Why Continuous Monitoring Isn’t Optional in Healthcare and Fintech SaaS Security

Healthcare organizations remained prime targets for cybercriminals last year, with millions of patient records exposed through ransomware and hacking incidents that continue to outpace defenses. 

In March alone, more than 1.5 million individuals’ protected health information was compromised across 44 reported breaches, the vast majority driven by hacking and IT incidents — underscoring how attackers exploit weak internal safeguards and third-party integrations. 

High-impact cases — including ransomware-linked breaches at major providers and business associates affecting millions of patients — demonstrate that perimeter security isn’t enough when adversaries pivot to exploited applications and vendor relationships. 

This persistent structural gap in protecting user, data, and integration relationships inside SaaS and cloud environments is what enables ransomware and data exfiltration to keep healthcare on the front lines of cybersecurity risk in 2026. 

The Security Mirage: What Organizations Think Protects Them

Most regulated organizations that claim adequate cloud posture management point to three layers of defense.

Strong IaaS posture: Mature CSPM on AWS, Azure, and GCP, with hardened infrastructure, network segmentation, and continuous configuration scanning.

Access and traffic controls: SSO and MFA everywhere, VPN or ZTNA, DLP, and a CASB watching user traffic to sanctioned applications.

Periodic SaaS reviews: Annual or quarterly security and configuration audits for systems of record like Microsoft 365, Google Workspace, Salesforce, or core banking platforms.

On paper, this looks robust. It maps cleanly to HIPAA, PCI DSS, SOC 2, and ISO 27001 checklists.

The problem is that inside the SaaS layer, a very different reality exists.

Where Risk Actually Lives: Inside the SaaS Stack

Misconfigurations and permission drift create the largest exposure surface. Sharing defaults, external collaboration settings, public links, and logging configurations often misalign with internal policies. Between audits, these settings drift, and that drift drives a large share of SaaS incidents.

Over-privileged and dormant accounts persist long after they’re needed. Service accounts, legacy admins, and contractors retain broad access to PII and PCI data, fueling breach impact and insider risk.

Shadow and mirror SaaS proliferates when clinicians, claims teams, and traders adopt unsanctioned tools or use personal identities in sanctioned applications. Regulated data lands in completely unmanaged contexts.

These risks sit inside approved SaaS platforms. They’re rarely visible to tools focused on network paths rather than application posture.

The Structural Blind Spot

Security architecture stops at “who can log in” instead of asking “what can they do, with which data, through which integrations, and from which applications?”

CSPM versus SaaS posture: CSPM assumes that once you’re inside the SaaS application, the app is configured safely. In practice, configuration and data-sharing controls inside Microsoft 365, Google Workspace, Salesforce, and Slack are where many breaches originate.

CASB scope versus OAuth reality: CASB sees traffic it can proxy. But in 2025, a huge percentage of risk comes from API-based OAuth and SaaS-to-SaaS integrations that communicate directly with each other, with no inline control point. Studies show 92% of organizations experienced at least one API-related security incident last year.

Point-in-time audits versus continuous evidence: Annual security reviews create a posture snapshot. But misconfigurations, new third-party applications, and new data flows appear daily, leaving teams confident on paper and exposed in practice.

For healthcare, PII quietly flows into unsanctioned AI tools, scheduling platforms, and browser extensions. For fintech, PCI or trading data gets exposed via over-permissive integrations and poorly governed external sharing.

The Timeline Problem: From Misconfiguration to Material Incident

In healthcare SaaS environments, the timeline from misconfiguration to material incident is usually measured in weeks to years, not hours.

The misconfiguration often looks harmless when created. It only becomes visible once regulators, patients, or threat actors force you to notice.

Day 0: Misconfiguration Introduced

Someone approves a broad-scope OAuth application with permissions like “read all CRM data” or “access all mailboxes.” Or they loosen sharing defaults to “anyone with the link.”

In many healthcare organizations, this happens when growth, marketing, or other workflow teams connect analytics, outreach, or AI tooling to PII-adjacent systems.

Days to Years: Quiet Exposure Window

For misconfigured sharing, data becomes reachable to anyone with the link, search engines, or scraping tools. No alarms fire because no policy watches SaaS object-level exposure.

For over-permissioned OAuth, the connected application or its tokens can now exfiltrate large data sets or pivot into other systems, bypassing MFA and most perimeter controls.

