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Transcend introduces Agentic Assist and MCP Server. New products by Transcend make it easier than ever for privacy teams to automate compliance workflows, reducing days of manual work to minutes of guided review. Transcend, the compliance layer for customer data powering global companies, has announced Agentic Assist and the Transcend MCP Server, bringing agentic AI capabilities directly into enterprise governance workflows. The company will demonstrate both live at the IAPP's Global Summit in Washington, DC. Enterprise AI adoption is accelerating faster than the compliance programs designed to govern it. Gartner estimates that enterprise applications with task-specific agents will increase 8x by the end of 2026, but warns that 40% of agentic AI projects risk cancellation without governance, observability, and ROI clarity. For CIOs and Chief Privacy Officers alike, the implication is clear: compliance teams need agentic tools purpose-built for privacy and governance. Agentic Assist is an AI assistant built into Transcend that draws on the platform's existing knowledge of an organisation's data footprint - systems, data flows, consent preferences, and processing activities - to automate compliance tasks that today require hours of manual effort. Where complex assessments once took days, Agentic Assist prepopulates them in seconds, reducing the process to a single review cycle in preliminary testing. Unlike AI agents retrofitted onto compliance platforms, Agentic Assist is built on Transcend's integration layer, meaning it already knows an organisation's systems, data flows, and workflows. That context is the difference between an agent that can complete assessments, triage cookies, and fulfil DSRs, and one that just generates recommendations your team still has to execute manually. The Transcend MCP Server lets teams administer Transcend directly from the AI tools they already use, whether that's Copilot, Claude, ChatGPT, Gemini, or Cursor. Instead of switching into the Transcend dashboard, teams can initiate data subject requests, run assessments, and manage consent configurations from within their existing agentic workflows. Taken together, Agentic Assist and the Transcend MCP Server allow teams to deploy agentic automation across the full lifecycle of customer data compliance: * Triage and classify cookies in real time: Surface and categorise uncategorized cookies and trackers across domains, flag unknowns, assign confidence levels, and push updated consent configurations live, all from a chat interface that can do it at 5x the pace. * Compile assessments in minutes, not days: Pre-populate privacy impact assessments and vendor risk assessments from existing knowledge, flag sensitive data by risk level and regulatory relevance, and complete assessments through natural language conversation. * Keep preference data in sync more easily: Query preference records, surface cross-system anomalies, and get agent-guided configuration for downstream systems like Braze and Salesforce, all through natural conversation. * Keep DSR fulfilment running at scale: Surface workflow failures, diagnose integration errors, and guide remediation, or build and modify automation workflows through natural language - without opening an engineering ticket. * Get regulatory guidance in the flow of work: Surface context-specific regulatory guidance to inform compliance decision-making without leaving Transcend. Both products are built with enterprise-grade controls. Agentic Assist operates within each customer's Transcend instance with no cross-tenant data sharing, and AI capabilities can be disabled at any time. The MCP server requires user authentication, and every tool call runs within the organisation's own environment, limiting agents to actions the server explicitly exposes. "The rapidly accelerating pace of technology is causing pileups in the privacy office, yet privacy teams are hungry to amplify critical business initiatives like AI adoption. Today, we're giving privacy professionals the tools to scale their work," said Ben Brook, CEO and Co-Founder of Transcend. "Across our customers, we see this consistently: once privacy teams get on top of their backlogs, they become more strategic partners in the business - engaging in technology strategy more deeply, and going from bottleneck to enabler." Aimee Cardwell, CIO/CISO in Residence at Transcend, said, "Every CIO I talk to wants their teams moving faster, but until now privacy and governance haven't had the tooling to keep up." "Agentic Assist and the MCP Server change that calculus - teams get the full upside of AI-driven efficiency without giving up the control they need. There's no longer a tradeoff between working smarter and working safely." The Martechvibe team works with a staff of in-house writers, and industry experts. View More

Transcend has launched Agentic Assist and the Transcend MCP Server, bringing AI-powered automation to enterprise compliance workflows. The tools will be demonstrated at the IAPP Global Summit in Washington, DC, from 30–31 March. Agentic Assist is an AI assistant that automates compliance tasks by drawing on an organisation's existing data footprint. It can prepopulate complex assessments in seconds, triage cookies, fulfil data subject requests and compile privacy impact assessments. The MCP Server allows teams to administer Transcend directly from AI tools like Copilot, Claude and ChatGPT. Both products feature enterprise-grade controls, with Agentic Assist operating within each customer's instance and no cross-tenant data sharing. The tools begin rolling out to customers in April.

