Shadow AI refers to unapproved or unsupervised AI use across tools and teams. It poses governance, compliance, and data security risks.
A structured 90-day shadow AI detection program enables organizations to move from blind spots to operational control without disrupting innovation.
The 90-day roadmap is split into three phases: discovery and baselining, control design, and operational integration.
Discovery focuses on identifying all AI touchpoints and establishing risk-based classifications, setting a factual foundation for future controls, audits, and regulatory review.
Control design turns insight into action by defining clear policies, integrating detection tooling, and setting up risk-based alerts tied to identity and data types.
Operational maturity embeds detection into governance workflows, with tuned reporting and KPIs to ensure ongoing oversight, executive visibility, and board readiness.
A 90-day shadow AI detection program provides security leaders with a realistic way to move from uncertainty to control without halting innovation. Shadow AI in enterprises refers to the use of artificial intelligence tools, features, or assistants that are deployed or used without formal approval, governance, or security oversight within an organization. This includes both standalone AI tools and embedded AI capabilities in existing platforms.
Many organizations, particularly those in rapid or decentralized AI adoption phases, already have some level of AI usage spread across assistants, browsers, SaaS platforms, and developer tools. Check out one of our other blog posts to learn about examples of shadow AI that commonly arise in everyday workflows. This roadmap treats shadow AI as a governance and risk problem first, not a tooling problem.
Industry surveys by Gartner, and regulatory briefings from the National Institute of Standards and Technology (NIST), increasingly highlight uncontrolled AI usage as a material governance risk, worse than previous shadow IT challenges due to AI’s ability to infer and recombine information. It urges for detection with executive oversight, audit readiness, and regulatory expectations.
Each phase of the roadmap builds on the previous one, reducing disruption while increasing confidence. The structure also mirrors how CISOs report progress to boards and regulators. By the end of 90 days, shadow AI detection becomes an operational capability rather than a one-time project.
The first goal of a shadow AI detection program is to discover all AI usage, including both approved and unapproved AI tools. Most organizations underestimate the number of AI touchpoints already in place across employees and teams.
The second goal is to classify risk in a consistent way that security, legal, and data governance teams can agree on. This approach mirrors how leading enterprises now respond to AI-related incidents, as without shared risk categories, shadow AI discussions remain subjective and slow.
Thirdly, aim to prioritize remediation based on impact rather than reactive industry narratives. This prevents blanket bans that drive AI usage further underground.
Lastly, we want to establish repeatable detection and governance processes that scale as AI adoption grows.
The phased approach divides shadow AI detection into discovery, control design, and operational maturity.
During days 1-30, the focus is on visibility and baselining rather than enforcement. This prevents early resistance and avoids false assumptions about usage patterns.
Days 31-60 build towards translating discovery into enforceable policies and technical controls. This is where AI assistant security and AI coding safety controls begin to take shape.
Days 61-90 focus on operationalizing detection into governance workflows, audits, and reporting. This phase is vital for board-level confidence and regulatory defensibility.
By separating phases, organizations avoid overengineering before they understand the problem.
The first 30 days establish the factual baseline for all future shadow AI decisions. This phase is about evidence, not assumptions or enforcement.
A post on Forbes about high-profile enterprise incidents clearly illustrates how quickly unmanaged AI use can lead to sensitive data exposure. This includes cases in which employees unintentionally shared proprietary source code and confidential information with public AI tools. For example, Samsung employees exposed proprietary source code to a public generative AI service, prompting an internal ban and board-level governance review. These events have accelerated board-level attention on shadow AI risk and reinforced the need for early discovery and baselining.
Many organizations discover that AI usage is broader than expected and often embedded in everyday workflows. Industry surveys consistently show that browser-based AI tools and extensions are among the most common forms of unmonitored AI use in enterprises, particularly among knowledge workers experimenting outside formal IT workflows. Early visibility also reveals where AI assistant security risks already exist, such as oversharing in chat tools. And developer environments frequently surface hidden AI coding safety risks through copilots and plug-ins.
This phase supports risk, governance, and compliance by creating an audit-ready inventory. Without this baseline, later controls lack credibility.
Inventorying AI touchpoints requires looking beyond obvious chatbots and copilots. This includes public browser-based AI tools, browser extensions, embedded AI features within SaaS platforms, developer coding assistants, and informal internal pilots or side projects. Browser-based public LLMs and extensions often represent the highest volume of unapproved usage. Industry research from Gartner shows that a significant share of employees access generative AI through web browsers and browser extensions outside approved enterprise controls, making browser-based AI one of the most common sources of shadow AI exposure.
The issue is further complicated by SaaS platforms which increasingly embed AI features that users activate without a security review. Developer tools and coding assistants introduce AI into source code workflows by default. Internal pilots and side projects frequently bypass formal approval because they start as experiments.
Each of these touchpoints creates different AI assistant security and AI coding safety risks. A complete inventory allows security teams to see patterns rather than isolated incidents.
