The Dangerous Moment Is the State Change: Security Testing for Connected Devices

The firmware image passes static analysis.

The administrative interface survives a penetration test. The network scan shows only approved services. The mobile application and cloud API have been reviewed. The device enters production with a clean report.

Then the device changes state.

A technician enters maintenance mode. An update loses power halfway through installation. A factory reset prepares the device for a new owner. A recovery process restores an older image. A field engineer connects to a local service interface. A retired device remains registered to a cloud account.

The controls that worked during normal operation may not work the same way during any of those transitions.

That is the testing gap.

Connected-device security is usually evaluated as a collection of components and attack surfaces: firmware, hardware, network services, wireless interfaces, applications, APIs, and cloud infrastructure. Each view matters. None of them, by itself, proves that the product preserves its intended trust state when it is provisioned, updated, interrupted, repaired, reset, transferred, recovered, or retired.

The practical lesson is straightforward:

Device security assurance should test the transitions that change authority, software, data, and operating mode—not only the device’s steady state.

ConfigRvw

A Connected Product Has More Than One Security State

A connected device is not simply secure or insecure.

It moves through states.

The lifecycle may include manufacturing, initial provisioning, normal operation, degraded operation, maintenance, software update, rollback, recovery, factory reset, ownership transfer, and retirement. Some products add calibration, emergency operation, offline modes, safety overrides, diagnostic modes, or vendor-support access.

Each state can change:

  • Which identities are trusted.
  • Which interfaces are reachable.
  • Which code is allowed to run.
  • Which secrets are present.
  • Which data is retained.
  • Which actions are permitted.
  • Which events are logged.
  • Which recovery paths are available.

The current NIST IR 8259 Revision 1, finalized in April 2026, tells IoT product manufacturers to define expected customers and use cases, identify cybersecurity requirements, and plan how the product will support customer risk management. The companion NIST IR 8259A baseline includes device identification, configuration, data protection, logical access to interfaces, secure software update, and cybersecurity-state awareness.

Those capabilities do not exist only during normal operation.

They must remain meaningful while the product changes state.

Steady-State Testing Can Miss Transition Risk

Static firmware analysis can identify exposed secrets, unsafe configurations, vulnerable components, dangerous functions, and other defects inside an image.

Dynamic testing can examine running services, authentication, authorization, input handling, communications, and runtime behavior.

Hardware and interface testing can examine local access paths that a remote network assessment will never see.

Application and API testing can evaluate the services that control the device, store its data, and connect it to other systems.

The OWASP IoT Security Testing Guide reflects this broader reality by organizing testing around the device model, firmware, interfaces, communications, and runtime behavior rather than treating an IoT product as a single web endpoint.

The transition problem appears between those views.

Consider the questions created by an update:

  • Who is allowed to initiate it?
  • How does the device authenticate the update source and verify the image?
  • Can an authorized image be replayed after it should have expired?
  • What happens if power, storage, or connectivity fails halfway through installation?
  • Does rollback return the device to an older but vulnerable state?
  • Are security settings, keys, and logs preserved correctly?
  • Does the device report that it is running a degraded or unexpected version?

Now consider a factory reset:

  • Which user data is removed?
  • Which device identity and cryptographic material remain?
  • Are cloud registrations, mobile-app bindings, or vendor accounts disconnected?
  • Does the reset restore an unsafe default configuration?
  • Can a previous owner or administrator still reach the product?
  • Is the reset event visible to the next operator?

A review that tests only the stable image before and after the transition may miss the condition created during the transition itself.

Build a State-Transition Test Model

Start with a state diagram, not a tool list.

For each important transition, create a compact test record with six fields.

1. Entry condition: What starts the transition?

Identify the command, signal, physical action, scheduled event, fault, account action, or environmental condition that moves the device out of its current state.

Examples include an update request, boot failure, service-button sequence, cloud command, expired certificate, loss of connectivity, storage exhaustion, ownership transfer, or operator decision.

The entry condition should be testable. If the team cannot reproduce the condition safely, it cannot demonstrate how the product behaves when the condition occurs.

2. Authority: Who or what may cause it?

Name every actor that can initiate, approve, block, or complete the transition.

That may include the device owner, local administrator, vendor support team, cloud service, companion application, update-signing system, physical technician, automation service, or another device.

Then test the boundary between those roles.