Runtime governance for AI Agents is finally happening. Here's the layer everyone is still missing. Enterprises are adding privacy controls and access governance to live AI agents. But authenticated agents still fail when they retrieve stale, contradictory knowledge. AI Agents Enterprise AI Knowledge Governance Privacy Agentic AI Three separate enterprise governance stories broke this week. Taken individually, each is a product announcement. Taken together, they're a signal that the industry is finally getting serious about something that's been quietly accumulating risk for two years. Privacy and data governance are moving into the live runtime of AI agents. Not the audit log. Not the quarterly review. The moment the agent acts. That's the right move. It's also, on its own, not enough. What happened this week. On March 30, Transcend launched Agentic Assist alongside an MCP server, adding what it describes as real-time privacy, consent, and data-access controls directly into the agent execution layer. The same day, Major League Soccer announced a league-wide collaboration with DataGrail to roll out AI-driven privacy and governance across all clubs - a large, federated, consumer-data-heavy organization making a bet on this category. Five days earlier, BigID announced the expansion of its Data Access Governance capabilities to cover AI agents explicitly. The framing from BigID's CPO was direct: "Agents are now first-class data consumers, and they're operating at a scale and speed that makes traditional review cycles irrelevant." This week also brought a relevant data point from LexisNexis, whose Future of Work 2026 report surveyed 1,400 professionals across 20 industries. 51% of organizations say they've launched internal AI agents. Only 44% of their employees clearly understand what those agents are or how they work. 53% of professionals report using genAI without formal approval. 28% work at companies with no formal AI policy at all. That gap between deployment speed and governance maturity is exactly what Transcend, BigID, and DataGrail are building into. Why this matters: governance is moving to where decisions happen. For most of the last two years, enterprise AI governance has been a retrospective function. You ran a model, something went wrong, you reviewed the logs. Policy lived in documents nobody read. Compliance lived in annual reviews nobody changed anything from. What's shifting now is the enforcement point. The access controls, consent checks, and data-lineage requirements are being pushed into the actual execution layer - the moment an agent decides to retrieve a record, browse a system, or write to a database. That's a different design philosophy, and it matters. BigID's framing captures why: enterprise governance was built for humans. Employees review requests, wait for approval, get access revoked when they leave. AI agents don't leave. They don't tire. They operate continuously, at machine speed, across systems that cross organizational boundaries, often with permissions set months ago by someone who no longer works there. Traditional review cycles don't fit this operating model. Runtime governance - with least-privilege access, real-time activity monitoring, and consent enforcement baked in - is the response. It's genuinely better than what came before. The three layers enterprises actually need. Here's where the current market conversation is leaving a gap. Most coverage of agent governance stops at two layers. Enterprises need three. Layer 1: tool and runtime governance. This is what Transcend is building - controlling what actions an agent can take, what tools it can invoke, what data it can touch. Privacy, consent, and data-lineage requirements enforced at the moment of execution, not reviewed afterward. Layer 2: identity and access governance. This is BigID's play - treating agents as non-human identities with their own access profiles, least-privilege scopes, and real-time activity monitoring. Discovery of what agents exist, what they're touching, whether their permissions are still appropriate. Both layers are necessary. Neither is sufficient. Layer 3: Knowledge governance. This is the one nobody in this week's coverage is talking about. Runtime governance controls what an agent can access. It says nothing about whether what it retrieves is accurate. An agent can be perfectly authenticated, operating within its least-privilege scope, with every action logged and traceable - and still produce bad outcomes because the document it retrieved was outdated, or conflicted with three other documents it found in the same knowledge base. This problem isn't theoretical. Enterprise knowledge bases accumulate stale content by default. A policy document gets updated