Continuous Threat Modeling for AI-Driven Codebases

Threat models are going stale faster than most security teams are willing to admit, and that should make you uncomfortable.

AI-assisted development has changed how fast software moves. Code is generated, rewritten, and refactored continuously. Architectures shift without formal reviews. The system security signed off on last sprint is already different, sometimes materially different, from what is running now. Threat modeling, however, still assumes change happens slowly enough to stop and reassess.

We’re talking about real risks. Threat models are still used to justify risk acceptance, controls, and compliance claims, even when the assumptions behind them no longer match the running system. Nothing breaks, and no tool fails. Security teams believe coverage exists, leadership believes the risk was assessed, and confidence quietly replaces verification.

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Table of Contents

1. Why traditional threat models fail in AI-accelerated development
2. AI changes the attack surface faster than humans can re-model it
3. What continuous threat modeling actually means (and what it does not)
4. Keeping threat models alive requires automation with real oversight
5. Continuous threat modeling should fit into how engineers already ship
6. Why this matters for compliance, audits, and board-level risk conversations
7. How often do your threat models actually reflect production reality?

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Why traditional threat models fail in AI-accelerated development

Traditional threat modeling does not fail because your team forgot how to do it, but because it depends on a delivery cadence that no longer exists.

Most threat modeling programs are built around a predictable rhythm: you schedule a design review, you lock a point-in-time architecture, you walk the data flows, you agree on trust boundaries, and you capture threats and controls that make sense for that version of the system. That approach assumes the system stays stable long enough for the model to remain a useful reference. AI-accelerated development breaks that assumption, because meaningful change now happens continuously and often without a clean design moment that security can hook into. 

Here is what that looks like in real engineering environments where copilots and automation are in the loop:

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Copilot-generated logic changes the real behavior of a component without a visible architecture change

A developer asks for a helper to handle auth, normalize input, or add caching, and the generated code introduces new libraries, new parsing paths, or new error handling behavior that changes how data moves and fails. You end up with new preconditions, new edge cases, and sometimes new implicit trust in upstream inputs, all inside the same service boundary that the threat model already covered.

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Automated refactoring reshapes attack paths while keeping the external interface stable

Refactors often split modules, consolidate utilities, move validation layers, or change where authorization checks occur. The API surface looks the same, so teams assume the risk posture is unchanged. Internally, the control points moved, sometimes from centralized enforcement to scattered checks, which increases the chance of inconsistent validation or missed authorization in one path.

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AI-assisted API creation expands the attack surface faster than documentation can keep up

Teams generate new endpoints, new GraphQL resolvers, new event handlers, and new internal service calls in hours. Even when the intent is small, the result often includes new request schemas, new serialization logic, new downstream queries, and new error responses. That changes data exposure and abuse scenarios, even when the feature feels incremental.

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Rapid iteration lands without explicit architecture updates, so security keeps reasoning from old artifacts

Design docs, diagrams, and threat model notes lag because delivery does not pause for documentation. Engineers still ship responsibly, but the source of truth for architecture drifts into code, pipelines, and runtime configuration, while the threat model stays tied to a prior snapshot.

Threat models rely on inputs that assume stability and human review as a gate. In AI-accelerated environments, those inputs decay quickly:

• Stable data flows that remain accurate release to release
• Known trust boundaries that do not shift without deliberate review
• Human-reviewed design artifacts that reflect how the system behaves right now
• A change trigger that reliably signals when the threat model needs an update

You can clearly see the gap here. The model assumes stability across the period it is meant to guide decisions, while the system behaves dynamically across that same period, with code generation and automation introducing meaningful shifts in data handling, control placement, and attack surface expansion. This is why teams can follow the process, run the sessions, produce the documents, and still end up with security decisions built on assumptions that no longer match production.