This window can be extremely long. The Blue Shield breach ran for almost three years before discovery.

In the Drift-Salesforce OAuth supply-chain attack, stolen tokens were used over at least 10 days to quietly pull CRM data and credentials at scale.

Detection: Often Months After First Exposure

Common detection triggers in healthcare include external notification from a vendor, regulator, threat intelligence provider, or law enforcement. Anomalies in logs—bulk exports, unusual API volume, or access from unexpected locations—finally get noticed during periodic audits, not in near-real-time.

Sometimes patients or partners complain after seeing targeted ads tied to medical conditions or finding their data exposed.

At an industry level, the average time to identify and contain cloud-driven breaches remains approximately eight months.

Materialization Into Reportable Incident

Once detection occurs, legal and compliance teams determine whether PII was accessed, viewed, or exfiltrated in a way that triggers breach-notification rules.

The incident is material in three ways: regulatory (mandatory notifications, OCR scrutiny, potential HIPAA penalties), operational (emergency disconnection of OAuth applications or integrations, disrupting billing, or engagement workflows), and reputational (public breach disclosures and erosion of patient and partner trust).

Healthcare breaches now cost an average of $10.22 million per incident in 2025, with each compromised medical record adding approximately $398 to the total.

What “Continuous Monitoring” Actually Means in Practice

Most organizations that claim continuous monitoring are continuously watching infrastructure, endpoints, and log streams, but not the SaaS-level identities, configurations, and data relationships where these incidents actually start.

They have a 24/7 view of traffic and systems, but only a periodic, checklist-driven view of SaaS posture.

What Organizations Are Watching

SIEM and XDR on all logs and endpoints: Constant ingestion of authentication logs, VPN and ZTNA logs, EDR telemetry, firewall events, and some SaaS audit logs into a central SIEM. Alerting tuned around known IOCs, brute force attempts, malware, anomalous logins, and data egress patterns at the network or endpoint layer.

CSPM and cloud-infrastructure scanning: Ongoing checks for misconfigurations in IaaS and PaaS (public buckets, security groups, IAM roles) and infrastructure vulnerabilities. Strong coverage for “is this VM, VPC, or S3-equivalent secure?” but limited or no deep inspection of SaaS tenants.

Control monitoring for compliance: Automated evidence collection to keep HIPAA, PCI, and SOC 2 controls in a passing state. These systems test whether controls exist, not whether SaaS-side relationships between users, data, and integrations have quietly drifted into a risky state.

This stack is valuable, but it’s oriented around systems and events, not SaaS configuration drift and identity-to-data exposure.

What They’re Not Watching in SaaS

OAuth scopes, connected applications, and SaaS-to-SaaS supply chain: No persistent inventory of which third-party applications are connected, what scopes they have, and when scopes change or spike in use. Most monitoring stops at “user logged in via SSO” and doesn’t ask “which applications are acting on behalf of this user or tenant via tokens, and what data can they touch?”

Sharing and configuration drift: Limited or no automated scanning for files, records, or workspaces that flipped from internal-only to “anyone with the link” or external domains. Security reviews of SaaS settings remain quarterly or annual projects, not minute-by-minute checks with drift detection and rollback.

Identity posture inside SaaS: Monitoring focuses on whether MFA is enforced, not whether there are dormant admins, over-privileged service accounts, or risky role combinations in the SaaS applications themselves.

Without that layer, an over-permissioned OAuth application or a sharing misconfiguration can sit in plain sight, passing all the high-level checks, for months or years.

Fintech’s Unique Velocity Problem

In fintech, acceptable SaaS posture is defined in much tighter, more granular, and more time-sensitive terms than in healthcare.

Any posture mistake can translate directly into fraud, lost transactions, or market-moving events in near real time.

How SLOs Differ

Remediation timelines for high-risk issues: In fintech, high-severity SaaS and API posture issues (over-privileged API keys, OAuth scopes touching payment flows, KYC and AML data exposures) often carry SLOs measured in hours or a small number of business days. They can be directly weaponized for fraudulent transactions or account takeover.

In healthcare, high-risk misconfigurations affecting PII are critical, but SLOs are frequently framed in days to weeks, reflecting a focus on regulatory deadlines and breach-notification windows rather than second-by-second financial loss.

Tolerance bands for critical posture: Fintech teams often operate with a target of zero known critical misconfigurations in systems that touch money movement, trading, or authentication, and only a small, time-boxed backlog of medium-severity issues.

