In my first blog of this two-part series, I broke down the five automation metrics and principles I rely on most to help leadership demonstrate value. This second blog builds on that thinking. In my e-book, ‘Network automation in 2026: building resilience, assurance and future-ready networks’, I explained that one of the biggest challenges that network and operations leaders face today is making every change safe. 

Automation is not just about efficiency, but maintaining control within modern networks that are dynamic, distributed and tightly-connected to cloud platforms and third-party services. While automation is essential, speed without control creates risk. By unifying the three capabilities of assurance, observability and lifecycle management, it becomes possible to execute network changes in a safe and repeatable way.

Assurance: Validate before and after every change

For me, assurance is the foundation. Validate every change is safe and compliant before it goes live, then confirm it behaves as intended after deployment. Continuous validation before and after every change is now expected, helping to ensure changes are safe and compliant. Streaming telemetry and service mesh architectures provide real-time visibility, making it easier to spot issues and respond quickly

How to implement assurance:

• Define policies as code and embed them in your pipeline. 
• Run intent checks to catch misconfiguration and drift early. 
• Use change windows that include automated validation and safe rollback paths.

Outcome: Fewer failed releases and emergency fixes and better audit outcomes because evidence is generated as part of normal work. 

Observability: Real insight from streaming telemetry

In my first blog, I covered MTTR and MTTD with the time it takes you to detect issues and restore normal service. Observability is what drives this. Move beyond static, device-centric health checks to provide continuous visibility across paths, services and users.

How to implement observability: 

• Stream telemetry from network and edge assets into a common model. 
• Use service mesh patterns where appropriate to trace requests end-to-end. 
• Align dashboards to service objectives, not individual devices. 

Outcome: Faster detection, clearer root cause and performance data that stakeholders can actually trust. 

Lifecycle management: Remove tech debt as you modernise

Teams often try to automate on top of legacy risks. Lifecycle management prevents that. You plan upgrades, renewals and retirements proactively to prevent new changes from piling risk onto legacy.

How to implement lifecycle management: 

• Maintain an accurate inventory and map controls to business risk. 
• Standardise on reference designs that are easier to secure and support. 
• Budget for renewal and decommissioning alongside new projects. 

Outcome: Lower exposure, simpler operations and a platform that adapts as the business evolves. 

How to implement a safe automation framework

To bring assurance, observability and lifecycle management together for safe automation, I recommend organisations consider the following best practices:  

1. Start with responsibility: Assign clear owners for providers and controls. Everyone should know who approves what. 
2. Use reference designs: Build simple patterns that map known threats to specific controls, then reuse them. 
3. Automate safely: Codify configuration and policy, prevent drift and escalate recovery with tested rollbacks. 
4. Adopt Zero Trust: Assume breach, verify access and enforce least privilege across sites and clouds. 
5. Strengthen monitoring: Track performance, changes, access and compliance in one place. 
6. Keep governance practical: Set standards that teams can follow, measure them and iterate. 

What to measure

To make progress visible and defensible, you can refer back to the core metrics from my e-book and previous blog:  

• Change success rate and rollback avoidance 
• MTTR and MTTD
• Compliance score and drift
• Latency and packet loss against service objectives.

These metrics will help you determine whether your automation is actually making change safer.  

Two quick wins for the first 30 days

If you want to quickly build momentum, I recommend: 

• Pre-change validation on one high-traffic service: Add automated checks for policy compliance and performance impact, then track the effect on change success rate. 
• Drift detection with weekly remediation: Choose a critical domain, enable drift alerts and close gaps to raise your compliance score. 

Where SD-WAN and SASE fit

At the edge, SD-WAN and SASE extend consistent policy and observability to every site. They simplify operations, support identity-led access that aligns to Zero Trust and reduce risks from technical debt and legacy systems so networks can adapt securely as business needs evolve. 

How we can help

In my work with clients, I see the same challenge time and again: network change needs to move faster, but it also needs to be safer and more predictable. At CACI, we help organisations bring structure, visibility and governance to complex networks so change can happen with confidence. 

We support teams in putting practical assurance and observability in place, improving lifecycle management and reducing configuration drift, without slowing delivery. That means fewer regressions, clearer accountability and a more predictable change pipeline.
 
If you’d like to explore how this approach could work in your environment, visit our Network Automation page to start the conversation with our specialists. 
 
You can also download my new Network Automation in 2026 eBook for a deeper dive into how assurance and automation work together to build resilient, future-ready networks. 

