Recommendations
Your choice depends on whether you need browser-only GenAI control, unified zero trust platform, or behavior-based DLP across channels.
- Best For Browser-Based GenAI Control: LayerX deploys as an extension across all major browsers with granular controls letting you block, warn, or restrict actions per application.
- Best For Unified Zero Trust and DLP: Zscaler Zero Trust Exchange provides unified platform covering users, workloads, IoT, and partner access with centralized policy management across VPN, firewall, web, and AI controls.
- Best For Behavior-Based GenAI DLP: Proofpoint DLP Transform combines content inspection with behavior-based GenAI controls going beyond simple URL blocking to content-aware policies.
- Best For Cloud-Native AI Governance: Palo Alto Networks AI Access Security , Anthropic’s Claude API safety features provide built-in content filtering and usage monitoring for organizations deploying generative AI.
- Best For Multi-Model AI Risk Management: Next DLP , Lakera provides runtime protection for multiple generative AI models with prompt injection detection and data exfiltration prevention.
LayerX
LayerX is a browser security platform focused on GenAI governance and shadow AI visibility. It deploys as an extension across Chrome, Edge, and other major browsers, giving security teams granular control over web-based activity without requiring endpoint agents. We think it’s one of the strongest options for organizations where browser-based data leakage is the primary concern.
LayerX Key Features
The GenAI controls go deeper than URL blocking. You can map sensitive data categories like source code, business plans, and customer records, then enforce policies specifically for AI applications. Pop-up warnings, full blocks, or selective restrictions are all configurable per app. The extension risk scoring is also strong; it identifies malicious browser add-ons that would slip past traditional security tools. LayerX has also recently introduced agentic browser protection, which distinguishes AI agent actions from human user actions in real time.
What Customers Say
Customers consistently praise the visibility into browser activity and the extension risk scoring. One security team identified risky Workday-related extensions that would have gone undetected with traditional tools. Day-to-day management gets good marks for being straightforward once deployed. Something to be aware of is that initial deployment through MDM can require custom scripting and technical effort upfront, depending on the environment.
Our Take
We were impressed by the granularity of the GenAI controls. If your primary risk is employees pasting sensitive data into ChatGPT or similar tools, LayerX delivers strong policy enforcement with minimal user disruption. It isn’t a full endpoint DLP replacement, but for browser-focused visibility and control, it’s well worth considering.
Strengths
- Extension-based deployment works across all major browsers with minimal friction
- Granular per-app GenAI controls for blocking, warning, or restricting actions
- Strong extension risk scoring for catching malicious browser add-ons
- Straightforward day-to-day policy management after initial setup
Cautions
- Reviews mention that MDM deployment requires custom scripting in some environments
- Not a full DLP replacement for endpoint-level data protection
Zscaler Zero Trust Exchange
Zscaler Zero Trust Exchange is an enterprise-grade zero trust platform covering users, workloads, IoT, and partner access. GenAI governance is one piece of a much larger security stack here, and it integrates naturally into the broader policy framework. We think it’s best suited for large organizations that need AI controls as part of a unified zero trust architecture rather than a standalone tool.
Zscaler Zero Trust Exchange Key Features
You can block AI sites outright, trigger user warnings, or get granular with ChatGPT-specific restrictions. Browser isolation adds another layer by letting users type prompts while blocking paste and upload actions. Cloud DLP extends across all applications, not just AI tools, and detailed logging shows which teams are using AI and what data is flowing through. Zscaler also now supports alignment with NIST AI RMF and EU AI Act frameworks, with CXO-level reporting on GenAI usage.
What Customers Say
Customers highlight the centralized management as a major win. Security teams can handle VPN policies, firewall insights, and web traffic from one console. Day-to-day usability gets praised as lightweight once everything is running. Something to be aware of is that initial deployment is complex and often requires consultant support, and some users note that automation options during setup are limited.
Our Take
We were impressed with the depth of integration across the platform. If you already run Zscaler infrastructure, the GenAI controls slot in without adding another vendor or console. If you need a standalone GenAI tool, this is overkill. But for enterprises building AI governance into broader network security, it delivers the visibility and policy enforcement your team needs.