This framing matters because it lets you explain the problem without blaming engineers or the security program. The program feels ineffective because it is anchored to a cadence mismatch, not because your people lack skill or discipline. Once leadership understands that mismatch, it becomes easier to justify a change in operating model, one where threat models stay tied to ongoing change instead of pretending that a quarterly snapshot can govern a system that evolves every day.

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AI changes the attack surface faster than humans can re-model it

AI-assisted engineering increases the rate of attack surface change in ways most security programs were never designed to observe, let alone keep up with. This is not about exotic AI threats or new classes of attackers. It is about speed, scale, and subtle change happening inside systems that security teams already believe they understand.

Attack surface no longer expands only when teams ship major features or redesign architectures. It mutates continuously as AI compresses implementation, refactoring, and iteration into a tight loop that runs far ahead of human review cycles.

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How AI-driven development mutates the attack surface

In real environments, these changes rarely announce themselves as security-relevant events. They arrive as small, reasonable changes that accumulate quickly.

Generated code introduces new reachable paths

When an assistant generates endpoints, handlers, or background jobs, it often brings along default middleware, framework conventions, and error handling behaviors that were never reviewed explicitly. A single generated endpoint can introduce permissive input parsing, broad error messages, missing rate limits, or inconsistent authentication decorators. In distributed systems, it can also create new service-to-service calls that change trust assumptions without updating any architecture artifact.

Refactoring shifts control placement without changing interfaces

Automated refactoring changes where authentication, authorization, validation, and logging actually happen. Controls move from centralized layers into helpers, utilities, or shared libraries, sometimes applied unevenly across code paths. APIs still look the same from the outside, but enforcement timing and consistency change, which is exactly where access control bugs and logic flaws tend to hide.

AI suggestions subtly alter data handling behavior

Many security-impacting changes look like cleanup. Parsing rules get relaxed. Validation switches from strict allowlists to pattern-based checks. Identifiers get normalized or trimmed in ways that create collisions. Error handling becomes more verbose to improve debugging. None of this looks dangerous in isolation, but together it changes how data enters, flows through, and exits the system, often opening paths that never existed in the original threat model.

Why attackers benefit from this pace of change

Attackers do not need new techniques to take advantage of these shifts. They benefit from newly exposed paths, inconsistent enforcement, and edge cases created by fast-moving codebases. Most real-world incidents still come from familiar failures like authorization mistakes, input handling issues, insecure defaults, and unintended exposure of internal interfaces. AI-driven change increases how often those conditions appear, even in otherwise well-run environments.

The attacker only needs one newly reachable path that bypasses an assumption the security team still believes is true. Accelerated delivery increases the odds that such paths exist at any given moment.

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Why manual re-modeling cannot keep up

Trying to re-run traditional threat modeling in response to this change rate does not scale, and the limitation has nothing to do with effort or competence.

The change velocity outpaces human review

Threat modeling requires context, validation, and reasoning across components. Even lightweight reviews take time, and by the time they complete, the system has already evolved again.

Expertise does not scale linearly

High-quality threat modeling depends on people who understand architecture, code, and attacker behavior. AI increases the number of security-relevant changes faster than those experts can review them, even in well-staffed teams.

The model is disconnected from the real change signals

Traditional threat modeling triggers off planned reviews and documentation updates, while real attack surface changes originate in commits, generated diffs, dependency shifts, CI pipelines, and runtime configuration changes. When the model is not tied directly to those signals, it misses where risk actually changes.

AI does not automatically make systems inherently unsafe. It’s a force multiplier, and it multiplies both delivery speed and the rate at which exposure can appear. Static controls and periodic threat models cannot reliably govern environments where attack surface shifts continuously and often quietly.

This is why security programs feel like they are working harder while gaining less confidence. The system is moving faster than the assumptions used to secure it. Addressing that gap requires changing how threat models stay current, not asking teams to manually chase a moving target faster than humans realistically can.

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What Continuous Threat Modeling actually means (and what it does not)

By this point, continuous threat modeling probably sounds like another phrase that promises clarity and delivers overhead. So let’s strip the ambiguity out of it and talk about what this actually means in operational terms, because this only works when expectations are precise.

