AI is no longer a future consideration—it’s already reshaping your Salesforce environment.
From Agentforce to Einstein GPT to a growing wave of third-party AI integrations, today’s Salesforce orgs are rapidly becoming intelligent, autonomous ecosystems. But while these technologies promise faster workflows and smarter decisions, they also introduce an exponential increase in risk—especially when it comes to data exposure, process breakages, and compliance failures.
The problem? These new risks aren’t entirely new—they’re amplifications of long-standing issues like bad data hygiene, poor access controls, and unmonitored integrations. In a typical Salesforce org with 50+ connected applications, most teams don’t have clear visibility into which apps are accessing sensitive data, what they’re extracting, and who’s responsible.
Today, every Salesforce org is connected to a constellation of tools: Slack AI, ZoomInfo, Gong, Salesforce Code Builder, meeting transcription tools, marketing automation platforms, and AI-powered analytics. These apps interact with your CRM via OAuth tokens, pulling and pushing sensitive customer and business data, often without centralized oversight.
According to Arovy’s internal analysis of hundreds of customers, the average org has 57 connected apps. That’s 57 separate data access points—many of which are not fully governed, logged, or even known to platform owners.
The implications are serious:
With tools like Agentforce, AI is no longer just querying Salesforce—it’s analyzing data, surfacing insights, writing to records, and even triggering workflows autonomously. That means a single misconfiguration can lead to wide-scale data leakage or even destructive automation.
Before AI, a misconfigured field or profile might result in minor workflow issues. But now? Those same missteps could result in:
Integrating AI agents and third-party apps into Salesforce introduces new layers of complexity—and significantly magnifies existing vulnerabilities. From data exposure to automation risks, these issues demand a proactive security strategy.
In the webinar, Jack and Brian outlined four core risk categories every team should prioritize as they scale AI and connected app usage across their Salesforce environment. Here's what to watch for:
AI models like Einstein GPT and other LLM-powered agents rely entirely on the quality of data in your Salesforce environment. If that data is inconsistent, outdated, or misclassified, the AI will amplify those issues—not fix them. What used to be a “back-end” problem buried in fields rarely used by end users is now front and center in automated decisions, summaries, and customer-facing recommendations.
What once was harmless noise in your org becomes high-stakes insight delivered at scale. Inaccurate records, duplicated values, or incomplete data models aren’t just technical debt—they’re risks when AI agents start making decisions on top of them.
These risks occur because:
AI’s promise of productivity leads to rapid experimentation—and often, new tools are connected to Salesforce without the knowledge of security or ops teams. These unsanctioned integrations, especially AI-enabled tools like summarizers, note-takers, or Chrome extensions, may start extracting data into off-platform storage, completely bypassing your internal governance workflows.
This “shadow AI” layer is extremely difficult to track without automated visibility into connected apps, OAuth token activity, and field-level data usage. This is critical because:
As AI tools grow more powerful, they also grow more invasive—requiring access to deeper layers of your CRM to deliver value. That means field-level governance , masking, and classification become urgent priorities, not long-term roadmap items.
Salesforce Shield Event Monitoring offers critical visibility into system activity, but without structured data classification and connected app monitoring dashboards layered on top, compliance teams are left without the context they need to take action.
Striking a balance between protecting sensitive data while making it useful is critical because:
Understanding the risks is only the beginning. With AI accelerating the pace of change—and expanding your risk surface—security teams need to evolve their Salesforce governance models to be real-time, adaptive, and automation-ready.
In the webinar, Jack emphasized that preparing your Salesforce org for safe, scalable AI adoption doesn’t require a complete overhaul. It’s not about boiling the ocean—it’s about starting with core security fundamentals that provide maximum visibility, control, and agility. These foundations allow your teams to embrace innovation without compromising integrity.
The foundation of AI security in Salesforce is knowing exactly which applications are connected, what data they can access, and how they’re behaving. This visibility should be constant—not limited to quarterly audits or manual exports.
Many teams are surprised to discover dozens of apps connected to Salesforce that were never formally reviewed or documented. As new AI tools are quickly adopted by business units, the number of access points—and potential risks—grows rapidly. Real-time visibility means:
AI doesn’t just amplify your workflows—it also amplifies your data exposure. That’s why understanding the sensitivity and purpose of every field in your Salesforce environment is now a critical requirement.
But manually tagging fields, maintaining a data dictionary, or chasing down context from different teams is time-consuming—and often inaccurate.
That’s where AI-assisted data classification comes in. Arovy uses intelligent automation to:
Visibility is the first step—but awareness without action doesn’t reduce risk. Once your connected apps and data classification are in place, you need a system for monitoring behavioral changes and flagging anomalies before they become incidents.
Salesforce Shield Event Monitoring gives you the raw data. Arovy turns it into actionable intelligence.
This includes:
As AI agents take on more autonomous roles inside Salesforce—writing to records, launching workflows, and handling sensitive customer data—it’s essential to establish governance controls specific to agent activity.
Treat AI agents like internal users: they need scoped access, behavioral monitoring, and lifecycle management. Without intentional design and oversight, agents can easily operate outside of your expectations—or worse, within permissions that haven’t been fully reviewed.
Arovy enables teams to:
AI is moving fast—and your security model needs to move faster. As connected apps and autonomous agents become foundational to how your teams sell, service, and operate, the risk of data exposure, process failure, and compliance violations scales with them.
But this isn’t a reason to slow down. It’s a reason to evolve your Salesforce governance strategy.
By combining Salesforce Shield Event Monitoring with Arovy’s purpose-built visibility, classification, and monitoring platform, you can confidently adopt AI while maintaining control over your most sensitive data and processes. From day-one discovery to long-term oversight, Arovy helps you secure what matters—without slowing your business down.