An identity authorized to read device status should not automatically gain authority to install firmware. A field technician permitted to run diagnostics should not silently become a permanent administrator. A cloud service that can request an update should not be able to bypass the device’s independent verification of the image.

3. Protected state: What changes or persists?

List the assets and security properties that the transition can modify:

  • Firmware and boot components.
  • Configuration and policy.
  • Credentials, keys, certificates, and trust anchors.
  • User, customer, operational, or diagnostic data.
  • Device identity and ownership records.
  • Local and network interfaces.
  • Logging and monitoring state.
  • Safety limits and operating parameters.

Testing should confirm both what changes and what must not change.

4. Failure path: What happens when the transition does not finish normally?

Interrupt the process in a controlled environment.

Remove a dependency. Deny a required authorization. Present an invalid or older image. Exhaust an expected resource. Break connectivity at a defined step. Restart the device while state is being written.

The purpose is not to create theatrical destruction.

It is to learn whether failure moves the product into a secure, observable, and recoverable condition.

5. Evidence: What proves the resulting state?

Define the evidence an operator, customer, assessor, or incident responder can use to determine what happened.

That may include version and integrity measurements, event logs, update records, administrative actions, device-state reports, cloud audit records, tamper indicators, recovery results, or retained forensic artifacts.

If the device cannot explain its own security state, the organization may have to guess whether the transition succeeded.

6. Consequence: What can the new state affect?

Connect the technical result to the product’s real use.

Could the device disclose data, stop a business process, permit unauthorized control, produce incorrect measurements, lose availability, weaken a safety function, or become impossible to recover remotely?

The same technical defect can have very different significance in a consumer accessory, payment device, building controller, medical product, industrial sensor, or safety-related system.

Protect, Detect, and Recover

Device assurance is incomplete if it tests only whether unauthorized change can be prevented.

NIST SP 800-193 organizes platform-firmware resilience around three goals: protect the platform from unauthorized change, detect unauthorized change when it occurs, and recover rapidly and securely.

That sequence is a useful testing standard.

Protect

Verify that only authorized entities can initiate the transition and that the device independently validates the software, configuration, or command it is about to trust.

Test version policy, signing, authorization separation, local access controls, service interfaces, and the protection of keys and trust anchors.

Detect

Verify that the product can recognize and report an incomplete, unauthorized, degraded, or unexpected state.

An update mechanism that rejects a bad image but leaves no useful record may protect the device while weakening incident response. A device that boots a fallback image without reporting the version change may remain operational while concealing a material security event.

Recover

Verify that recovery returns the product to an intended trust state, not merely to an operable state.

The device may power on and still be running an obsolete image, unsafe configuration, untrusted key set, or diagnostic mode. Recovery evidence should show what was restored, which trust decision authorized it, and what follow-up action remains.

Physical Consequence Changes the Test Plan

Connected products can affect more than data.

The final NIST SP 800-82 Revision 3 defines operational technology broadly as programmable systems and devices that interact with the physical environment and emphasizes that security measures must account for performance, reliability, and safety requirements.

That changes how testing should be performed.

An aggressive production scan, unplanned reboot, malformed command, or interrupted update may be acceptable in a disposable lab unit and unacceptable in a live operational process. Some questions should be answered through offline firmware analysis, emulation, configuration review, representative hardware, isolated test networks, or a controlled lab before anyone considers validation in the field.

The test plan should distinguish:

  1. What can be examined without operating the device.
  2. What can be exercised safely on representative hardware.
  3. What requires an isolated system or simulated environment.
  4. What must be validated in the deployed environment under a carefully approved procedure.
  5. What should not be tested actively because the safety or availability consequence is unacceptable.

Restraint is part of good testing.

The objective is not to prove that the assessor can break the device.

It is to produce useful evidence without creating the harm the assessment is supposed to prevent.

Scale Assurance by Consequence and Change

Not every connected product needs the same depth of testing.

A low-consequence, easily replaced device with no sensitive data and minimal authority does not need the same assurance program as a long-lived controller that can alter a physical process.

Set the testing tier using factors such as:

  • Safety and operational consequence.
  • Access to sensitive data or privileged actions.
  • Exposure to untrusted networks, users, or physical access.
  • Authority of local, wireless, cloud, and service interfaces.
  • Complexity of update, rollback, and recovery mechanisms.
  • Dependence on companion applications, APIs, and vendor cloud services.
  • Expected service life and ability to replace the product.
  • Availability of logs, integrity evidence, and emergency recovery.

