Shadow AI refers to the use of artificial intelligence tools—especially generative AI systems—within an organization without the awareness, approval, or oversight of its IT, security, or governance teams. It is analogous to “shadow IT” (employees using unauthorized software or services), but with additional risk dimensions that come from how AI processes, stores, and outputs data.

Key Characteristics of Shadow AI

• Unmonitored usage: Employees, teams, or departments adopt AI tools (for example ChatGPT, code assistants, or AI plugins) without going through security review or formal governance.

• Blind spots: Because these tools are not on the organization’s sanctioned list, standard security controls (e.g. data loss prevention, audit logging) may not apply.

• Data exposure risk: Sensitive information (customer data, proprietary code, internal strategy docs) may be pasted into prompts or passed to AI tools that do not guarantee data protection or regulatory compliance. 

• No traceability: Without centralized logging or audit trails, it is difficult to tell who used what AI tool, when, or what data was shared.

• Propagation of errors or hallucinations: Outputs from unsanctioned AI tools can include hallucinated or incorrect content, which may then be reused, versioned, or integrated into downstream workflows.

Why Shadow AI is a Security Risk

1. Compliance and regulation
   Many industries must abide by strict data privacy, security, or confidentiality rules (e.g. GDPR, HIPAA, financial regulations). If sensitive data enters an AI model outside approved boundaries, the organization may violate compliance regimes and be held liable.

2. Intellectual property exposure
   If proprietary code, trade secrets, or internal logic is fed into AI tools that do not guarantee non-retention, those assets may leak or become accessible to external parties.

3. Attack surface expansion
   Shadow AI introduces new vectors for attackers. For example, malicious prompt injections, backdoors, or model manipulation become possible when the enterprise lacks visibility.

4. Lack of governance over model behavior
   Sanctioned models are typically vetted for bias, security, and behavior. Unsanctioned models may produce outputs that violate policy, mislead users, or spread disinformation.

5. Cascade of “shadow data”
   Even if the input was innocuous, the AI output (e.g. summaries, code snippets, analysis) often gets stored, shared, or integrated into enterprise systems. That output then becomes “shadow data”—it may not be subject to backup, classification, or DLP rules.

How organizations detect or mitigate Shadow AI

• Discovery & inventory: Scan networks, endpoints, browser plugins, developer environments to detect usage of AI tools outside approved ones (e.g. shadow browser extensions, AI APIs).

• Access gating: Restrict or block connections to external AI endpoints unless explicitly allowed.

• Data controls at the boundary: Use controls to inspect, classify, and possibly redact or block sensitive data before it is sent to any AI service.

• Policy and governance: Adopt an “acceptable AI usage policy” that delineates which AI tools are approved, under what conditions, and what data may or may not be shared.

• User education: Train employees about the risks of pasting internal or regulated data into third-party AI systems.

• Adaptive risk engines: Use systems like Acuvity’s Adaptive Risk Engine to continuously monitor AI use, detect anomalies or disallowed models, and enforce policy in real time.

Acuvity integrates Shadow AI Discovery as a feature of its Adaptive Risk Engine, allowing the system to surface AI use that lies outside the approved perimeter.

Yes. Code plugins (for example, AI-assisted development extensions, code autocomplete tools, or plugins that connect to AI models from IDEs) can introduce significant risk. Because these tools operate inside development environments, they often interface with internal data, project code, secrets, and repositories. Below are the ways code plugins can create risk, and how an adaptive risk engine can help manage them.

Risk pathways introduced by code plugins

1. Data exfiltration via prompts or context
   Many AI coding assistants send context around the developer’s environment—e.g. nearby code lines, comments, file contents, function signatures—as part of the prompt to the AI backend. If that context contains proprietary code or confidential logic, it may leak into the AI provider’s environment.

2. Membership leakage / inference attacks
   Suppose the plugin’s underlying model was trained on proprietary code or valuable internal data. Attackers may exploit membership inference attacks to find out whether a certain code snippet was in the model’s training data. A notable study shows that for code models, membership inference attacks can have high success rates. arXiv

3. Dependency on external model endpoints or runtime APIs
   Many plugins communicate with remote AI model endpoints or microservices. If those endpoints are compromised or malicious, they can act as exfiltration points for data inside the plugin or even push malicious code back to the user’s environment.

4. Supply chain and dependency risk
   Plugins often depend on open-source libraries or frameworks. If one of those dependencies is compromised, it can introduce vulnerabilities. Or if the plugin is malicious (or becomes malicious via update), it can stealthily exfiltrate or tamper with internal systems.