At its core, continuous threat modeling treats threat models as living system representations, and not as documents you produce, approve, and file away. The model stays current because it updates as the system changes, using the same signals that engineering already generates every day. 

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What is Continuous Threat Modeling?

Continuous threat modeling ties security understanding directly to the reality of how systems evolve. It does not rely on scheduled workshops or static diagrams to stay accurate.

In practical terms, this means:

Threat models update as the system changes

Changes to code, APIs, infrastructure, configuration, and data flows automatically feed into the model. New endpoints, modified auth paths, dependency shifts, and trust boundary changes are reflected as they happen, instead of weeks later during a review cycle.

Models are anchored to real, machine-readable artifacts

The model derives its understanding from source code, API specifications, infrastructure definitions, CI pipelines, deployment configs, and data flow representations. This keeps the model aligned with what is running in production, not what a diagram claimed last quarter.

Security visibility becomes continuous signal

Instead of producing a single output that decays immediately, the model produces ongoing insight into where risk increases, where controls drift, and where new attack paths appear. Security teams see changes in posture as part of normal operations.

Threat context stays scoped to actual changes

Updates focus on what changed and why it matters, rather than forcing teams to re-evaluate entire systems. This keeps attention on meaningful deltas instead of repeating broad, generic assessments.

The model reflects relationships

Continuous models track how services interact, how identities propagate, how data moves across boundaries, and how control enforcement shifts. This is where most real-world risk emerges as systems evolve.

Coverage improves without linear increases in effort

As systems grow and change faster, the model scales with them because it is driven by automation and integration, not by scheduling more human-intensive sessions.

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What Continuous Threat Modeling is not

This is where most misconceptions live, and where resistance usually starts. Continuous does not mean heavier process or constant interruption. It is not:

More meetings or recurring threat modeling workshops

Security does not need to pull engineers into frequent sessions just to keep models current. The point is to reduce dependence on synchronous reviews, and not to multiply them.

More documentation to maintain manually

Teams are not expected to produce extra diagrams, spreadsheets, or long-form write-ups to feed the model. Documentation exists to support understanding, not to satisfy the process.

A flood of alerts on every minor change

Continuous modeling does not mean every commit becomes a security event. The focus stays on changes that materially affect attack surface, trust boundaries, or data exposure.

A replacement for security judgment or accountability

The model surfaces what changed and how risk may shift. Humans still decide what matters, what gets accepted, and what requires action based on business context.

A mandate for engineers to become threat modeling experts

Engineering teams do not need to learn new frameworks or run analyses themselves. Their normal development activity keeps the model current without added cognitive load.

A compliance artifact designed to satisfy auditors

Continuous threat modeling supports defensible security decisions, but it is not a checkbox exercise. Its value comes from accuracy and relevance, and not from volume of output.

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How ownership actually works

For this to work at scale, ownership has to be clear and realistic.

Security owns the threat model. That includes defining relevant threat categories, interpreting trust boundaries, setting risk criteria, and deciding how findings translate into action.

Engineering activity keeps the model current. As teams generate code, refactor services, update APIs, and modify infrastructure, those changes naturally flow into the model without engineers having to stop, document, or formally run threat modeling.

This division is what makes continuous threat modeling sustainable. Security stays accountable for risk decisions. Engineering keeps moving fast. The model stays accurate because it is tied to the same change stream that already drives delivery.

Once framed this way, continuous threat modeling stops sounding like an aspirational practice and starts looking like table stakes for AI-driven environments. It does not add work. It aligns security understanding with reality, which is the minimum requirement for making defensible decisions at speed.

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Keeping threat models alive requires automation with real oversight

Keeping threat models current across an AI-accelerated codebase requires automation, because the volume and frequency of change outpace what human reviews can cover. That does not mean you hand the keys to an AI system and treat its output as truth. Unmanaged automation creates the same kind of false confidence security teams already learned to hate from noisy scanners, it just moves the problem earlier in the lifecycle and makes it harder to spot.

