GenAI Integration in Enterprise Security

An enterprise client went live with GenAI on AWS last quarter. Within 11 days, sensitive customer data appeared in model outputs. No guardrails. No owner. This isn’t an edge case. It’s the most common GenAI failure pattern I see in enterprises. Here’s what went wrong, and the control layer we added. The team did everything right strategically: Executive buy-in. Budget approved. AWS Bedrock live. Pilot defined. Model selected. Go-live shipped. What they skipped: runtime governance. There were: • No prompt injection controls • No input data classification • No tool-call audit trail • No separation between readable vs restricted data Governance was pushed to “after we ship.” In production, that moment never comes. So we built a 5-layer control stack. Layer 1 - Prompt Injection Detection Input validation before every call. Adversarial patterns blocked at the API layer. All attempts logged, not just successes. Layer 2 - Input Data Classification PII masked before reaching context. AWS Macie auto-classified document inputs. The model only sees what it needs. Layer 3 - Access Discipline Least-privilege IAM for every agent and tool. Scoped service accounts. Automated access reviews in CI/CD. Layer 4 - Tool Call Audit Trail Every tool call logged: who, what, result. LangSmith for live observability. Anomaly alerts for unusual behavior. Layer 5 - Runtime Guardrails AWS Bedrock Guardrails blocking defined categories. Output filtering before user delivery. Human-in-the-loop for uncertainty cases. Result: Same model. Same team. Same use case. From a data leak in 11 days → zero governance flags over 90 days. The technology wasn’t the issue. The missing control layer was. GenAI is scaling fast. Governance is still catching up. Most enterprises sit in between, unsure which layer they’re missing. The full governance audit framework is in the first comment. Question for teams deploying GenAI today: Who actually owns AI guardrails in your org - security, ML, ops… or no one yet? That answer reveals more risk than any architecture diagram.

One of the core themes I'm tracking closely (starting next month) is understanding the best solutions for preventing data exfiltration and the role that security for AI/LLMs companies will play in solving this issue for enterprises. I'm interested in seeing how the AI security category inflects this year in helping organizations prevent data leakage relative to other areas like data security (specifically data loss prevention (DLP)), which I wrote about last month. Let's explore the relationship between data security (DLP-focused) vendors relative to security for AI vendors for a moment. While there are many AI security vendors, I find it interesting to see what Prompt Security has built in and around preventing data leakage. The rise of ChatGPT and Microsoft 365 Copilot continues to transform how enterprises work—but it’s also exposing them to new data risks that legacy Data Loss Prevention (DLP) solutions weren’t built to handle. We've seen GenAI introduce dynamic risks around: - Shadow AI: Undetected tools used by employees. - Prompt Injection: Malicious manipulation of AI outputs. - Sensitive data leaks: Unintentional data exposure during AI interactions. What I'm seeing is that AI security companies like Prompt Security or others are managing this risk for organizations better in Gen-AI enterprise stack. Unlike legacy DLP / Data security vendors, they are showing better promise at: 1) Redacting sensitive data in real-time before it reaches GenAI tools. For example, we see better detection capabilities from pattern matching to contextual AI-based detection: for instance, DLPs like Zscaler can detect a social security number, but companies like Prompt can detect a corporate document with intellectual property better. 2) Better at detecting unauthorized AI tool usage (Shadow AI) across M365 AI tools, Github co-pilots and many more 3) Better at preventing AI-specific attacks like prompt injections. 4) These companies are able to surface educational popups so that employees or users are aware of when they're using an AI site or have violated the company AI policy 5) Full observability of AI usage and ensuring compliance. In general, AI security startups like prompt security (and a few others too) are showing they can dynamically adapt to the fluid, unstructured nature of data as it deals with GenAI interactions and take actions as needed with an agent or extension. In 2025, as more organizations embrace GenAI to stay competitive, data security is top of mind / foundational, so it'll be interesting to see how GenAI startups vs legacy DLP / data security vendors interact in this market. This is a trend to watch and I'll be uncovering this theme closely later next month!

Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents by Vineeth Sai Narajala and Om Narayan As generative AI (GenAI) agents become more common in enterprise settings, they introduce security challenges that differ significantly from those posed by traditional systems. These agents are not just LLMs; they reason, remember, and act, often with minimal human oversight. This paper introduces a comprehensive threat model tailored specifically for GenAI agents, focusing on how their autonomy, persistent memory access, complex reasoning, and tool integration create novel risks. This research work identifies 9 primary threats and organizes them across five key domains: cognitive architecture vulnerabilities, temporal persistence threats, operational execution vulnerabilities, trust boundary violations, and governance circumvention. These threats are not just theoretical they bring practical challenges such as delayed exploitability, cross-system propagation, cross system lateral movement, and subtle goal misalignments that are hard to detect with existing frameworks and standard approaches. To help address this, the research work present two complementary frameworks: ATFAA - Advanced Threat Framework for Autonomous AI Agents, which organizes agent-specific risks, and SHIELD, a framework proposing practical mitigation strategies designed to reduce enterprise exposure. While this work builds on existing work in LLM and AI security, the focus is squarely on what makes agents different and why those differences matter. Ultimately, this research argues that GenAI agents require a new lens for security. If we fail to adapt our threat models and defenses to account for their unique architecture and behavior, we risk turning a powerful new tool into a serious enterprise liability. #AI #safety #security

The OpenAI–Mixpanel breach(https://lnkd.in/giZuQ3mP) is a warning sign for every company using public SaaS and GenAI tools, not because of what leaked, but because of what it revealed. Last week, OpenAI confirmed that a breach at its analytics provider Mixpanel exposed user names, email addresses, and metadata, even though core data, API keys, and chat content were not affected. On the surface, it feels minor. But in security, metadata is rarely “just metadata.” It's identity. It’s behavior. It’s a map of who uses what, from where, and when. And in the GenAI era, it’s often the connective tissue between people, systems, and enterprise workflows. 👉 The real insight: As organizations integrate public SaaS + GenAI applications, their attack surface no longer stops at their own infrastructure. It now extends to every analytics script, plugin, browser extension, and third-party system stitched into their workflow. We’ve spent years hardening core data systems. But very few companies have hardened the data exhaust  telemetry, user metadata, prompts, logs, and behavioral signals that flow silently to vendors. This incident highlights three truths: 1️⃣ Data security must move upstream. We must classify and monitor every type of data, not just the obviously sensitive fields. 2️⃣ Vendor ecosystems are now part of your security perimeter. A single compromised SaaS vendor can create a breach path into hundreds of enterprises. 3️⃣ GenAI adoption amplifies the blast radius. Because AI systems rely heavily on prompts, analytics, and context, they naturally create more metadata than traditional apps and more opportunities for leakage. As someone building in the GenAI security space, I see this as a pivotal moment for our industry. AI is accelerating faster than governance practices are keeping up. We cannot keep treating SaaS telemetry as harmless or optional. It’s part of the risk model. The organizations that win with GenAI will be the ones that: 1. Know where their data flows 2. Understand what leaves their environment 3. Minimize what 3rd-party vendors can see 4. And embed security, visibility, and governance into every stage of their AI journey Breaches like this aren’t outliers, they are signals. Signals that the future of AI adoption must be paired with a new generation of security practices. Because the question isn’t just “How do we secure AI?” It’s “How do we secure everything that goes to AI ?” Good News: Concentric AI strong can help!