Strengths
- Centralized policy management covers VPN, firewall, web, and AI controls in one console
- Browser isolation blocks paste and upload while still allowing prompt interaction
- Cloud DLP extends protection beyond GenAI to all web applications
- Supports NIST AI RMF and EU AI Act compliance frameworks
Cautions
- Customers note that initial deployment is complex and often requires consultant support
- Users report that setup automation options are limited compared to day-to-day management
Proofpoint DLP Transform
Proofpoint DLP Transform is an enterprise data loss prevention platform spanning endpoint, cloud, and email channels. It combines content inspection with user behavior analysis, and GenAI governance fits naturally into the broader DLP framework. We think it’s a strong option if you already run Proofpoint for email security, since the integration advantages are real.
Proofpoint DLP Transform Key Features
The behavior analysis adds real depth to GenAI controls. You can allow or block access to ChatGPT and Gemini based on what users are actually doing, not just blanket policies. Source code uploads and corporate data pasting get blocked before they reach AI chatbots. The data classifiers handle sensitive content identification across channels, so your GenAI policies use the same detection logic as your email and endpoint DLP. Proofpoint has also recently added DeepSeek to its list of protected GenAI sites.
What Customers Say
Customers consistently praise the visibility into user behavior. Security teams flag the intel as invaluable for investigations, especially when tracking sensitive outbound data. Deployment specialists get strong marks for being responsive and knowledgeable.
Our Take
We were impressed by the cross-channel consistency. The same classifiers working across email, endpoint, cloud, and GenAI applications means you aren’t maintaining separate rule sets for each channel. If you need standalone GenAI controls without broader DLP requirements, lighter tools exist. But for unified data protection, Proofpoint DLP Transform is well worth considering.
Strengths
- Behavior-based GenAI controls go beyond URL blocking to content-aware policies
- Same data classifiers work across email, endpoint, cloud, and AI channels
- User behavior analytics provide actionable intel for SOC investigations
- Responsive deployment specialists during rollout
Cautions
- Reviews flag that initial policy tuning requires significant effort before detection is accurate
- Insider risk configuration needs iterative refinement according to customer feedback
Palo Alto Networks AI Access Security
Palo Alto Networks AI Access Security is a cloud-based platform focused on GenAI monitoring and risk management. It extends PANW’s enterprise data security stack, requiring either NGFW or Prisma Access as the foundation. If you already run Palo Alto infrastructure, this slots in as a purpose-built GenAI governance layer with strong app coverage and granular user-level controls.
Palo Alto Networks AI Access Security Key Features
Risk assessments cover over 600 GenAI applications with compliance checks, giving you a real picture of shadow AI usage. User risk scores let you enforce policies at a granular level based on individual behavior patterns. The OpenAI API integration stands out; you can scan data at rest in ChatGPT Enterprise, including custom GPTs. End-user notifications through Slack, Teams, and email catch policy violations before they escalate. Palo Alto has also recently acquired Protect AI and announced intent to acquire Portkey, expanding the platform’s AI security capabilities significantly.
What Customers Say
Customers praise the visibility and threat mapping capabilities. Security teams highlight the AI-specific threat detection as a differentiator from general-purpose tools. The direct integration with existing PANW infrastructure gets positive marks for reducing deployment friction. Some users flag false positives as an issue that requires manual review to filter incorrect flags.
Our Take
We think this fits best if you already run Palo Alto firewalls or Prisma Access. The integration advantages are significant, and the 600+ app coverage gives you real visibility into shadow AI. Standalone buyers face a higher barrier since PANW infrastructure is a prerequisite. For existing customers wanting dedicated GenAI controls, it delivers strong visibility and granular policy enforcement.
Strengths
- Covers 600+ GenAI applications with compliance-based risk assessments
- API integration with ChatGPT Enterprise scans data at rest including custom GPTs
- User risk scoring enables granular policy enforcement based on individual behavior
- End-user notifications via Slack, Teams, and email catch violations in real time
Cautions
- Requires existing PANW NGFW or Prisma Access infrastructure to deploy
- Users report that false positive rates require manual review to filter incorrect flags
Next DLP
Next DLP’s Reveal Platform is an enterprise DLP solution covering endpoints, mobile devices, and cloud applications. It uses machine learning to classify data as it’s used rather than requiring upfront policy configuration, which cuts down on one of the biggest pain points with traditional DLP. The platform was acquired by Fortinet in 2024 and is now available as FortiDLP, with integration into Fortinet’s SASE stack underway.
Next DLP Key Features
The classification approach is refreshing. The platform identifies sensitive data through machine learning and anomaly detection rather than relying entirely on admin-defined rules. GenAI templates come preconfigured for ChatGPT, Gemini, Dall.E, and other popular tools. You can detect sensitive content like internal project names flowing into AI conversations. Clipboard controls block copy/paste of sensitive data in the browser, and incident-based training turns violations into teachable moments for employees.