Then define retest triggers.

A new firmware branch, bootloader, hardware revision, wireless stack, update service, cloud backend, cryptographic design, manufacturing process, or field-service workflow can change the transition model even when the product name remains the same.

The current NIST IR 8259 Revision 1 reinforces this lifecycle view by asking manufacturers to consider customer needs, product requirements, risk mitigations, and the support customers require to use the product securely.

Security testing should follow the product as those conditions change.

Measure Transition Assurance, Not Just Defect Counts

A vulnerability count is useful for remediation tracking.

It does not show whether the product preserves trust across its lifecycle.

More useful measures include:

  • Percentage of high-consequence state transitions represented in the test model.
  • Percentage of update and recovery paths that independently authenticate software and enforce version policy.
  • Percentage of reset, transfer, and retirement paths that remove customer data and obsolete account bindings as designed.
  • Percentage of maintenance and diagnostic actions attributable to a named identity.
  • Percentage of interrupted transitions that produce an observable security-state record.
  • Time required to recover a representative device to a known, supported, and verified state.
  • Number of product changes that altered the threat model but did not trigger security retesting.
  • Percentage of critical transition tests that include the companion application, API, cloud service, and deployed operating assumptions.

One question is especially revealing:

After an update, reset, or recovery fails, can you prove which trust state the device is in?

If the answer depends on a visual guess, undocumented service sequence, or the memory of one engineer, the product may be recoverable without being governable.

What Security Leaders Should Do This Week

Choose one connected product with meaningful business, safety, privacy, or operational consequence.

  1. Draw its states. Include provisioning, normal operation, update, degraded operation, maintenance, recovery, reset, transfer, and retirement where applicable.
  2. Name every transition authority. Identify who or what can initiate, approve, complete, or block each state change.
  3. Map what changes and persists. Include software, configuration, identities, keys, data, interfaces, logging, and cloud bindings.
  4. Choose one failure path. Safely interrupt an update, recovery, reset, or maintenance workflow on representative equipment.
  5. Verify protect, detect, and recover. Confirm that unauthorized change is prevented, abnormal state is visible, and recovery restores a supported trust state.
  6. Connect the result to consequence. Decide what the observed behavior could mean for safety, operations, customers, data, and incident response.
  7. Set the retest triggers. Require reassessment when a material component, interface, update path, backend dependency, or field workflow changes.

A secure firmware image matters.

A hardened interface matters.

A tested API matters.

The product still has to move safely between the states that join those components together.

Test the transition.

That is where the device changes what it trusts.


Put MicroSolved’s Device and Lab Testing Experience to Work

MicroSolved, Inc. can help organizations evaluate industrial, IoT, embedded, and connected products through controlled, evidence-driven security testing.

MSI can assist with:

  • Product threat modeling and state-transition test design.
  • Firmware extraction, static analysis, emulation, and controlled runtime testing.
  • Review of local, network, wireless, service, application, API, and cloud interfaces.
  • Update, rollback, reset, maintenance, recovery, and retirement-path validation.
  • Embedded-device, firmware, AMI, SCADA, segmentation, and tamper-resistance assessment.
  • Test planning for high-availability, safety-sensitive, and critical-infrastructure environments.
  • Prioritized mitigation guidance that connects technical findings to business and operational consequence.

To discuss connected-device, firmware, industrial, or lab testing, contact MicroSolved at info@microsolved.com or +1.614.351.1237.

Relax. We’re on watch.

AI tools were used as a research assistant for this content, but human moderation and writing are also included.

 

Three Field Guides for the Security Problems Showing Up Right Now

Over the last several months, we have released three longer-form guides around problems we keep seeing in real environments:

  • The Antifragile SOC Book
  • The AI Agents Management Framework
  • The Security Leader’s Operating System

They cover different areas, but they share a common theme: how to operate effectively as complexity, automation, and pressure increase.

Books

Why We Created These Materials

These guides did not begin as content projects.

They came from working problems.

Over the years, we have accumulated a lot of models, processes, questions, and operating approaches that we use when helping organizations solve difficult security problems. Some came from client work, some from research, and plenty came from lessons learned the hard way.

Much of that knowledge lived in conversations, notes, presentations, and experience.

We decided it was time to write more of it down.

Not as theory, and not as another collection of cybersecurity predictions.

As practical field material people can use, challenge, adapt, and apply.