In my new e-book ‘Network automation in 2026: building resilience, assurance and future-ready networks’, I uncover how network automation is no longer just about speed, but about reducing operational risk, strengthening compliance and stabilising services when the unexpected strikes. To meet the expectations of leadership, network automation must clearly demonstrate its ability to deliver on outcomes.  

This first blog in a two-part series breaks down five automation metrics and principles I rely on to help advise leadership: practical, executive-friendly and aligned to how boards evaluate resilience, risk and customer experience.

1. Change success rate and rollback avoidance 

What it is: This is the proportion of changes that complete as planned without causing incidents or requiring rollback. 
Why it matters: In my experience, this is one of the fastest ways to prove to leadership that automation is about increasing safety and predictability, not just throughput. 

How to improve:  

• I always begin with applying pre-change validation, policy gates and standardised reference designs that map controls to threats with simple, repeatable patterns. These give teams simple, repeatable patterns that map controls to threats. 
• Instrument your pipelines to capture change outcomes automatically.
• Assign clear ownership to execute each change and align teams.  

What good looks like: A steady rise in successful, first-time changes and a consistent fall in rollbacks over consecutive release cycles. 

2. Mean time to detect (MTTD) and mean time to repair (MTTR)

What it is: The time it takes you to detect issues and restore normal service. 
Why it matters: I find that detection and recovery are very important for leadership, especially because automation and observability deliver measurable business value. 

How to improve:  

• Stream all of your telemetry into a single view, then use intent checks to highlight drift or policy violations and automate first line remediation where safe.  
• Strengthen monitoring by tracking network performance, changes, access, compliance and security events.

What good looks like: Faster detection windows followed by runbook-driven recovery that is measured in minutes, not hours.

3. Compliance score and configuration drift

What it is: A combined indicator of how closely your estate aligns to policy and how far it strays from approved configurations. 
Why it matters: Boards and auditors need confidence that controls are enforced consistently across hybrid estates. 

How to improve:  

• Treat policies as code and run continuous checks.  
• Block non-compliant changes before they land.  
• Generate audit evidence automatically to save a huge amount of time.  
• Keep governance practical by setting clear standards, control owners and measurable policies. 

What good looks like: A rising compliance score with drift trending down. Exceptions are documented and time-boxed. 

4. Alert volume reduction

What it is: A measure of how many alerts actually correlate to meaningful incidents. 
Why it matters: High alert volume hides real risk and drains team capacity. 

How to improve:  

• Consolidate tooling, de-duplicate at the source, only measuring what maps to user or service objectives.  
• Safely automate by applying Infrastructure as Code and Policy as Code to prevent drift and speed up recovery.

What good looks like: Fewer alerts, higher signal quality and a clear link between alerts and customer impact. 

5. Latency and packet loss against service objectives

What it is: End-to-end performance measured against the targets that matter most for your services. 
Why it matters: User experience is the ultimate goal. Device health means little if transactions stall. 

How to improve:  

• Set service-level objectives (SLOs) for your priority journeys, instrument path visibility and factor network changes into performance reviews.  
• Adopt Zero Trust principles to assume breach, verify access and enforce least privilege.  

What good looks like: Stable or improving latency and loss for your top services, even during high change periods. 

How to get started 

I recommend teams start small when adopting these metrics, but take the following into consideration: 

1. Select two high impact metrics that you can measure today. 
2. Automate the collection and reporting so data is timely and trusted.
3. Share a simple scorecard with trend lines and short commentary.
4. Only add more metrics when the first set is stable. 

How we can help

In my work with CIOs, one of the biggest challenges I see is turning network automation into something that’s measurable, governed and trusted. At CACI, we help organisations align automation with business goals, reduce operational risk and create real clarity around performance and compliance. 

We bring proven architectures, practical operating models and clear measurement frameworks, so teams can track success rates, reduce configuration drift and improve incident response. We also help teams build simple, outcome focused scorecards that connect day-to-day network activity to executive priorities. 

If you’d like support establishing a metrics baseline or shaping an automation roadmap around the principles in this blog, visit our Network Automation page to learn more or get in touch with our specialists. 

You can also download my Network Automation in 2026 eBook for a deeper look at the frameworks and metrics that high performing organisations are using today. 

In the next blog in this series, I’ll explore how assurance, observability and lifecycle management work together to make every network change safe. 

What is Model Based Systems Engineering (MBSE)? A practical explainer for modern engineering 

Engineering domains like defence, automotive, manufacturing and critical infrastructure have always dealt with complexity. But today that reality is compounded by volatility. One seemingly small change can ripple across an entire architecture: a single component going end of life forces updates to requirements, interfaces and test plans or single regulatory change means revisiting assumptions and evidence across multiple teams.  