Healthcare organizations often accept that a certain number of non-blocking misconfigurations will exist at any time, as long as PII-centric systems stay compliant with HIPAA and related frameworks.

Why the Fintech Operating Model Forces This

Transaction velocity and fraud risk: Fintech platforms run high-volume, real-time transactions where a misconfigured SaaS integration or API can lead to rapid, compounding financial loss before a traditional incident process can respond. Financial services breach costs average $5.56 million in 2025, driven by direct monetary targets and heavy regulation.

API-first, partner-heavy architectures: Fintechs rely heavily on external and partner APIs—banks, card networks, KYC providers, rewards programs. Their SaaS posture decisions must assume that third-party changes can instantly affect downstream risk. They embed continuous posture checks into CI/CD and runtime for APIs and SaaS integrations, treating deviations as production-impacting incidents.

Regulatory focus on resilience and continuity: Financial regulators increasingly emphasize operational resilience (maintaining service continuity and preventing financial harm) alongside confidentiality. The SEC now requires public companies to file Form 8-K Item 1.05 within four business days of determining an incident is material.

In fintech, secure posture is redefined as “we can continuously prove that no SaaS misconfiguration or integration drift can be used, right now, to move money or alter balances outside of approved paths.”

The Operational Architecture That Works

In fintech organizations that reach trading-limit precision on posture SLOs, the monitoring infrastructure is organized around a small number of tightly defined questions: “Who or what can move money where, and did that change?”

Everything else is treated as noise.

Who Watches What

Security engineering and platform teams own continuous SaaS posture (SSPM) and identity: admin roles, OAuth scopes, API keys, service accounts, and SaaS-to-SaaS integrations that can touch ledgers, payment rails, and authentication. Their mandate is “no configuration, token, or integration can move money or issue authentication tokens outside of approved patterns.”

Fraud and risk analytics teams watch real-time transaction and API behavior: risk scores on calls, velocity anomalies, device and account fingerprints, and partner behavior across event streams. Their question is “given current posture, are any calls using legitimate paths in illegitimate ways?”

SOC and incident response teams monitor correlated alerts: posture changes plus API anomalies plus identity events (SSO, step-up authentication, failed checks) in a SIEM or XDR. They focus on high-fidelity, multi-signal cases that indicate real risk to funds movement or customer data.

This division means nobody is “watching everything.” Each team owns a narrow slice connected directly to money movement and customer trust.

Frequency and Control Loops

Inline and real-time (milliseconds to seconds): API gateways and fraud engines score every transaction and high-risk API call in real time, using both behavioral features and posture context. Decisions like block, step-up authentication, or allow are executed inline.

Streaming posture checks (seconds to minutes): SSPM and identity services subscribe to admin and audit events from core SaaS and API platforms, flagging changes to permissions, routes, policies, or integrations that touch financial actions. High-risk changes can trigger automatic rollback or quarantine within minutes.

Batch analysis and tuning (hours to days): Fraud and security teams regularly review aggregated metrics—false-positive rates, blocked attempts, posture-change volumes—and adjust thresholds, allow-lists, and models.

Distinguishing Signal From API Noise

Schema- and action-aware monitoring: Not all endpoints are treated equally. Monitoring distinguishes between “read-only balance check” versus “initiate payout” versus “change settlement account,” focusing posture enforcement and alerting on the few verbs that actually move money or authorization.

Baseline- and correlation-driven alerts: SIEM and fraud platforms use baselines per client, per partner, and per route, then alert on deviation patterns rather than raw events. Alert rules usually require multiple conditions: a posture change plus anomalous traffic plus, sometimes, a fraud-engine spike.

Strict whitelisting and posture gates: Only a small, vetted set of SaaS integrations, API clients, and scopes are allowed to touch funds-moving paths. Anything outside that allow-list is blocked or sandboxed by default. CI/CD and change-management gates ensure that new routes, scopes, or SaaS connections cannot go live without posture checks.

The Healthcare Analog: Organizing Around PII Flows

The healthcare equivalent of “who can move money where” is: “Who or what can see, change, or export which PII, through which paths, and did that change in a way that violates our regulatory constraints?”

For healthcare organizations implementing continuous SaaS posture monitoring, the control loops work best when centered on three core questions.