When asked to identify where generative AI capabilities are supporting security operations, practitioners most frequently cite automation via agentic AI (40%). Automating aspects of detection, analysis or response, including outside tool coordination and data retrieval, can streamline repeatable incident response tasks in chronically understaffed security operations centers (SOCs). A close second is using GenAI assistants to correlate current activity with past activity or known threat actor tactics, techniques and procedures (38%), a key part of threat hunting. The remainder of the top five responses highlight efforts to boost efficiency in some of the most complained-about, time-intensive SOC tasks: summarizing incidents in write-ups, automating reporting, and making remediation recommendations. The most damning finding in the SecOps study over the past few years remains the percentage of alerts that SecOps staff are aware of and simply cannot address due to a shortage of person-power. In the 2025 study, the average proportion is 45%, steady with 43% in 2024 and an improvement over 2023’s 54%. This number helps explain the enthusiastic response to capabilities that automate important but repeatable tasks, as outlined in the paragraph above: Any improvement that allows for added investigation of known anomalous or problematic activity is likely to be welcomed. While correlation should not be confused with causation, one cannot ignore that GenAI tool integration accompanied the 10-percentage-point drop in average unaddressed alerts between 2023 and 2024, after years of continual increase. In the 2025 study, generative AI assistants (44%) are the most commonly cited technology integrated into SIEM or security analytics, ahead of supplemental threat detection and response (38%) and threat intelligence tools (37%). As noted above, enterprises are applying GenAI capabilities for a variety of purposes, led by agentic automation.

OWASP GenAI Security Project – Solutions Reference Guide (Q2–Q3’25) OWASP has released its latest GenAI Security Solutions Reference Guide, a vendor-agnostic roadmap to secure Large Language Models (LLMs) and Agentic AI systems. 🔐 Highlights: - Extends the OWASP Top 10 for LLMs and Agentic Risks & Mitigations Taxonomy -Maps identified GenAI risks to practical open-source and commercial solutions -Defines a structured LLMOps & LLMSecOps lifecycle — covering planning, data handling, deployment, and monitoring -Introduces frameworks for Agentic AI security, red teaming methodologies, and emerging AI defense tools such as: √ LLM Firewalls √ AI Security Posture Management √ Guardrails & Policy Enforcement Systems #OWASP #GenAI #LLMSecurity #AIsecurity #Cybersecurity #ResponsibleAI

🔥Are you missing this Defender control for Generative AI apps?🔥 Block the Generative AI category in Defender for Cloud Apps According to a recent report, 77% of employees paste corporate data into GenAI apps. 82% of those pastes are into GenAI apps using personal accounts, exfiltrating corporate data. (https://lnkd.in/enP5QpBm) Microsoft Defender for Cloud Apps and Microsoft Defender for Endpoint can prevent users from accessing these GenAI sites. There is a category in Defender for Cloud Apps App Catalog for "Generative AI." You can create a Defender for Cloud apps App Discovery policy to block this entire category on your devices and still allow for approved GenAI apps. Create the policy with "Category equals Generative AI" and "App tag does not equal Sanctioned" and Governance Action "Tag as unsanctioned." When a user tries to visit an unsanctioned GenAI website on their corporate device, Defender for Endpoint will block their access. To allow a web app, mark it as Sanctioned in Defender for Cloud Apps. This configuration requires the device to be onboarded to Defender for Endpoint in Active Mode, Network Protection needs to be enabled on the device, and "Microsoft Defender for Endpoint Integration - Enforce app access" needs to be enabled in the Defender for Cloud Apps settings. #MicrosoftDefender #DefenderXDR #DefenderforCloudApps #GenerativeAI #AISecurity #CyberSecurity #M365Defender #DataSecurity #CloudSecurity #AIGovernance #ShadowIT #ZeroTrust

Our Verizon 2025 Mobile Security Index (MSI) reveals a storm is brewing in mobile security, with AI as the wind and human error as the rain. The Enterprise Risk is Clear: - Mobile attacks are up for 85% of organizations. But the game-changer is Generative AI (GenAI). - Expanded Attack Surface: 93% of your employees are using GenAI on their mobile devices for work. 64% of organizations see data compromise through GenAI as their top mobile risk. Smarter, Faster Threats: Cybercriminals are using GenAI to increase the volume and sophistication of attacks. 34% of organizations fear this will significantly increase their risk, especially with AI-powered ransomware. Security Gap: Only 17% of businesses have specific security controls against AI-assisted attacks. My message to business leaders is that you cannot rely on perimeter defense alone. Actionable Mitigation for Business Leaders: To mitigate this systemic risk, you must take a unified, multi-layered approach: - Train, Train, Train: Invest in continuous, relevant mobile and AI-specific risk training. - Secure the Tech Stack: Seamlessly integrate network and mobile security. A proactive, multi-layered approach is no longer a "best practice"—it's a business imperative for resilience. - Control AI Usage: Implement clear, well-enforced AI usage policies and leverage intelligent security solutions to manage the use of GenAI on mobile devices. We talk about a "perfect storm." It’s time to move beyond talking and make immediate, decisive investments to ensure business continuity. Read more about the Verizon 2025 Mobile Security Index (MSI) to understand the landscape and build your defense strategy. https://lnkd.in/eUvZTK_3 #MobileSecurity #Cybersecurity #EnterpriseSecurity Verizon Business #Verizon #GenAI #AI #5G #MSI