5. Unauthorized API calls / tool chaining
   A plugin might call into other services (databases, internal APIs, or microservices) to gather context or augment features. That can create cross-service data leaks if not constrained.

6. Elevated privileges / secrets exposure
   Plugins may access environment variables, credentials, or IDE secrets (e.g. API keys, tokens). If those are passed to the AI backend or stored insecurely, they become a vector for credential theft.

7. Behavioral drift or misuse
   As the plugin evolves, or if a user misuses it (intentionally or accidentally), it may begin doing things outside its original purpose—for example injecting code, making unauthorized changes, or executing dynamic queries.

How an adaptive risk engine can mitigate plugin risks

An adaptive risk engine (like Acuvity’s) can apply context, behavior, and policy in real time to reduce plugin risk.

• Real-time behavioral monitoring
  The engine tracks how the plugin is used: frequency, code submissions, file types, user identity, location, etc. It can flag anomalous uses (e.g., large code dumps, unusual file types) for review or automated intervention.

• Contextual inspection of plugin prompts and responses
  The engine inspects plugin prompt payloads, classifies sensitive content (e.g. proprietary code, PII, secrets), and may redact or block them before transmission to the AI model.

• Risk scoring and adaptive enforcement
  Based on user role, environment, historical behavior, and sensitivity of data, the engine assigns a risk score. If the risk is high, it can degrade functionality (e.g. disallow certain plugins, limit prompt size, require justification or approval).

• Provider and model trust calibration
  The engine can maintain trust scores for the AI model endpoint the plugin uses. If model risk increases (e.g. due to a compromise or changed policy), the engine can throttle or disable plugin access.

• Policy as code and dynamic policy engine
  Policies can stipulate what types of code or data may be used with plugins, whether secret keys can appear in prompts, or whether certain file directories are off limits. The dynamic policy engine enforces those rules adaptively.

• Audit and traceability
  Every plugin interaction is logged: prompt content (or sanitized form), user identity, timestamp, and enforcement action. This supports forensic investigation, compliance audits, and accountability.

Because code plugins operate at the intersection of development and AI, they are high leverage points for both productivity and risk. Without real-time safeguards, they can leak high-value intellectual property or secrets. An Adaptive Risk Engine helps create a controlled boundary between developer productivity and enterprise security.

“MCP” stands for Model Context Protocol—a protocol (or emerging standard) that connects AI agents or models to external data sources, tools, or services. (Sometimes called “agent tooling connectors,” “AI context plugins,” or “tooling APIs.”) When AI agents or models can query external APIs, databases, or services via MCP, that connectivity is a critical point of data flow — effectively a “gateway.” These gateways, if misconfigured or compromised, can become channels of data leakage.

Below I list principal types of MCP gateways and how they lead to leakage, plus how an adaptive risk engine helps defend them.

Common gateways to MCP data leakage

1. Internal databases or data stores
   AI agents may query internal databases (e.g. customer records, financial systems, HR systems) via MCP. If an agent requests large or unfiltered data, sensitive records (PII, IP, protected data) can flow out.

2. APIs and microservices
   Agents may call internal or external APIs via MCP. Uncontrolled parameterization or lack of filtering may expose sensitive fields, internal endpoints, or privileged API responses.

3. File storage systems / document stores
   Agents may access document repositories, file shares, or content stores. If permissions are too broad the agent might read files it should not, and exfiltrate them.

4. Third-party services or SaaS connectors
   Agents may integrate with external SaaS systems (e.g. CRM, analytics, cloud services). In that path, sensitive data can leak to external entities if connectors are granted excessive privileges or the external service is compromised.

5. Agent orchestration / tool chaining
   Agents may call other agents, or orchestrate workflows. This chaining can cause data to propagate across systems where policies are weaker, increasing the leakage surface.

6. MCP server misconfigurations
   The MCP server itself (the endpoint providing data access to agents) may lack proper authentication, authorization, rate limiting or input sanitization. A compromised or rogue MCP server can act as a wide open conduit. Acuvity+3arXiv+3Mend.io+3

7. Prompt injection via MCP
   An attacker might exploit prompt injection tricks to coerce the agent (via MCP) to fetch or return more data than intended. For example, a malicious prompt appended to a request may trick the agent into executing a broader query.

Why these gateways are high risk

• Agents often bypass usual application front ends, so leakage via MCP may skip conventional access controls or logging.

• MCP endpoints often operate in a more privileged context to support data access. That increases the potential impact if misuse occurs.