PII access and exposure: Which identities (users, service accounts, SaaS applications) can currently access or export PII, and did their effective access or sharing boundary change? This includes SaaS-to-SaaS integrations, trackers, and analytics tools that can see patient identifiers, visit details, claims data, or images.

PII data flows and destinations: Where does PII actually flow (EHR, CRM, billing, collaboration, AI tools) and did any new path appear that bypasses our approved data-flow map or business associate agreements? This covers new external shares, public or “anyone with link” objects, and new third-party applications with PII-level scopes.

Configuration drift on PII systems: Did any configuration on systems that handle PII (sharing policies, retention, logging, access control) drift from our baseline in a way that could enable unauthorized viewing, exfiltration, or tampering?

Where the Model Breaks Down

When healthcare teams implement PII-centric continuous monitoring, the model almost always breaks first on where PII actually lives and flows versus where leaders believe it does.

Organizations discover that their data maps quietly assume PII is confined to a small set of sanctioned systems. Continuous visibility shows it leaking into shadow SaaS, tracking pixels, AI tools, and low-friction integrations that were never in the diagram.

Hidden PII destinations: Continuous monitoring reveals PII or highly inferable health data flowing into web analytics, ad platforms, embedded maps, chat widgets, survey tools, and CRM systems that were never classified as PII systems. Studies show approximately 65% of SaaS applications in healthcare lack formal IT approval.

Clinicians and staff paste visit notes, imaging summaries, or exported reports into unsanctioned AI tools, completely outside the approved PII design and without business associate agreements in place.

Over-trusted third-party access: Organizations discover dozens of connected applications and OAuth integrations per major SaaS tenant, many with broad PII-level scopes that haven’t been used in months but still have access. Continuous posture tools show that these integrations often move PII to systems and geograPIIes never contemplated in the original risk assessment.

Misaligned PII classification: Continuous DSPM and SSPM-style discovery highlights PII or PII-adjacent fields embedded in logs, exports, spreadsheets, test environments, and collaboration spaces that were assumed to be non-sensitive. Role-based access control models look sound on paper, but monitoring shows widespread permission creep.

The approved design turns out to be a narrow island in a much larger sea of real PII flows.

The Conversation That Moves From “Shut It Down” to Sustainable Posture

When healthcare security leaders see that reality for the first time through continuous monitoring (all those shadow AI tools, tracking pixels, and unapproved integrations), the conversation with operational leadership needs to start by treating those shadow tools as evidence of real operational need, not just violations.

The goal shifts from “shut it all down” to “bring the tools people clearly need into a governed, PII-aware model.”

Start With Empathy and Shared Intent

Acknowledge why the shadow ecosystem exists: “The fact that we see PII in AI tools, pixels, and unsanctioned SaaS tells us people are trying to close gaps in workflows, communication, and analytics that our official stack hasn’t kept up with.”

This validates the effort clinicians and staff have made to improve care and efficiency, instead of framing them as the problem.

Reframe risk in their language. Translate continuous-monitoring findings into outcomes they care about: patient/client trust, reputational damage, downtime during investigations, regulatory scrutiny, and the burden of breach notifications to their populations.

“If this pattern continues, a single investigation could require notifying tens of thousands of your patients. That means clinic disruption, call-center overload, and a hit to the program you’ve built.”

Move From “Stop” to “Standardize and Prioritize”

Segment tools into “keep, fix, or retire.” Jointly classify what continuous monitoring found:

Keep: High-value tools that clearly improve care or operations and can be brought under business associate agreements and technical controls.

Fix: Tools that are useful but misconfigured for PII (pixels with identifiers, over-broad sharing, unsafe export patterns).

Retire: Tools with low value or high inherent risk where safer alternatives exist.

This gives operational leaders a voice in deciding what survives, instead of imposing a blanket ban.

Co-create simple guardrails: “No PII into tools without a business associate agreement and explicit approval. No identifiers into tracking pixels. No unsecured exports.”

Make clear that the goal isn’t fewer tools—it’s fewer unguarded PII paths.

Share Ownership and Define a Sustainable Operating Model

Propose a lightweight intake process for new tools and workflows that operations leaders can actually live with: standard questionnaires, risk tiers, and pre-approved patterns for common use cases like patient messaging, scheduling, and internal AI summarization.

Commit to turnaround times and standard reference architectures. “If you stay within these patterns, security will say yes quickly.”