Telemetry collection turns anecdotal concerns into measurable signals. Relevant telemetry sources typically include network-level visibility (such as proxy and firewall logs), endpoint or browser activity signals, and SaaS management data that exposes embedded AI features. Network logs from proxies, CASB, and firewalls reveal which AI domains are actively used. Endpoint and browser monitoring show how often AI tools are accessed and by whom. SaaS management platforms help identify embedded AI features that traditional security tools miss.
When combined, these sources provide context that single tools cannot. This telemetry supports risk, governance, and compliance by linking AI usage to identities and devices. Early telemetry also highlights where data may already be leaving controlled environments.
Risk classification brings structure to shadow AI conversations. Low-risk usage typically involves experimentation with non-sensitive data and minimal downstream impact. Medium-risk usage often includes internal data shared with AI tools that lack formal approval. High-risk usage involves regulated data such as personal, financial, legal, or source code assets. Clear categories help teams agree on priorities without emotional debate.
This classification directly supports executive reporting and regulatory oversight. It also sets the foundation for later AI assistant security and AI coding safety controls. Many organizations underestimate the business and regulatory impact of uncontrolled AI usage, which is why understanding common shadow AI risks is needed before defining enforcement thresholds.
The discovery checklist ensures that visibility gaps are explicitly acknowledged. Many organizations realize they cannot answer basic questions about AI usage with confidence. Knowing which teams access which AI endpoints is essential for accountability. Tagging regulated data and high-risk personas prevents the implementation of generic policies that fail in practice. Understanding where prompts and outputs are stored highlights hidden data retention risks.
This phase strengthens early alignment among risk, governance, and compliance. It also prepares the organization for later audit and DPIA discussions.
Days 31-60 should be used to convert insight into enforceable action. This is where shadow AI detection becomes a program rather than a report. Policies must reflect the real usage patterns discovered in phase one. Controls should support productivity while reducing unacceptable risk. At this stage, many organizations introduce a simple escalation and ownership flow that visually maps detection signals to response actions across security, IT, and data governance teams.
This phase introduces formal guardrails for AI assistant security and AI coding safety. It also connects shadow AI detection to broader governance and compliance frameworks. Integration matters more than perfection at this stage. The goal is consistent enforcement with clear ownership.
Defining policies starts with clarity on what counts as unapproved AI usage. Ambiguity creates friction and inconsistent enforcement. Policies should specify which AI tools and features are allowed, monitored, and restricted. Department-specific patterns help avoid one-size-fits-all rules. Engineering teams require different AI coding safety controls than HR or marketing teams. Clear definitions support escalation decisions during incidents. Well-written policies also enable board-level oversight and regulatory confidence.
Tooling should reinforce governance rather than replace it. Shadow AI tools must integrate with existing identity, security, and observability systems. Identity integration enables event attribution to real users and roles. SIEM and CASB integrations support centralized monitoring and response. Endpoint tools add context about devices and environments. Integration enables risk, governance, and compliance teams to share a standard view while avoiding fragmented tooling and duplicated alerts.
Detection rules translate policy into action. Flagging specific AI domains and plugins creates immediate visibility for risky usage. Detecting sensitive data uploads prevents silent data leakage. Alerts for new or unclassified AI tools highlight emerging risks early. Contextual alerts reduce noise and improve response quality. These signals directly support AI assistant security and AI coding safety objectives, and proper alert routing ensures accountability across security and data governance teams.
Controls related to AI assistant security primarily focus on conversational and search-based AI interactions, where oversharing and contextual inference pose the highest risk. AI coding safety controls, by contrast, emphasize developer tools and copilots, where source code exposure and intellectual property leakage are the dominant concerns.
The control design phase should be used to validate readiness for enforcement. This phase strengthens audit trails and executive reporting, while also preparing the organization for future AI risk simulations.
Risk-based rules ensure proportional responses rather than blanket blocks. Routing alerts to the right owners prevents delays and confusion. Well-defined escalation paths typically move from automated detection to security, or IT review, and finally to data governance or legal review for high-impact cases. Tying AI usage to users, devices, and data types supports insider risk and zero-trust validation. Clear escalation paths are essential for high-risk events.
Days 61–90 should see shadow AI detection transformed from a project into an operational security capability. At this stage, the organization should understand where AI is used and which risks matter most. Now, the focus shifts to stability, auditability, and executive confidence. Detection signals must be reliable enough to support governance decisions. Findings must feed directly into compliance, risk management, and oversight workflows.
This phase aligns shadow AI detection with the long-term AI governance strategy. It is also where CISOs gain the evidence needed for board and regulator discussions.
Signal tuning begins by reviewing 30 to 60 days of accumulated detection data. Duplicate alerts often surface from the same tools, users, or workflows. Consolidating these signals improves analyst efficiency and trust in the system. Genuinely low-risk usage can be safely whitelisted once it is understood and documented. High-risk tools and behaviors should be subject to tighter detection thresholds. Adding context through personas, data labels, and business functions improves accuracy.