The right mental model is simple. Automation keeps coverage and detects drift. Humans decide what matters, what gets fixed, what gets accepted, and what gets escalated. When you keep that separation clear, continuous threat modeling becomes scalable without turning into another source of risk.

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Where automation adds real value

Automation earns its place when it watches the change stream closely and translates it into security-relevant deltas that humans can act on. That means tracking what actually changes, and not what someone remembered to document.

The high-leverage automation capabilities look like this:

Detect changes in architecture and data flow from real artifacts

Monitor repos, API specs, IaC, service mesh configs, gateways, and CI/CD pipelines to identify new services, new endpoints, new dependencies, and new connectivity paths. This is where the attack surface expands in modern systems, and it is also where manual threat models fall behind first.

Flag when assumptions no longer hold

Threat models are built on assumptions such as “this endpoint is internal,” “this service only receives validated input,” “this data store never holds regulated data,” or “this trust boundary is enforced at the gateway.” Automation can detect changes that undermine those assumptions, like an endpoint becoming internet-reachable through an ingress change, a validation layer being bypassed by a new code path, or sensitive fields appearing in a data flow that previously did not carry them.

Maintain coverage across large and fast-changing systems

At scale, the problem is not a single missed threat model, it is drift across hundreds of components. Automation can continuously map inventory, relationships, and trust boundaries so security does not lose visibility as teams add microservices, split domains, adopt new third-party components, or restructure auth and identity propagation.

Surface security-relevant diffs rather than raw change noise

The best automation does not alert on every commit. It highlights deltas that change exploitability, exposure, or control enforcement, such as new unauthenticated routes, changed authorization checks, new deserialization paths, new cross-service calls, or new storage of sensitive data.

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Where humans stay critical

Automation can tell you that something changed and it can propose what that change might imply. It cannot own business risk, it cannot understand what trade-offs leadership is willing to make this quarter, and it cannot carry accountability when something goes wrong.

Human judgment stays essential in the places that determine real outcomes:

Business impact decisions

The same technical issue means different things depending on customer impact, regulatory exposure, and operational blast radius. Humans connect technical risk to business consequence.

Risk acceptance and compensating controls

Accepting risk is an executive decision backed by security rationale. Automation can provide evidence and options, but approval, ownership, and compensating control design belong to humans.

Contextual prioritization

Automation can rank signals, but prioritization requires context like threat activity against your industry, real asset value, control maturity, deployment realities, and what teams can deliver without breaking production.

Validation of model outputs and boundary interpretations

A machine can infer trust boundaries and data flows from artifacts, but humans confirm whether those boundaries are real in production, whether an assumed control actually enforces policy, and whether the described flow matches runtime behavior.

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Common failure modes that create new risk

These failures rarely look dramatic in the moment. They accumulate quietly and show up later as confidence gaps during incidents, audits, or post-mortems.

• Generated threat models can sound precise while still being wrong. When teams stop questioning assumptions, security decisions drift away from reality.
• Without routine sampling and human review, errors compound over time. The model continues producing output that looks consistent but no longer reflects the system.
• False positives, dismissed findings, and confirmed issues never improve future output. The system repeats the same mistakes release after release.
• High service coverage means little when trust boundaries and data flows are misinterpreted. Broad visibility without correctness creates false confidence.
• Flagging every commit trains teams to ignore the signal. Meaningful shifts in exploitability get buried under noise.
• Models built only from code and diagrams miss behavior enforced at gateways, platforms, or cloud services. Security assumptions break when runtime reality differs from design intent.
• Individual services look safe in isolation. Risk emerges where services interact, share identity, or exchange data across boundaries.
• AI-generated logic often reflects framework defaults, instead of organizational policy. Controls that are mandatory in hand-written code can be absent or inconsistently applied.
• Findings without clear owners stall indefinitely. Risk does not reduce just because it was detected.
• Models require tuning as architectures, teams, and threat landscapes evolve. Neglect turns automation into another stale control.