in one folder but not another. A support article describes a product feature that was deprecated six months ago. An internal SOP says one thing; a compliance document says the opposite. Nobody noticed because the documents sit static and the conflicts don't surface until an agent reads both and tries to act. As we've written before, AI agents retrieving stale documents don't just give wrong answers - they take wrong actions. That's a meaningfully different risk profile than a chatbot hallucinating in a consumer app. What a complete agentic governance stack looks like. An agent can be perfectly credentialed and still fail. The credential problem and the knowledge problem are separate - they require separate solutions. The knowledge governance layer means: * Source attribution on every retrieval. Agents should be able to surface not just what they found, but exactly where it came from and when it was last verified. This is what makes agent outputs auditable, not just logged. * Contradiction detection across the knowledge base. Before agents operate on content, the content should be checked for internal conflicts. Two documents giving different answers to the same question should surface as a governance issue, not get silently arbitrated by the model. * Freshness controls. Documents past a defined threshold should trigger a review queue before they're available for retrieval. Not because the information is necessarily wrong, but because you can't assume it's still right. * Permission-aware retrieval. The knowledge base itself should respect access boundaries - an agent operating in the customer support context shouldn't retrieve internal pricing memos, regardless of what its access token technically allows. Platforms like Mojar AI are built around this layer: RAG retrieval that's source-attributed, paired with active knowledge base maintenance that scans for contradictions, flags stale content, and can update documents through natural-language instructions. The design assumption is that the retrieved knowledge needs to be governed, not just the retrieval act. This isn't a replacement for what BigID and Transcend are building. It's the complement. You need all three layers. Runtime controls, identity governance, and knowledge accuracy - and the third one currently has the least vendor investment and the least enterprise attention. The LexisNexis data makes the urgency clear. Organizations are already running internal agents at scale. Less than half of employees understand what those agents do. If those agents are operating on ungoverned knowledge - stale SOPs, conflicting policies, outdated support content - then access controls and activity monitoring are catching the wrong failure modes. What enterprises should do next. Before expanding agent deployments, the practical steps aren't complicated - they're just not what most governance checklists currently include: * Map the knowledge domains your agents will retrieve from, and assess their accuracy and freshness * Run a contradiction audit across high-stakes document sets (compliance, policy, support content) * Define freshness thresholds - how old is too old for an agent to act on without human verification? * Ensure retrieval is source-attributed so agent outputs can be traced to specific document versions * Layer runtime controls and identity governance on top of a clean knowledge foundation, not instead of one The agent governance category is finally getting the market attention it needed. Runtime enforcement, least-privilege access, real-time monitoring - all of this is the right direction. The next step is making sure the knowledge those agents read is held to the same standard as the access they're given. An authenticated agent reading an outdated policy doesn't just give a bad answer. It takes a bad action. That's the governance problem still waiting for most enterprises to catch up with. Frequently asked questions. Runtime governance for AI agents means enforcing access controls, privacy policies, and monitoring at the moment agents operate - not as a post-hoc audit. It covers what data agents can reach, which tools they can use, and whether their actions are logged and traceable to specific policies. Access control determines what an agent can retrieve. It doesn't determine whether what's retrieved is current, accurate, or non-contradictory. An agent can be perfectly authenticated and pull confidently from documents that are outdated or internally conflicting. Knowledge governance ensures that the documents and content AI agents retrieve are accurate, current, source-attributed, and free of contradictions. It's the layer that sits alongside access control to ensure that what agents are allowed to read is also trustworthy.