Position the new PII-centric monitoring as something that protects their innovations by catching drift, configuration mistakes, and vendor changes early, rather than as a tool to punish deviation.

Share a small, curated set of metrics with operations leadership so they see progress, not just findings: “Number of unapproved PII flows trending down. Percent of PII in sanctioned, monitored systems trending up.”

The Three-Step Implementation Roadmap

If a healthcare CISO walked out of that meeting with leadership’s buy-in, three concrete steps turn continuous PII monitoring from “overwhelming visibility into problems” into “governed innovation with measurable risk reduction.”

Step 1: Normalize the PII Inventory Into a Single, Shared Map

Turn raw continuous-monitoring findings into one authoritative view that security, compliance, and privacy leaders all recognize as “the map.”

Consolidate outputs from SSPM, DSPM, and other tools into a unified catalog of systems, SaaS applications, integrations, data stores, and channels where PII is actually present. Apply automated PII classification and tagging so each asset is labeled by PII sensitivity, volume, and business owner.

For every PII-bearing system or integration, record the operations owner, privacy contact, security contact, and whether it’s currently “keep, fix, or retire.”

This turns the continuous feed into a living data-governance map instead of an endless alert stream.

Step 2: Implement a PII Risk Queue With SLOs Aligned to “Keep/Fix/Retire”

Convert that map into a small number of operational queues, with explicit SLAs and playbooks so issues are worked down instead of just observed.

Group continuous-monitoring findings into a single PII-risk queue, with tiers:

Critical: PII in unsanctioned or non-business-associate-agreement tools, public exposure, open links, or risky third-party flows.

High: Sanctioned tools misconfigured for PII—over-broad sharing, disabled logging, weak access controls.

Medium/Low: Hygiene issues, over-retention, or access creep without current external exposure.

Each issue links back to the map: which workflow, which leader, how many records or patients, which regulations are implicated.

With leadership’s buy-in, define realistic but firm targets. For example: critical PII exposures remediated in 72 hours or less, high in 30 days or less, medium as part of quarterly cycles.

Make these SLOs visible on a shared dashboard that shows both risk reduction—critical items trending down, PII increasingly in sanctioned systems—and innovation health—number of approved tools and workflows supported.

Step 3: Embed PII-Aware Guardrails Into Everyday Workflows

Ensure new and existing innovation flows are automatically steered into safer patterns, so you don’t recreate the same shadow ecosystem six months later.

Implement a simple, fast intake for new tools and workflows that operations teams can live with, backed by standard blueprints for common patterns: patient messaging, telehealth, AI summarization, analytics.

Each blueprint specifies allowed data elements, required PII protections (no identifiers in pixels, encryption, access controls), business associate agreement requirements, and how the tool will be monitored via SSPM and DSPM.

Use SSPM, DSPM, and identity integrations to implement technical controls aligned to those blueprints: prevent new SaaS connections without review, block risky sharing defaults, alert on PII appearing in unsanctioned destinations, and auto-tag new PII repositories.

Feed concise, business-oriented metrics back to leaders: “X shadow tools migrated to sanctioned patterns, Y critical PII paths eliminated, Z new workflows onboarded under approved designs.”

What Success Looks Like in Measurable Terms

Success shows up as a small set of numbers that move in the right direction together: PII is increasingly concentrated in governed systems, high-risk exposures are fixed faster, and teams are still adopting new, sanctioned workflows.

Leading Indicators: Posture and Behavior

Shrinkage of uncontrolled PII surface: Percentage of PII stores and flows in sanctioned, monitored systems versus shadow tools trending up quarter over quarter. Count of newly discovered PII locations per month declining, even as monitoring remains constant or expands.

Improved risk posture and remediation performance: Number of open critical and high PII-related misconfigurations and exposures decreasing over time, with SSPM posture scores or similar composite risk scores improving. Mean time to remediate PII-related findings by severity consistently meeting or beating agreed SLOs.

Healthy, governed innovation flow: Volume of new tools and workflows onboarded through the approved “keep/fix” pathways increasing or at least holding steady, while the ratio of shadow tools to approved tools drops. Percentage of new PII-impacting projects that go through intake and blueprint patterns from day one, rather than being discovered reactively by monitoring.

Lagging Indicators: Incidents, Audits, and Trust

Fewer and smaller PII incidents: Year-over-year reduction in the number of PII breaches attributed to SaaS misconfigurations, exports, or third-party tools, and a reduction in the patient count per incident when they do occur. Measurable drop in dwell time for PII exposures, particularly for SaaS and integration-driven events.