What Customers Say
Customers consistently highlight ease of use. The console gets strong marks for being straightforward to manage without sacrificing control. Security teams appreciate getting visibility without disrupting end users. Cross-platform support covering Windows, Linux, and macOS gets positive mentions. Something to be aware of is that the Fortinet acquisition is now complete, and the product is transitioning to FortiDLP branding, which may affect future product direction.
Our Take
We think Next DLP fits organizations that want DLP capabilities without the typical policy complexity. The ML-based classification means you can deploy and get value faster than with traditional DLP tools that require extensive policy building upfront. If you need heavy customization, look elsewhere. But for teams prioritizing usability and faster time to value on GenAI controls, it’s a good option to consider.
Strengths
- ML-based classification reduces the need for complex admin-defined policies
- Preconfigured GenAI templates cover ChatGPT, Gemini, and other popular AI tools
- Cross-platform agent supports Windows, Linux, and macOS endpoints
- Incident-based training turns policy violations into employee learning moments
Cautions
- Fortinet acquisition complete; product transitioning to FortiDLP branding
- Customers note less customization depth compared to traditional enterprise DLP platforms
Harmonic Security
Harmonic Security is a startup built specifically for GenAI data protection. The platform uses pre-trained LLMs to let you define sensitive data in natural language rather than building traditional policies. If you want GenAI governance without the complexity of enterprise DLP, this is worth a look. The company was founded in 2023 by the team behind Digital Shadows and has raised $26M to date.
Harmonic Security Key Features
The natural language approach is really different from traditional DLP. You describe what sensitive data looks like in plain English, and the LLMs handle classification. No regex patterns, no data labeling projects, no policy trees to maintain. Visibility covers over 6,000 AI applications, plus shadow IT tracking and monitoring of third parties using your data for AI training. The platform also uses end-user nudging, which engages directly with users through context-specific interventions to guide safe AI usage.
What Customers Say
Customer feedback is limited given the 2023 launch. Early adopters note that the range of features can feel overwhelming initially. The platform packs a lot into its interface, which creates a learning curve despite the natural language approach to policies. The founding team brings credibility from the Digital Shadows acquisition, and RSA Innovation Sandbox recognition signals industry validation. But this is still a young product without the deployment track record of established DLP vendors.
Our Take
We think Harmonic fits organizations that want dedicated GenAI controls without layering onto existing DLP infrastructure. The natural language policy definition removes the traditional barriers to deployment that make most DLP projects slow and painful. If you need broader data protection beyond AI apps, look at full DLP platforms. But for speed to value on GenAI governance specifically, it’s well worth considering.
Strengths
- Natural language policy definition eliminates complex configuration and data labeling
- Tracks 6,000+ AI applications plus shadow IT and third-party AI training usage
- End-user nudging guides safe AI usage with context-specific interventions
- Purpose-built for GenAI rather than adapted from traditional DLP
Cautions
- Young startup without an extensive enterprise deployment track record
- Reviews mention that feature density can feel overwhelming during initial onboarding
HackerOne AI Red Teaming
HackerOne AI Red Teaming is a service that puts your AI systems through adversarial testing using a global community of security researchers. Rather than automated scanning, you get human testers probing for vulnerabilities, unintended behaviors, and exploitable weaknesses. If you build or deploy AI models, this helps validate security before problems hit production.
HackerOne AI Red Teaming Key Features
The researcher community approach is compelling for AI testing. Automated tools miss the creative attack paths that human researchers discover. HackerOne’s platform lets you define threat models, prioritize specific attack scenarios, and execute targeted testing against your AI systems. The service now supports classification against OWASP Top 10 for LLM Applications (2025) and OWASP Top 10 for Agentic Applications (2026), and reports can be mapped to NIST AI RMF, SOC 2, ISO 42001, and GDPR frameworks.
What Customers Say
Customers praise the quality and depth of findings. Security teams consistently note that researchers uncover issues that standard penetration tests miss. The platform builds trust between organizations and the hacker community through transparent engagement models. Something to be aware of is that triage response times can be slow. The platform also requires internal commitment to manage effectively.
Our Take
We think HackerOne AI Red Teaming fits organizations building AI applications that need rigorous security validation. If you just use third-party AI tools, governance platforms make more sense. For teams developing models or deploying custom AI systems, the human-driven adversarial testing catches what automated tools miss, which is a meaningful advantage.
