WitnessAI is building the guardrails that make AI safe, productive, and usable. Our platform allows enterprises to innovate and enjoy the power of generative AI, without losing control, privacy, or security. WitnessAI is a set of security microservices that can be deployed on premise in your environment, in a cloud sandbox, or your VPC, to ensure that your data and activity telemetry is separated from other customers. Unlike other AI governance solutions, WitnessAI provides regulatory segregation of your information. #AIGovernance #EnterpriseAI #SecureAI #GenerativeAI #AICompliance #DataPrivacy
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WitnessAI VP Products Trevor Welsh discusses Agentic AI. Watch the interview.
How can organizations effectively deploy, manage and govern AI agents? Trevor Welsh, VP of products at WitnessAI, breaks down the key challenges and solutions for AI governance in this Techstrong.ai interview. Watch here: ?? https://lnkd.in/d6eiHmtW #IT #AI #AIAgents #AIGovernance
Watch our latest customer success story! At InComm Payments, the goal was clear: enable employees to safely leverage AI to boost productivity while protecting sensitive data. With large databases containing personal health information, payment card data, and PII, InComm Payments needed a solution to monitor data flow and prevent data loss in AI applications. By implementing the WitnessAI Secure AI Enablement Platform, InComm Payments gained full visibility into AI activity, ensuring control, privacy, and security without compromising on innovation. Watch the full case study: https://lnkd.in/giSmSw9d #AI #DataSecurity #CustomerSuccess #GenerativeAI #TechInnovation #InCommPayments #WitnessAI
Read the full report here : https://lnkd.in/eAyBHtvp
Why MLSecOps is critical for AI security? Enterprises already have DevSecOps. But securing AI requires something different: MLSecOps. Machine Learning Security Operations (MLSecOps) is about baking security into every phase of the AI lifecycle. Why does this matter? Key reasons as a result: 1/ AI Engineers Work in Unsecured Environments Most AI research happens in Jupyter Notebooks, Databricks, and MLOps platforms—far outside traditional security perimeters. Security teams can’t enforce DevSecOps principles in environments where AI development happens informally. How do organizations bring security controls into the AI research phase? 2/ MLSecOps Requires Continuous Threat Modeling AI models aren’t static. They evolve based on new data, adversarial interactions, and real-world conditions. Security can’t just be a checkpoint before deployment—it must be a continuous feedback loop. What does AI-specific threat modeling look like inside an MLSecOps workflow? 3/ AI Pipelines Need Versioning & Rollback Protection When a traditional app gets hacked, security teams revert to a previous version. But what happens when an AI model is compromised? Rolling back isn’t that simple. Without model versioning, a poisoned model can go undetected for months. What frameworks exist to manage AI rollback & security validation? MLSecOps isn’t just a concept—it’s the next step for AI security maturity. See the link to the full research in the comments! Representative vendors who we use as case studies for our report, include, Palo Alto Networks HiddenLayer Protect AI Pillar Security Prompt Security WitnessAI Noma Security TrojAI Zenity. *** For the next post, we'll be looking more closely at 'The biggest risks in AI model development—data poisoning, backdoors, and model drift'. Follow Software Analyst Cyber Research for AI security insights and trends. #AI #AISecurity #Cybersecurity #CISO #MachineLearning
March Madness is here and we're bringing the action to you this week in New York! Join us on March 27th with Cyera, SentinelOne, and Silverfort for a night of basketball, networking with industry leaders, and the chance to win two tickets to the Elite Eight. ?? Register here to save your spot: https://lnkd.in/evjyQCYM
Insights on NVIDIA GTC from WitnessAI CEO Rick Caccia on the rapid pace of AI integration into applications. Read the full article: https://lnkd.in/ehfRbJ4c
AI chatbots are revolutionizing customer service, employee support, and more—but without a strong "identity," they can quickly go off-script, jeopardizing your brand, security, and compliance. In our latest blog post, Trevor Welsh, VP of Products at WitnessAI, dives deep into why defining and enforcing a clear "personality" for your AI chatbot is critical to maintaining brand consistency, security, and compliance. Why Does Your AI Need a Personality? ? Brand Consistency: Ensure your chatbot aligns with your company’s tone, values, and standards. ? Compliance: Avoid costly mistakes in regulated industries by controlling what your AI can and cannot discuss. ? Security: Protect your AI from malicious actors by enforcing clear boundaries in its behavior and responses. The Bottom Line Establishing a strong model identity isn’t just about improving user experience—it’s a fundamental element of secure, responsible AI. Check out the full blog post for insights on how to implement Model Identity Protection and safeguard your chatbot from becoming a liability. ?? https://lnkd.in/gX4wrm5D #AI #AIChatbots #ModelProtection #TechInnovation #Cybersecurity #WitnessAI #CISO #DigitalTransformation
Shadow AI is driving innovation as employees embrace AI tools to boost productivity, even in the face of IT restrictions. Discover how companies can harness this shift and ensure secure, responsible AI adoption in this insightful article from SiliconANGLE https://lnkd.in/gCrQafkq #AI #Innovation #TechTrends
Discover how tracking intent patterns over time reveals critical organizational behaviors before they become problems. The AI Usage Information Value Stack transforms scattered data points into actionable intelligence. https://bit.ly/3FlkMHP Learn to spot: - Shifting intent patterns in employee AI interactions - Correlation between query types and potential risk behaviors - Early warning signals that standard monitoring misses Join Trevor Welsh, VP of Products, WitnessAI and Sharat Ganesh, Head of Product Marketing, WitnessAI for "How AI Interactions Reveal Insider Threats" on March 18 at 10AM PST. See how leading organizations are already using these techniques to protect their most valuable assets. Register now for our live webinar:?https://bit.ly/3FlkMHP #CyberSecurity #InsiderThreatPrevention #AISecurityIntelligence
After organizations onboard AI products and copilots successfully, they often feel inspired to build their own and WitnessAI can help with that too. We provide an AI Detection and Response tool that’s able to detect jailbreaks, prompt injections, and can catch sensitive data being passed to LLMs. Our solution also has the ability to redact data instead of just simply blocking an entire query from going to an LLM to help ensure that more queries are usable. For a deeper dive into securing AI/LLMs in 2025, check out this insightful article: https://lnkd.in/dMuSQn44 #AI #DataPrivacy #Compliance #AItools #EnterpriseAI #TechSecurity #WitnessAI
Security Implications of Open vs. Closed Data in AI: A Deep Dive. Transparency is becoming not just a choice, but a necessity. AI models like DeepSeek-R1 and Open R1 present a pivotal crossroads: closed vs. open data. WitnessAI's Head of Security Engineering, Mathew Solnik, explores the security implications of these two approaches in his latest blog post. Ready to dive in? ?? Read the full blog https://lnkd.in/gKUY8krH and discover why openness is the key to building trust and security in AI. #AI #SecurityByDesign #OpenData #MachineLearning #AIethics #DeepSeekR1 #OpenR1 #DataTransparency #AITrust