This tuning phase strengthens AI assistant security and reduces alert fatigue without lowering protection.
Shadow AI detection must be fully integrated into formal governance processes to be effective. Detection findings should inform security reviews and internal risk assessments. DPIAs and AI risk registers benefit from objective evidence of use rather than assumptions. Observed behaviors help refine AI access control and AI usage controls over time. Training materials are more credible when they are based on real examples.
This integration supports risk, governance, and compliance objectives. It also ensures that shadow AI detection influences policy evolution rather than remaining isolated.
Operational maturity requires predictable AI monitoring and observability, along with consistent reporting rhythms.
Weekly triage ensures new shadow AI events are reviewed before they escalate. Monthly reporting provides CISOs and risk leaders with trend visibility. Quarterly summaries translate technical findings into board-level insights. This reporting cadence supports executive oversight and regulatory preparedness. It also demonstrates continuous improvement in AI governance maturity.
The run-state phase focuses on long-term risk management. Continuous monitoring ensures new AI tools and features do not bypass controls. Trend tracking reveals who is using AI, where, and with what data. Detection outputs must directly influence policy updates and user education. Audit-ready logs provide defensible records for investigations.
This phase supports insider risk and zero-trust validation, while also preparing the organization for future AI risk assessments and simulations.
Metrics turn shadow AI detection into a manageable program rather than a reactive effort. Well-defined KPIs help CISOs demonstrate progress to leadership. Metrics also reveal whether controls reduce risk or simply generate alerts, and help identify specific tools or departments requiring intervention. A balanced scorecard combines operational detection data with governance indicators.
These measurements support internal accountability and external audits. They also enable benchmarking over time. Perhaps most importantly, clear metrics strengthen confidence in an AI security strategy.
Together, the following KPIs support oversight of AI assistant security and AI coding safety. They also provide defensible metrics for executive reporting.
The number of new, unapproved AI tools detected each month indicates growth in exposure or effectiveness of control measures. Categorization rates reveal how quickly risks are understood and addressed. Tracking sensitive data incidents highlights real business impact. Mean time to detect (MTTD) reflects how quickly the organization becomes aware of new or risky shadow AI activity after it first occurs, often measured in hours or days depending on telemetry coverage. Mean time to remediate (MTTR), by contrast, measures how long it takes to take corrective action once an issue is detected, such as blocking a tool, updating a policy, or remediating data exposure, and typically spans days to weeks depending on governance complexity.
Comparing approved AI tools to those in use reveals policy alignment gaps. Coverage across high-risk departments indicates the maturity of detection. Training completion rates show whether awareness efforts are practical.
These metrics reflect risk, governance, and compliance health. They help identify where governance breaks down in practice. Over time, they support continuous improvement. Strong governance metrics build confidence among regulators and boards.
Organizations that want to accelerate early visibility often start with a purpose-built solution that focuses on discovery and governance at the knowledge layer rather than file-level inspection. In the discovery and baselining phase (days 1-30), Knostic supports organizations by revealing how AI assistants and enterprise search tools infer and surface knowledge across users and data sources. During control design on days 31-60, this visibility provides the policy context needed to define risk-based controls aligned with personas, data sensitivity, and governance requirements. In the operational phase from days 61-90, Knostic-generated evidence feeds directly into audits, reporting, and ongoing governance workflows.
Knostic supports shadow AI detection by focusing on the knowledge layer, auditing prompts, retrieval paths, and generated outputs, so you catch oversharing and policy violations that traditional CASB and DLP tools miss. These legacy controls struggle to detect when AI infers and recombines information during generation. Knostic complements them rather than replacing them, to deliver practical, audit-ready AI governance.
The platform distinguishes between retrieval and generation events, applies identity-aware usage controls that factor in roles, permissions, and need-to-know, and produces explainable audit logs that show who accessed what and how it was inferred. This evidence strengthens AI assistant security, accelerates safe adoption, and directly supports the telemetry, policy, and audit needs outlined in the 90-day plan.
Knostic embeds governance into detection: policies tie to labels, personas, and defined risk tiers, and every AI interaction is logged automatically for audits, investigations, and compliance. Governance becomes continuous rather than periodic, reducing the overhead of manual review.
Incremental Adoption is easy: start with one assistant or team, then expand to coding tools, enterprise search, and copilots. Knostic provides evidence that supports audits and DPIAs, and consistent metrics to simplify board reporting, enabling scale without losing visibility or control.
A structured shadow AI detection program can be established in about 90 days, from initial discovery through operational governance. Preliminary visibility is often achieved within the first 30 days.
You need visibility into AI usage, identity context, and data access, typically combining existing security tooling with a dedicated knowledge-layer governance platform like Knostic. Traditional DLP or CASB tools alone are not sufficient to detect inferred AI exposure.
The primary risk is uncontrolled exposure of sensitive or regulated knowledge through AI assistants and search tools. This can lead to compliance violations, intellectual property leakage, and loss of executive trust in AI adoption.