Strengths
- Global researcher community finds creative vulnerabilities automated tools miss
- Supports OWASP Top 10 for LLMs and Agentic Applications classification
- Reports map to NIST AI RMF, SOC 2, ISO 42001, and GDPR frameworks
- Flexible engagement models from private programs to public disclosure
Cautions
- Users report that triage response times can slow researcher compensation workflows
- Requires internal commitment and program maturity to maximize value
Forcepoint One
Forcepoint One is a cloud-based Security Service Edge platform combining CASB, DLP, and Zero Trust Network Access in a single stack. GenAI governance is one use case within a broader enterprise security suite. If you need both access controls and data protection for AI applications, the integrated approach avoids stitching together separate tools. Forcepoint has recently rebranded the platform as Forcepoint Data Security Cloud, reflecting its evolution toward an AI-native data protection model.
Forcepoint One Key Features
The combination of DLP and ZTNA controls is effective for GenAI scenarios. You can limit which users and devices access AI applications while simultaneously controlling what data flows into them. Over 1,700 data classifiers provide granular detection, and copy/paste controls block sensitive content at the browser level. Data security posture management gives you visibility into where sensitive data lives and how it moves into GenAI applications. ZTNA policies can restrict access to approved AI tools based on user groups, device posture, or application risk.
What Customers Say
Customers highlight the unified interface as a major advantage. Managing multiple security services from one console reduces operational overhead. The platform gets strong marks for ease of initial setup and modern design. Advanced data search can run slow during complex queries, and integration options with third-party tools are more limited compared to some point solutions.
Our Take
We think Forcepoint One fits organizations evaluating SSE platforms who also need GenAI controls. The 1,700+ data classifiers and integrated ZTNA give you both access and data protection in one platform, which is good to see. If you only need AI governance, standalone tools cost less. But if SSE is already on your roadmap, the integrated capabilities handle GenAI without adding another vendor.
Strengths
- Integrated DLP and ZTNA controls manage data protection and access in one platform
- Over 1,700 data classifiers for fine-grained sensitive data detection
- Unified console reduces operational complexity across security services
- Modern interface and straightforward setup lower the learning curve
Cautions
- Reviews mention that advanced data search performance can lag during complex queries
- Customers note that third-party integration options are more limited than some point solutions
Darktrace ActiveAI Security Platform
Darktrace ActiveAI Security Platform uses self-learning AI to detect anomalous behavior across your network and respond autonomously to threats. The platform’s GenAI governance capabilities, now branded as Darktrace / SECURE AI, launched in February 2026, adding dedicated visibility and policy enforcement for AI application usage. We think the standout here is coverage for organizations building custom AI applications, not just consuming third-party tools.
Darktrace ActiveAI Security Platform Key Features
The behavioral approach is well-suited to GenAI monitoring. The platform learns normal patterns and flags suspicious activity that might indicate data loss incidents in AI applications. This catches novel threats that signature-based tools miss. Policy enforcement lets you control employee access to external GenAI tools with options to monitor, warn, or block by user group. Darktrace / SECURE AI also covers embedded SaaS AI, cloud-hosted models, and autonomous agents in both low-code and high-code development environments.
What Customers Say
Customers consistently praise the support experience. Customer success managers get strong marks for regular engagement, and the support team responds quickly to investigation requests. The email module in particular gets called out as one of the best AI-based filtering tools available. Some users mention that setup is complex and may need dedicated implementation effort, and some users note the interface design feels dated.
Our Take
We think Darktrace fits organizations that want AI-driven threat detection alongside GenAI governance. If you only need usage policies, lighter tools exist. But if behavioral anomaly detection across network, email, and AI applications appeals to your security model, the self-learning approach delivers strong detection without requiring predefined signatures, which is a meaningful advantage.
Strengths
- Self-learning AI detects anomalous GenAI usage without predefined signatures
- Covers external AI tool governance and internal custom AI application security
- Customer success management and support consistently praised as responsive
- Autonomous response capabilities shut down threats before they escalate
Cautions
- Reviews flag that initial setup is complex and may need dedicated implementation effort
- Users report the interface design feels dated and affects readability
Cisco Secure Access
Cisco Secure Access is a cloud-based SSE platform combining ZTNA, secure web gateway, CASB, and firewall services in a single console. GenAI governance is one layer within this broader security stack. If you already run Cisco infrastructure, the integration advantages compound quickly, and the multi-layered approach to AI controls is strong.