• Leakage through these gateways may be silent — internal logs might only see the initial API call, not the breadth or nature of returned data.

• Agents may retain or cache responses, or propagate them to other systems. Thus leakage is not only in-flight but persistently stored or shared.

How an adaptive risk engine defends against MCP leakage

Acuvity’s Adaptive Risk Engine plays a central role in protecting these gateways. Here’s how:

• Contextual risk scoring per MCP request
  For each agent request via MCP, the engine assesses context (which user initiated it, which model or agent, what the data classification is, the size and type of request) and assigns a risk score. If the score crosses a threshold, it can reduce or block the request.

• Data classification and redaction
  The engine inspects what data is being requested, and what would be returned. It classifies sensitive fields (PII, PHI, trade secrets) and may partially redact or filter the output, or disallow it entirely.

• Prompt sanitization / filtering
  The engine can intercept the prompt or query the agent is about to send to the MCP, check for suspicious patterns (like “give me all PII” or “dump entire DB”), and block or rewrite it.

• Access enforcement tied to identity and role
  The engine ensures only appropriate user/agent identities can invoke certain MCP endpoints or request certain data scopes. If an agent is misused, the engine can revoke permissions dynamically.

• Rate limiting and anomaly detection
  To prevent mass data extraction, the engine can throttle requests, detect anomalous frequency or volume, and raise alerts or block further calls.

• Trust scoring of MCP servers
  The engine maintains reputation or trust scores for MCP servers. If an MCP endpoint shows signs of misbehavior, elevated risk, or is newly introduced, it can be restricted or quarantined.

• Audit trails and logs
  Every MCP interaction (request, response, identity, timestamp, enforcement action) is logged for audit, forensic, and compliance purposes.

Because MCP is one of the critical interfaces connecting AI to data, securing those gateways is one of the highest-leverage defense moves. Adaptive, context-aware control over MCP requests helps prevent leakage even when agents or models try to overreach.

The Adaptive Risk Engine computes risk scores in real time by combining multiple signal types, continuously adjusting its assessment as context evolves. Its approach is not based on static rules alone. Instead it integrates behavioral, contextual, identity, and data-sensitive signals into a unified risk model. Below is a breakdown of how it works and what factors influence the score.

Signal categories and how they feed risk

1. User / agent identity signals

   • Role or job function

   • Prior behavior (historical usage, compliance history)

   • Device, location, authentication context

   • Trust level or alignment with known profiles

2. Prompt / input content signals

   • Classification of data in the prompt (does it contain sensitive or regulated fields?)

   • Length, structure, and complexity

   • Presence of suspicious keywords (e.g. “export,” “dump,” “all_records”)

   • Similarity to prior high-risk prompts

3. Model / provider signals

   • Trust rating of the AI provider or model

   • Known vulnerabilities, past incidents, data handling policies

   • Latency, anomaly, or drift in model responses

4. Agent or tool signals

   • Which agent or plugin is being used

   • Whether it has been whitelisted or sandboxed

   • The context in which it is operating (e.g. dev, test, prod)

5. Environmental / contextual signals

   • Time of day, location or IP zone

   • Network context (VPN, internal, external)

   • Concurrent sessions or conflicting accesses

   • Volume or rate of requests

6. Policy / compliance constraints

   • Regulatory limits on certain categories of data

   • Organizational policies (e.g. no PII in free model queries)

   • Escalation triggers (e.g. exceeding data thresholds)

Scoring model and adaptation

• The engine uses a weighted scoring model or machine learning model that ingests the above signals, producing a continuous risk score for each interaction.

• The score is dynamic: as more signals change (for example, a user escalates access, or the data being requested becomes more sensitive), the score is adjusted accordingly.

• Based on thresholds, the engine may react (block, redact, require justification, throttle) or allow the request.

Why dynamic scoring is important:

• Static rules are brittle: they fail when adversaries find novel patterns.

• A dynamic model adapts to evolving threat patterns, adjusting enforcement without manual rewriting.

• By combining signals, the engine reduces false positives—if a prompt looks risky in isolation but other signals (trusted user, low data sensitivity) are positive, the system can allow it rather than block outright.

Acuvity’s Adaptive Risk Engine continuously analyzes Gen AI interactions across users, agents, and applications, and dynamically adjusts control based on behavior, policy, and provider intelligence.