These are the predictable failure modes that appear when automation runs without structure, feedback, and human judgment. Avoiding them is what keeps continuous threat modeling from becoming another source of silent exposure instead of the control it is meant to be.

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Continuous threat modeling should fit into how engineers already ship

Continuous threat modeling only works when it rides along with existing engineering workflows, because that is where the real change signal lives. Security does not need another parallel process, it needs a way to detect meaningful shifts in attack surface as they happen, then turn those shifts into actions that engineers can take without slowing delivery.

In practice, this is less about tools and more about integration points and output discipline. You pick the moments where engineering already declares intent through artifacts, then you generate security signal that is scoped to what changed, easy to validate, and easy to act on.

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Where continuous threat modeling gets triggered

These triggers map to real changes in exposure and trust boundaries, so they give you leverage without forcing teams into extra ceremonies.

Pull requests

PRs are where code and intent meet review. Triggering here lets you detect new endpoints, changed authorization logic, new parsing and deserialization paths, new outbound calls, and changes to sensitive data handling, then tie the threat model delta directly to the diff that introduced it.

CI/CD pipeline changes

Pipeline updates often change guardrails, build steps, artifact promotion rules, secret handling, and deployment flows. When a pipeline change reduces security checks, alters signing and provenance, or modifies environment promotion paths, the threat model should reflect that control drift immediately.

Infrastructure and configuration updates

IaC changes regularly introduce real exposure, such as new ingress rules, widened network paths, permissive IAM roles, new public buckets, relaxed firewall rules, or changed service-to-service routing. Triggering on Terraform, Helm, CloudFormation, Kubernetes manifests, and gateway policy updates catches trust boundary shifts that rarely show up in application design docs.

New APIs, schemas, and data stores

API specs and schema changes are direct indicators of attack surface expansion and data exposure risk. New endpoints, new GraphQL fields, new event topics, new queues, and new storage systems should trigger updates that reflect new entry points, new data flows, and new abuse cases.

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What outputs should look like inside real workflows

The output format determines whether this gets adopted or ignored. CISOs can mandate continuous threat modeling, but engineers will tune out fast when the output looks like another report that nobody has time to read. The output needs to be:

Actionable signal

Deliver findings as a small set of high-confidence deltas tied to the change, such as new unauthenticated route introduced, authorization check moved after resource fetch, new external call added without timeout and allowlist, or sensitive field introduced into logging path

Tied to what actually changed

The model should point to the specific file, endpoint, policy, or resource that changed, and explain why that change affects trust boundaries, data exposure, or exploitability. This keeps the feedback grounded in the diff, which makes review and remediation realistic.

Scoped to blast radius

Show which components and flows are affected, instead of generic threat lists. A useful output identifies which service gained exposure, which identity boundary shifted, what data moved, and which downstream systems now sit in the attack path.

Mapped to the control that should exist

Engineers move faster when you connect the signal to a concrete control such as auth middleware, policy-as-code, input validation, rate limiting, egress restrictions, secrets handling, or logging redaction. This reduces back-and-forth and keeps security guidance consistent.

Designed for human validation

A reviewer should be able to confirm or dismiss the signal quickly, because high-friction validation kills adoption. The output should make it obvious what evidence the system used, and what assumption it believes is now unsafe.

Threat modeling can be something else other than a scheduled event and, instead, become a background capability that tracks the system as it evolves. Security teams spend less time chasing documentation and more time reviewing meaningful changes, validating assumptions, and making risk decisions with current evidence. Engineering teams stop seeing threat modeling as a meeting they have to endure, because the work shows up where they already operate, in PRs, pipelines, and infrastructure changes, and it shows up as small, precise actions tied to their code.