Cisco Secure Access Key Features
App discovery identifies which AI tools are in use across your organization with risk breakdowns and top user tracking. Web filtering lets you block, allow, or restrict access to approved corporate AI URLs only; Cisco added a dedicated Generative AI content category in February 2025 for web and DNS traffic. DLP policies add data protection on top of access controls. The code controls stand out here, preventing users from downloading ChatGPT-generated code or uploading proprietary code to AI tools. Cisco has also expanded AI Defense with AI BOM for centralized AI asset visibility, MCP Catalog, and real-time agentic guardrails.
What Customers Say
Customers praise the integration with existing Cisco infrastructure. Teams running Umbrella, Firepower, or other Cisco products get a unified view through Cloud Director. Support gets consistently strong marks, with direct engagement through assessment, design, pilot, and deployment phases. Something to be aware of is that geolocation issues have surfaced for some deployments outside the United States, and migration to the platform requires manual effort and planning.
Our Take
We think Cisco Secure Access makes the most sense for organizations already invested in Cisco networking and security. The code-specific controls for blocking AI-generated downloads and proprietary code uploads are a standout feature that not all competitors offer. Standalone buyers face steeper value justification, but existing Cisco customers get smooth integration with their current stack.
Strengths
- Multi-layered approach combines app discovery, web filtering, and DLP for GenAI control
- Code-specific controls block AI-generated downloads and proprietary code uploads
- Strong integration with existing Cisco infrastructure through Cloud Director
- Direct Cisco support through the full deployment lifecycle
Cautions
- Customers note geolocation issues for deployments outside the United States
- Reviews mention that migration to the platform requires manual effort and planning
What To Look For: GenAI Security Checklist
GenAI security spans multiple layers, and you likely need more than one tool. Here’s what matters when evaluating solutions:
- Application Coverage and Shadow AI Visibility: How many AI applications does the platform recognize? Can it identify shadow AI usage you don’t know about? Does it track third-party AI training on your data?
- Data Classification and Protection: Does the platform use machine learning to classify sensitive data, or does it require upfront policy configuration? Can it detect source code, trade secrets, customer data, and other categories? Does it work across endpoints, cloud, alongside browsers and email?
- Policy Enforcement Granularity: Can you allow or block access by user, department, device, or risk level? Can you allow ChatGPT access but block Gemini? Can you restrict actions like paste and upload at the browser level?
- Integration With Existing Stack: Does it integrate with your endpoint management, identity provider, or SIEM? Does it fit into your zero trust architecture or DLP ecosystem? Are APIs available for custom workflows?
- Ease of Deployment: Does it require extensive upfront policy work, or can you deploy templates and refine over time? Can you deploy via MDM? How much training do your team members need?
- User Impact and Adoption: Will enforcement friction cause users to work around policies? Can the platform enforce restrictions without frustrating productivity? Does it educate users when violations occur, or just block?
Weight these based on your risks. Organizations with high IP or customer data exposure should prioritize detection accuracy. Teams with compliance requirements should focus on audit logging and policy enforcement. Those building custom AI need adversarial testing and model security coverage.
How We Compared The Best GenAI Security Solutions
Expert Insights is an independent editorial team that evaluates GenAI security solutions. We do not accept payment for favorable reviews. Our scores reflect product quality only.
We evaluated 10 GenAI security tools across browser controls, DLP platforms, zero trust solutions, and red teaming services. We examined application coverage, policy enforcement granularity, alongside integration depth and ease of deployment. We evaluated how each handles shadow AI and data classification, plus violation detection.
Beyond hands on testing, we interviewed organizations using these tools and analyzed customer feedback to understand real-world deployment experiences. We examined how well platforms balance security enforcement with user productivity. We reviewed detection capabilities and the operational effort required for ongoing policy management.
This guide is updated quarterly. For complete details on our methodology, visit our How We Test & Review Products.
The Bottom Line
GenAI security isn’t monolithic. You likely need multiple layers, visibility into shadow AI, policy enforcement at the browser or endpoint, and possibly behavioral monitoring or red teaming.
For fast browser-level rollouts, LayerX blocks data exfiltration without endpoint overhead. Extension risk scoring catches malicious add-ons.
If you need DLP without policy complexity, Next DLP uses machine learning to classify sensitive data. Preconfigured GenAI templates deploy immediately.
For purpose-built GenAI controls, Harmonic Security replaces policy configuration with natural language. No regex patterns, no data labeling.
