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.

Transcend president: Privacy, regulatory management divergences will force ‘market bifurcation' March 17, 2026 Kate Parker, president at Transcend, which won the Legalweek Leaders in Tech Law Award for Data Privacy & Cybersecurity Innovation, talks about how the increasingly complex AI regulatory landscape is challenging AI development and adoption.