Continue reading →

Cut SOC Noise with an Alert-Quality SLO: A Practical Playbook for Security Teams

Security teams don’t burn out because of “too many threats.” They burn out because of too much junk between them and the real threats: noisy detections, vague alerts, fragile rules, and AI that promises magic but ships mayhem.

SOC

Here’s a simple fix that works in the real world: treat alert quality like a reliability objective. Put noise on a hard budget and enforce a ship/rollback gate—exactly like SRE error budgets. We call it an Alert-Quality SLO (AQ-SLO) and it can reclaim 20–40% of analyst time for higher-value work like hunts, tuning, and purple-team exercises.

The Core Idea: Put a Budget on Junk

Alert-Quality SLO (AQ-SLO): set an explicit ceiling for non-actionable alerts per analyst-hour (NAAH). If a new rule/model/AI feed pushes you over budget, it doesn’t ship—or it auto-rolls back.

 

Think “error budgets,” but applied to SOC signal quality.

 

Working definitions (plain language)

  • Non-actionable alert: After triage, it requires no ticket, containment, or tuning request—just closes.
  • Analyst-hour: One hour of human triage time (any level).
  • AQ-SLO: Maximum tolerated non-actionables per analyst-hour over a rolling window.

Baselines and Targets (Start Here)

Before you tune, measure. Collect 2–4 weeks of baselines:

  • Non-actionable rate (NAR) = (Non-actionables / Total alerts) × 100
  • Non-actionables per analyst-hour (NAAH) = Non-actionables / Analyst-hours
  • Mean time to triage (MTTT) = Average minutes to disposition (track P90, too)

 

Initial SLO targets (adjust to your environment):

  • NAAH ≤ 5.0  (Gold ≤ 3.0, Silver ≤ 5.0, Bronze ≤ 7.0)
  • NAR ≤ 35%    (Gold ≤ 20%, Silver ≤ 35%, Bronze ≤ 45%)
  • MTTT ≤ 6 min (with P90 ≤ 12 min)

 

These numbers are intentionally pragmatic: tight enough to curb fatigue, loose enough to avoid false heroics.

 

Ship/Rollback Gate for Rules & AI

Every new detector—rule, correlation, enrichment, or AI model—must prove itself in shadow mode before it’s allowed to page humans.

 

Shadow-mode acceptance (7 days recommended):

  • NAAH ≤ 3.0, or
  • ≥ 30% precision uplift vs. control, and
  • No regression in P90 MTTT or paging load

 

Enforcement: If the detector breaches the budget 3 days in 7, auto-disable or revert and capture a short post-mortem. You’re not punishing innovation—you’re defending analyst attention.

 

Minimum Viable Telemetry (Keep It Simple)

For every alert, capture:

  • detector_id
  • created_at
  • triage_outcome → {actionable | non_actionable}
  • triage_minutes
  • root_cause_tag → {tuning_needed, duplicate, asset_misclass, enrichment_gap, model_hallucination, rule_overlap}

 

Hourly roll-ups to your dashboard:

  • NAAH, NAR, MTTT (avg & P90)
  • Top 10 noisiest detectors by non-actionable volume and triage cost

 

This is enough to run the whole AQ-SLO loop without building a data lake first.

 

Operating Rhythm (SOC-wide, 45 Minutes/Week)

  1. Noise Review (20 min): Examine the Top 10 noisiest detectors → keep, fix, or kill.
  2. Tuning Queue (15 min): Assign PRs/changes for the 3 biggest contributors; set owners and due dates.
  3. Retro (10 min): Are we inside the budget? If not, apply the rollback rule. No exceptions.

 

Make it boring, repeatable, and visible. Tie it to team KPIs and vendor SLAs.

 

What to Measure per Detector/Model

  • Precision @ triage = actionable / total
  • NAAH contribution = non-actionables from this detector / analyst-hours
  • Triage cost = Σ triage_minutes
  • Kill-switch score = weighted blend of (precision↓, NAAH↑, triage cost↑)

 

Rank detectors by kill-switch score to drive your weekly agenda.

 

Formulas You Can Drop into a Sheet

NAAH = NON_ACTIONABLE_COUNT / ANALYST_HOURS

NAR% = (NON_ACTIONABLE_COUNT / TOTAL_ALERTS) * 100

MTTT = AVERAGE(TRIAGE_MINUTES)

MTTT_P90 = PERCENTILE(TRIAGE_MINUTES, 0.9)

ERROR_BUDGET_USED = max(0, (NAAH – SLO_NAAH) / SLO_NAAH)

 

These translate cleanly into Grafana, Kibana/ELK, BigQuery, or a simple spreadsheet.