For large enterprises already committed to zero trust, Zscaler Zero Trust Exchange integrates GenAI governance into broader access controls.
If you build or deploy custom AI applications, HackerOne AI Red Teaming uses human researchers to find vulnerabilities automated tools miss.
Read the individual reviews above for deployment specifics, feature depth, and how each approach fits your security posture.
FAQs
Everything You Need To Know About GenAI Security Tools (FAQs)
There are several security challenges posed by generative AI:
- Visibility: Most enterprise apps are either governed by one central admin console or are only used by certain teams. GenAI can be used by everyone, across an organization, but has no centralized interface or controls for security teams to monitor and govern AI use.
- Data privacy: GenAI tools can be extremely helpful to end users. But there is nothing to stop them uploading sensitive data, and there have been public instances of PII (personally identifiable information), IP, source code, and customer data being uploaded to GenAI platforms.
- Compliance: Users may unknowingly be breaching compliance regulations if they are uploading documents or creating prompts containing customer data. This needs to be monitored by security teams. In addition, users may generate content, images, and video with GenAI tools that could breach copyright laws.
- Code generation: A common GenAI challenge is generated code. AI-generated code can include security vulnerabilities and weaknesses that must be resolved before being used in live production environments.
While some organizations may think it sensible to block the use of GenAI altogether, we wouldn’t recommend taking this step. There are many valuable use cases for AI in the business – and a ban is only likely to force users into using GenAI tools in a personal capacity for work-related tasks, pushing control out of reach of your security team.
Expert Insights’ CEO Craig MacAlpine recently outlined his 5 recommendations for companies looking to invest in a GenAI solution:
- Don’t attempt to block AI usage: Shutting down access to tools like ChatGPT, Google Gemini, or Microsoft Copilot is unlikely to stop employees from using them. Instead, they might use these tools on personal devices, leading to a loss of oversight. Work with your team to implement best practices and monitor usage effectively.
- Treat AI prompts as public: Once you upload data to AI tools, ensuring its security becomes challenging. With new AI technologies emerging frequently, it’s often unclear how they handle and publish collected data. A cautious approach is to treat all data passed onto an AI as if it were public information. For confidential or regulated info, consider investing in ChatGPT Enterprise, a SOC 2 compliant option that encrypts conversations and promises that data is not used for training.
- Provide user awareness training: Security awareness training (SAT) can help users safely interact with AI-generated content and tools. These training programs can cover security and privacy concerns and are often affordable. If you already use SAT, ask your provider if they offer content specific to generative AI.
- Use code checking tools: Research suggests that AI-generated code can introduce security risks, such as AI hallucinations and prompt injection attacks. If you’re using AI to generate code, consider implementing code monitoring plugins or application security testing tools to mitigate these risks.
- Consider AI DLP tools: For SMBs dealing with highly sensitive data, investing in AI data exposure tools might be worthwhile. These solutions monitor, map, and govern generative AI usage within the business, ensuring that important data remains secure. Although an investment, these tools can provide significant benefits in terms of data governance and security.
Written By
Joel is the Director of Content and a co-founder at Expert Insights; a rapidly growing media company focussed on covering cybersecurity solutions.
He’s an experienced journalist and editor with 8 years’ experience covering the cybersecurity space. He’s reviewed hundreds of cybersecurity solutions, interviewed hundreds of industry experts and produced dozens of industry reports read by thousands of CISOs and security professionals in topics like IAM, MFA, zero trust, email security, DevSecOps and more.
He also hosts the Expert Insights Podcast and co-writes the weekly newsletter, Decrypted. Joel is driven to share his team’s expertise with cybersecurity leaders to help them create more secure business foundations.
Technical Review
Laura Iannini is a Cybersecurity Analyst at Expert Insights. With deep cybersecurity knowledge and strong research skills, she leads Expert Insights’ product testing team, conducting thorough tests of product features and in-depth industry analysis to ensure that Expert Insights’ product reviews are definitive and insightful.
Laura also carries out wider analysis of vendor landscapes and industry trends to inform Expert Insights’ enterprise cybersecurity buyers’ guides, covering topics such as security awareness training, cloud backup and recovery, email security, and network monitoring. Prior to working at Expert Insights, Laura worked as a Senior Information Security Engineer at Constant Edge, where she tested cybersecurity solutions, carried out product demos, and provided high-quality ongoing technical support.
Laura holds a Bachelor’s degree in Cybersecurity from the University of West Florida.