GenAI is exploding inside companies, and so are accidental data leaks From my CrowdStrike years I learned a hard truth: most security failures start as UX failures, unclear choices, over-permissive defaults, silent errors. ↳ In 2025 I keep seeing the same pattern in enterprise rollouts: ➤ Teams are running around 66 GenAI apps per org, with a handful in the high-risk bucket. GenAI now drives a meaningful share of DLP incidents and it is rising fast. The gap is not the model, the gap is the interface. Your interface is a security control. Design it like one. ↳ A human-centered GenAI safety checklist ➤ Data boundaries in the flow, show exactly what fields go to the model, give a quick “exclude sensitive data” toggle ➤ Identity awareness, display “You are acting as: Role,” enforce least-privilege on agent tools, add a “review before run” gate for high-impact actions ➤ Provenance by default, label AI-written content, show source files and last tool run, make “why this” explainer one click away ➤ Safe defaults, workspace knowledge off by default, paste-clean for PII, auto-redact on copy and export ➤ Injection hygiene, scan prompts and tool outputs, block on detection with clear, teachable microcopy ➤ Sandboxes and rate limits, ship agents in “safe mode,” cap actions until confidence and telemetry are proven ➤ Auditability in UI, “view logs” link near any agent action, show who did what, when, and with which data ➤ Consent that travels, per-feature “exclude from model training,” persistent across chat, docs, and dashboards ➤ Error states that help, explain what failed, what was protected, and how to complete the task safely ↳ How I implement this with product teams: ➤ Map workflows and stakeholders, where does sensitive data actually move ➤ Audit readiness, roles, data classes, and risk moments in the UI ➤ Scan tools and vendors, approve a short list with clear policies ➤ Trial small experiments, measure incident rate and task completion time ➤ Embed into operations, instrument everything, upgrade defaults, train humans ➤ Repeat and scale, retire what does not earn trust Useful over shiny is still the rule. Great UX is not just delight, it is defense in depth. Read the report by Palo Alto Networks and share it with your network. Follow Rose B. for human-centered AI, practical UX research.

Just got off a call with Duane Gran discussing about an interesting Gen AI security risk Duane Gran, a practical security leader, shared an eye-opening insight about how accidental security risks are surfacing with the rise of generative AI tools like CoPilot and ChatGPT. Here’s what’s happening in the industry: 1️⃣ IT buys a GenAI tool for productivity. 2️⃣ Employees unknowingly gain access to sensitive HR documents, like those stored in SharePoint, via CoPilot — documents they had access to before, but the power of AI search tools revealed gaps in the organization’s access control/permission settings. 3️⃣ Result: GenAI's magic improves productivity but inadvertently exposes confidential data, creating a security blind spot. This isn't about demonizing GenAI—it's amazing and transformative. But it has brought unintended consequences to light. The “Accident” Explained: GenAI indexes data repositories (like SharePoint) to provide helpful results. But without a strong implementation of least privilege, it could expose sensitive or confidential files to employees who shouldn’t have access. How to Fix It? 🔍 Implement data discovery and classification across all your repositories. 📁 Identify and label sensitive files, whether in SharePoint or elsewhere. 🚫 Create policies to ensure GenAI tools like CoPilot don’t index or display confidential information. Proactively managing data exposure with discovery, classification, and proper policies ensures the magic of GenAI stays productive without compromising security. 💡 What’s your take on balancing GenAI innovation with data security? #GenerativeAI #DataSecurity #CISOInsights #DataDiscovery #DLP