 

Fast Implementation Plan (14 Days)

Day 1–3: Instrument triage outcomes and minutes in your case system. Add the root-cause tags above.

Day 4–10: Run all changes in shadow mode. Publish hourly NAAH/NAR/MTTT to a single dashboard.

Day 11: Freeze SLOs (start with ≤ 5 NAAH, ≤ 35% NAR).

Day 12–14: Turn on auto-rollback for any detector breaching budget.

 

If your platform supports feature flags, wrap detectors with a kill-switch. If not, document a manual rollback path and make it muscle memory.

 

SOC-Wide Incentives (Make It Stick)

  • Team KPI: % of days inside AQ-SLO (target ≥ 90%).
  • Engineering KPI: Time-to-fix for top noisy detectors (target ≤ 5 business days).
  • Vendor/Model SLA: Noise clauses—breach of AQ-SLO triggers fee credits or disablement.

 

This aligns incentives across analysts, engineers, and vendors—and keeps the pager honest.

 

Why AQ-SLOs Work (In Practice)

  1. Cuts alert fatigue and stabilizes on-call burdens.
  2. Reclaims 20–40% analyst time for hunts, purple-team work, and real incident response.
  3. Turns AI from hype to reliability: shadow-mode proof + rollback by budget makes “AI in the SOC” shippable.
  4. Improves organizational trust: leadership gets clear, comparable metrics for signal quality and human cost.

 

Common Pitfalls (and How to Avoid Them)

  • Chasing zero noise. You’ll starve detection coverage. Use realistic SLOs and iterate.
  • No root-cause tags. You can’t fix what you can’t name. Keep the tag set small and enforced.
  • Permissive shadow-mode. If it never ends, it’s not a gate. Time-box it and require uplift.
  • Skipping rollbacks. If you won’t revert noisy changes, your SLO is a wish, not a control.
  • Dashboard sprawl. One panel with NAAH, NAR, MTTT, and the Top 10 noisiest detectors is enough.

 

Policy Addendum (Drop-In Language You Can Adopt Today)

Alert-Quality SLO: The SOC shall maintain non-actionable alerts ≤ 5 per analyst-hour on a 14-day rolling window. New detectors (rules, models, enrichments) must pass a 7-day shadow-mode trial demonstrating NAAH ≤ 3 or ≥ 30% precision uplift with no P90 MTTT regressions. Detectors that breach the SLO on 3 of 7 days shall be disabled or rolled back pending tuning. Weekly noise-review and tuning queues are mandatory, with owners and due dates tracked in the case system.

 

Tune the numbers to fit your scale and risk tolerance, but keep the mechanics intact.

 

What This Looks Like in the SOC

  • An engineer proposes a new AI phishing detector.
  • It runs in shadow mode for 7 days, with precision measured at triage and NAAH tracked hourly.
  • It shows a 36% precision uplift vs. the current phishing rule set and no MTTT regression.
  • It ships behind a feature flag tied to the AQ-SLO budget.
  • Three days later, a vendor feed change spikes duplicate alerts. The budget breaches.
  • The feature flag kills the noisy path automatically, a ticket captures the post-mortem, and the tuning PR lands in 48 hours.
  • Analyst pager load stays stable; hunts continue on schedule.

 

That’s what operationalized AI looks like when noise is a first-class reliability concern.

 

Want Help Standing This Up?

MicroSolved has implemented AQ-SLOs and ship/rollback gates in SOCs of all sizes—from credit unions to automotive suppliers—across SIEMs, EDR/XDR, and AI-assisted detection stacks. We can help you:

  • Baseline your current noise profile (NAAH/NAR/MTTT)
  • Design your shadow-mode trials and acceptance gates
  • Build the dashboard and auto-rollback workflow
  • Align SLAs, KPIs, and vendor contracts to AQ-SLOs
  • Train your team to run the weekly operating rhythm

 

Get in touch: Visit microsolved.com/contact or email info@microsolved.com to talk with our team about piloting AQ-SLOs in your environment.

 

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Daily Log Monitoring and Increased Third Party Security Responsibilities: Here They Come!