Stronger regulatory and audit outcomes: Improved compliance audit results related to SaaS, third-party risk, and data governance (fewer repeat findings, faster closure of corrective action items, and cleaner external assessments). Demonstrable, data-backed evidence of PII data-governance maturity that satisfies board and regulator questions without emergency projects.

Reduced friction between security and operations: Decrease in emergency security interventions that interrupt operations, and qualitative feedback that security is enabling, not blocking, their initiatives.

When those leading and lagging indicators line up (less unknown PII, faster and fewer high-severity findings, stable or growing adoption of sanctioned tools, and a clear drop in PII incidents and audit pain), a CISO can credibly say they’ve crossed the line from reactive firefighting to proactive, PII-centric posture management that still leaves room for innovation.

Moving Forward: From Awareness to Action

The average enterprise now utilizes over 275 SaaS applications (a 60% increase since 2023). Data breaches represent around 50-52% of all SaaS security incidents, with the average cost for a SaaS-related breach at approximately $4.88 million across industries.

Organizations experience an average of 19 days of business disruption following a significant SaaS-related breach, with recovery efforts consuming approximately 2,800 person-hours of IT staff time.

The structural gap between what organizations believe protects them and what actually happens in their SaaS environments isn’t closing on its own. Periodic audits happening quarterly, biannually, or annually are no longer sufficient. The cloud moves much faster than periodic auditing can cover.

We’ve observed this pattern across hundreds of deployments: the organizations that successfully transition from periodic snapshots to continuous posture management don’t do it by adding more tools to an already fragmented stack. They do it by organizing around a single, high-signal question tied directly to their most critical assets.

That leaves you with two core questions to ask yourself: 1) “Who or what can move money where, and did that change?” and 2) “Who or what can see, change, or export which PII, through which paths, and did that change in a way that violates our regulatory constraints?”

The shift from overwhelming visibility to governed innovation starts with turning PII or financial telemetry into a small, opinionated set of queues, owners, and rules. It requires acknowledging that shadow tools exist because official stacks haven’t kept up with operational needs. And it demands shared accountability between security, financial operations, and compliance teams.

Start by normalizing your inventory into a single, shared map. Implement a risk queue with SLOs aligned to your “keep, fix, or retire” decisions. Embed guardrails into everyday workflows so you don’t recreate the same exposure patterns six months later.

Measure success through both leading indicators (shrinking uncontrolled data surface, faster remediation, healthy innovation flow) and lagging indicators (fewer incidents, stronger audit outcomes, reduced operational friction).

Continuous monitoring isn’t optional anymore. The question is whether you’ll implement it reactively, after the next breach notification, or proactively, before regulators and patients force you to notice what’s been exposed for months or years.

Build the infrastructure that treats seconds as currency. Collapse the distance between visibility and recovery. Make downtime obsolete.

References and Sources

1. Compliancy Group, “March 2025 Healthcare Data Breaches: Hacking Remains the Top Threat,” Compliancy Group, 2025.
2. Nudge Security, “Breach of SalesLoft and Drift OAuth Tokens Leads to Salesforce Data Theft,” Nudge Security, 2024.
3. Ponemon Institute and IBM, “Cost of a Data Breach Report 2025,” IBM Security, 2025.
4. U.S. Securities and Exchange Commission, “Cybersecurity Risk Management, Strategy, Governance, and Incident Disclosure,” SEC.gov, 2023.
5. Reco.ai, “Managing Shadow IT: Healthcare SaaS Security Challenges,” Reco.ai, 2024.
6. AppOmni, “How PII in Healthcare SaaS Is at Risk,” AppOmni Blog, 2024.
7. Spin.AI, “What Is SSPM: SaaS Security Posture Management,” Spin.AI Blog, 2024.
8. Spin.AI, “SaaS Security Posture Management Platform,” Spin.AI, 2024.
9. Spin.AI, “Data Security Posture Management (DSPM),” Spin.AI, 2024.
10. Censinet, “The HIPAA Risk Blind Spot: Third-Party Vendors and the Rise of Shadow IT,” Censinet Perspectives, 2024.
11. Spin.AI, “Continuous Monitoring for SaaS Security,” Spin.AI Blog, 2024.

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