This change reduces fear of disruption because it does not require slowing delivery to do threat modeling. It makes threat modeling part of delivery, by turning continuous system change into continuous security awareness, with clear ownership and defensible decision points.

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Why this matters for compliance, audits, and board-level risk conversations

At some point, threat modeling stops being an AppSec discussion and becomes a governance problem. The question is not whether your team can produce a threat model, but whether you can defend the security decisions that model drove, using evidence that matches the system as it exists today.

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The audit problem is staleness

Audits and compliance reviews reward traceability. They expect you to show what you reviewed, when you reviewed it, what you decided, and what you did to keep it current as the system evolved. Static threat models struggle here because they represent a past version of the system, while your auditors are evaluating current controls, current exposure, and current operational oversight.

And this is where your teams struggle the most. They show a threat model, but it does not line up cleanly with current APIs, current data stores, current cloud configuration, or current identity flows. They can talk through intent, but intent is not evidence. A stale model creates a predictable failure pattern: lots of documentation, weak linkage to the running system, and a reliance on reviewing during the design stage that does not hold up under scrutiny when delivery moves continuously.

Continuous threat modeling helps because it produces audit-friendly artifacts that map to real change and real oversight:

Traceable decision history tied to system changes

Security decisions attach to the pull request, the infrastructure change, the new API spec, or the pipeline update that introduced the exposure, which gives you a clean chain from change to assessment to mitigation or acceptance.

Evidence of ongoing control verification

Instead of proving that a workshop happened, you prove that risk was monitored as the system evolved, with a record of signals, reviews, and actions over time.

Clear mapping between assumptions and current reality

Assumptions like internal-only, authenticated, encrypted at rest, or validated at the gateway can be continuously checked against actual configuration and code paths, then flagged when they drift.

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The board problem is answering are we exposed right now

Board-level risk conversations have changed. Leaders do not want a lecture on secure development. They want a clear answer to a simple question: what is our exposure right now, and are we managing it responsibly.

Most teams cannot answer that with confidence, even when they have strong security programs, because their evidence is lagging. Security reporting often reflects completed reviews, passed scans, and approved threat models that represent a prior state. That is not the same as a current risk view, and executives can feel that even when they cannot name it.

Continuous threat models give you a defensible way to talk about present risk without overpromising certainty. You can show what changed recently, what risk those changes introduced, what controls cover it, where coverage is incomplete, and what decisions were made with dates and owners attached.

When incidents happen, this matters even more. A continuous model supports a narrative grounded in evidence:

• What the system looked like when the decision was made
• What changed afterward that altered exposure
• Which signals detected the change and when
• Who reviewed it, what actions were taken, and what was accepted with justification

That narrative is what holds up in executive reviews, regulator conversations, and post-incident scrutiny, because it demonstrates governance and accountability rather than relying on process claims.

Threat modeling becomes useful at the top of the house when it stops being treated as a security chore and starts functioning as a risk management asset. Continuous threat models give you that shift, because they connect day-to-day engineering change to board-level questions about exposure, oversight, and defensibility.

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How often do your threat models actually reflect production reality?

Threat modeling as a security artifact? You should start changing that mindset right now and start treating it as a source of operational truth. The real risk here is not missing a vulnerability, but making confident decisions based on assumptions that quietly expired while delivery kept moving.

And this will only get worse from here. AI-assisted development will keep compressing design and implementation into the same motion. Regulatory scrutiny will keep shifting toward evidence of ongoing oversight instead of point-in-time reviews. Teams that rely on static models will struggle to explain why their controls looked reasonable on paper but failed to reflect the system that was actually running.

But there's also opportunity hiding here. Continuous threat modeling gives security leaders a way to anchor risk conversations in current reality, without slowing engineering or inflating processes. It becomes a shared reference point across security, engineering, compliance, and leadership, grounded in how the system behaves now, and not how it was intended to behave months ago.

So how about aligning threat modeling with the same signals that already drive modern delivery, then using that visibility to make clearer, faster, and more defensible decisions?