For years now we at MSI have extoled the security benefits of daily log monitoring and reciprocal security practices between primary and third party entities present on computer networks. It is constantly being proven true that security incidents could be prevented, or at least quickly detected, if system logs were properly monitored and interpreted. It is also true that many serious information security incidents are the result of cyber criminals compromising third party service provider systems to gain indirect access to private networks. 

I think that most large-network CISOs are well aware of these facts. So why aren’t these common security practices right now? The problem is that implementing effective log monitoring and third party security practices is plagued with difficulties. In fact, implementation has proven to be so difficult that organizations would rather suffer the security consequences than put these security controls in place. After all, it is cheaper and easier – usually – unless you are one of the companies that get pwned! Right now, organizations are gambling that they won’t be among the unfortunate – like Target. A fools’ paradise at best! 

But there are higher concerns in play here than mere money and efficiency. What really is at stake is the privacy and security of all the system users – which one way or another means each and every one of us. None of us likes to know our private financial or medical or personal information has been exposed to public scrutiny or compromise, not to mention identity theft and ruined credit ratings. And what about utilities and manufacturing concerns? Failure to implement the best security measures among power concerns, for example, can easily lead to real disasters and even loss of human life. Which all means that it behooves us to implement controls like effective monitoring and vendor security management. There is no doubt about it. Sooner or later we are going to have to bite the bullet. 

Unfortunately, private concerns are not going to change without prodding. That is where private and governmental regulatory bodies are going to come into play. They are going to have to force us to implement better information security. And it looks like one of the first steps in this process is being taken by the PCI Security Standards Council. Topics for their special interest group projects in 2015 are going to be daily log monitoring and shared security responsibilities for third party service providers.

That means that all those organizations out there that foster the use of or process credit cards are going to see new requirements in these fields in the next couple of years. Undoubtedly similar requirements for increased security measures will be seen in the governmental levels as well. So why wait until the last minute? If you start now implementing not only effective monitoring and 3rd party security, but other “best practices” security measures, it will be much less painful and more cost effective for you. You will also be helping us all by coming up with new ways to practically and effectively detect security incidents through system monitoring. How about increasing the use of low noise anomaly detectors such as honey pots? What about concentrating more on monitoring information leaving the network than what comes in? How about breaking massive networks into smaller parts that are easier monitor and secure? What ideas can you come up with to explore?

This post written by John Davis.

MSI Announces TigerTrax Reputational Threat Services

TigerTrax™ is MSI’s proprietary platform for gathering and analyzing data from the social media sphere and the overall web. This sophisticated platform, originally developed for threat intelligence purposes, provides the team with a unique capability to rapidly and effectively monitor the world’s data streams for potential points of interest.

 

The uses of the capability include social media code of conduct monitoring, rapid “deep dive” content gathering and analysis, social media investigations & forensics, organizational monitoring/research/profiling and, of course, threat intelligence.

 

The system is modular in nature, which allows MSI to create a number of “on demand” and managed services around the platform. Today the platform is in use in some of the following ways:

  • Sports teams are using the services to monitor professional athletes for potential code of conduct and brand damaging behaviors
  • Sports teams are also using the forensics aspects of the service to help defend their athletes against false behavior-related claims
  • Additionally, sports teams have begun to use the service for reputational analysis around trades/drafts, etc.
  • Financial organizations are using the service to monitor social media content for signs of illicit behavior or potential legal/regulatory violations
  • Talent agencies are monitoring their talent pools for content that could impact their public brands
  • Law firms are leveraging the service to identify potential issues with a given case and for investigation/forensics
  • Companies have begun to depend on the service for content monitoring during mergers and acquisitions activities, including quiet period monitoring and pre-offer intelligence
  • Many, many more uses of the platform are emerging every day

 If your organization has a need to understand or monitor the social media sphere and deep web content around an issue, a reputational concern or a code of conduct, discuss how TigerTrax from MSI can help meet your needs with an account executive today.

 

At a glance call outs:

  • Social media investigation/forensics and monitoring services
  • Customized to your specific concerns or code of conduct
  • Can provide deep dive background information or ongoing monitoring
  • Actionable reporting with direct support from MSI Analysts
  • Several pricing plans available

Key Differentiators:

  • Powerful, customizable, proprietary platform
  • Automated engines, bleeding edge analytics & human analysts to provide valuable insights
  • No web portal to learn or analytics software to configure and maintain
  • No heavy lifting on customers, MSI does the hard work, you get the results
  • Flexible reporting to meet your business needs