Once a computed risk score exceeds configured thresholds (which may differ by use case, user role, compliance regime, or policy tier), the Adaptive Risk Engine triggers one or more mitigation actions. The ability to respond in real time is central to its value: it doesn’t wait for human intervention if immediate risk is too high. Below is how response orchestration works.

Possible response actions

1. Blocking or rejecting the request
   The system may refuse to allow a particular AI interaction (prompt, plugin call, agent invocation) that is too risky.

2. Partial redaction or sanitization
   If only part of the data is risky, the engine may remove or mask sensitive parts (e.g. PII, credentials) while allowing benign content to pass.

3. Throttling or rate limiting
   The engine can slow down or limit the volume or frequency of requests from a user or agent to limit damage.

4. Requiring user justification or approval
   In some cases, the engine may prompt the user for justification or escalate to a manager or security team. If approved, the action may proceed under more oversight.

5. Session isolation or revocation
   The engine may isolate the session, revoke the user’s access temporarily, or require reauthentication.

6. Logging, alerting, and workflow triggers
   A high-risk event triggers audit logging, security alerts, or automated workflows (e.g. to SOC, compliance, or incident management systems).

7. Adaptive policy adjustment
   The engine may adjust policy parameters (e.g. future threshold tightening, provider trust adjustments) in response to observed threats.

How the engine ensures continuity and usability

• Actions are context-aware: low-risk actions may be allowed uninterrupted, while high-risk ones trigger enforcement.

• The system may degrade functionality rather than fully block in borderline cases (for example limiting prompt size or disallowing code upload).

• It maintains transparency and audit trails so that enforcement decisions can be reviewed, explained, or appealed.

• The responses can integrate with policy orchestration systems (e.g. a “Dynamic Policy Engine”) to align with enterprise governance. Acuvity’s Dynamic Policy Engine is designed precisely to enforce real-time controls based on the risk signals generated by ARE.

By combining detection, scoring, and enforcement in one feedback loop, Acuvity’s Adaptive Risk Engine aims to stop risky actions before they lead to data loss, compliance failure, or security breach—while preserving acceptable user utility on lower-risk interactions.

One of the distinguishing features of Acuvity’s Adaptive Risk Engine is its ability to evolve as usage patterns, threat vectors, AI models, or regulatory demands shift. Below are mechanisms and strategies by which the engine adapts, self-improves, and remains effective over time.

Mechanisms for adaptation

1. Feedback loops and learning from events
   The system feeds outcomes (e.g. incidents, false positives, user override events) back into its scoring and policy models to refine weights or thresholds.

2. Model retraining / signal recalibration
   As new attacks or patterns emerge (novel prompt injections, new plugin exploits), new features or signal models can be integrated and retrained.

3. Behavioral baseline recalculation
   The engine continuously learns “normal” for each user, agent, or domain, recalibrating profiles as usage evolves and adjusting what counts as anomalous.

4. Provider and model trust score updates
   If an AI model provider is found to have weak privacy guarantees or data incidents, its trust score is adjusted; the engine adapts by reducing reliance or access privileges.

5. Policy tuning and automation
   Organizations can adjust policy templates or thresholds. Over time, the system can recommend policy updates based on observed risk trends. Policy codification can evolve to incorporate new compliance rules or internal standards.

6. Threat intelligence integration
   The engine can ingest external threat feeds, vulnerability reports, and model-level exploit alerts to adapt its detection logic or enforcement rules.

7. Incremental deployment / phased rollouts
   The engine can start with alert-only mode or monitored mode, gradually enforcing stricter controls as confidence in its base behavior grows.

8. Continuous discovery of new AI usage paths
   As new AI tools, agents, or MCP endpoints come online, the engine tracks and assimilates them into its visibility scope, so it doesn’t remain blind to new gateways.

9. Anomaly detection for zero-day attacks
   Because the engine is not purely rule-based, it can detect deviations in pattern that may signal new exploitation techniques, even ones not yet codified.

Why adaptability is essential

• Threats evolve: attackers develop new prompt attacks, data exfiltration techniques, or model poisoning strategies. A static engine will become obsolete.

• AI models and capabilities evolve rapidly, so what was once safe may become risky (or vice versa).

• Usage patterns change: as users adopt new workflows, agents, or toolchains, the engine must adjust expectations.

• Regulatory and compliance regimes change. The engine must enforce updated rules without complete rewrites.

• Minimizing friction: over time, the engine tunes itself to reduce unnecessary false positives or overblocking.

Acuvity describes its Adaptive Risk Engine as “continuously evolving” and responsive to changing usage, threat environment, and regulatory landscapes.