Traditional, document heavy engineering methods simply weren’t designed for this pace, scale and level of interdependence. Big static specifications, linear stage gated processes and manual drafting and review cycles are slow, siloed and paperwork driven; they just can’t keep up with environments that depend on fast iteration, shared data, and real-time collaboration. 

Model Based Systems Engineering (MBSE) offers a more coherent way forward. It makes models, rather than documents, the primary way of understanding how a system is put together and how it behaves under change. And while it’s often discussed in abstract terms, its value is practical: clearer decisions, fewer surprises and systems that can evolve with the world around them. 

Understanding Model Based Systems Engineering 

Traditional systems engineering spreads knowledge across separate artefacts: requirements lists, design specifications, interface control documents, test plans and more. Each serves a real purpose, but together they create a fragmented picture that engineers must mentally stitch together. 

MBSE brings this information into a single system model. Instead of navigating isolated, and typically manual, documents, engineers work with a visual, traceable representation of requirements, behaviours, structures and constraints across the system’s lifecycle: from concept and design through to operation and decommissioning. 

This connected view enables teams to: 

• Simulate and validate designs before physical implementation 
• Understand the implications of a change across the whole system or system-of-systems 
• Maintain traceability between requirements, design and testing as the system evolves 
• Accommodate iterative and Agile delivery without losing architectural coherence 
• Establish a strong foundation for digital twins and digital continuity 

In short, MBSE replaces a fragmented understanding with a coherent one. By shifting the focus from assembling information to analysing the system as a dynamic whole, it makes decisions clearer and enables swifter action. 

MBSE vs. Enterprise Architecture – what’s the difference? 

As an approach, MBSE is often mentioned alongside or confused with Enterprise Architecture (EA) because both use models to bring structure to a changing, interconnected world. They sit on a continuum, but they don’t do the same job. 

Enterprise Architecture works at the organisational level, the so-called ‘30,000ft view’. It defines the capabilities the business needs, the processes that support them, the information that flows between them and the technology principles that keep everything aligned. EA sets the strategic intent and the architectural constraints within which engineered systems must operate. 

Model Based Systems Engineering works at the system level and, critically, does so visually. It uses graphical models to capture requirements, behaviour, structure and constraints so engineers can see how a system works, how its parts interact and how changes flow across the architecture. MBSE can represent a single engineered system or a “system of systems”, depending on the scale of the environment.  

In plain engineering terms: 

• EA defines the environment: capabilities, context, constraints.
• MBSE defines the system: behaviour, architecture, verification.

EA sets the intent; MBSE delivers the model‑based technical design that realises that intent. So, even when a “system of systems” MBSE model approaches EA in scope, it’s still serving a different purpose. Both disciplines tackle the same operational pressures but address them from different vantage points. 

Model Based Systems Engineering in practice 

In practice, MBSE means working from a dynamic system model that brings together the elements that matter most in complex engineering environments. Typically visualised in a dashboard, it provides a traceable, queryable representation of the system as a single point of truth, containing: 

• Requirements
• Behaviours and interactions
• System structure and architecture
• Constraints and dependencies
• Lifecycle considerations from concept to decommissioning

The shift from documents to models isn’t cosmetic. Documents age; models evolve. Documents sit in silos; models connect disciplines. Documents tell you what the system was; models show you what the system is — and what it could be as it adapts to new constraints, technologies or missions. 

Most organisations use modelling languages such as SysML and tools like Cameo, Rhapsody or Enterprise Architect. SysML remains the most widely used, giving teams a standardised way to express structure, behaviour and constraints across complex systems. But the tools are only the enablers. The real value lies in the clarity, consistency and shared understanding that modelling brings. 

The operational benefits – why MBSE matters in modern engineering

 MBSE gives teams a coherent view of how a system behaves and how change in one area affects others and, fundamentally, a more honest representation of how systems behave in the real world. That shift enables: 

• Earlier validation and simulation
• Clearer communication across disciplines
• Faster impact analysis
• Stronger traceability between requirements, design and testing
• Enhanced collaboration across teams and suppliers
• Scalability for managing large, multicomponent or “system of systems” architectures

This is why MBSE has become particularly relevant in sectors where systems are large, long-lived and safety or mission critical.  

In defence and aerospace, it supports mission level traceability, interoperability across suppliers and stronger evidence for certification. In automotive, it helps integrate mechanical, electrical and software design in increasingly software defined vehicles. And in digital and critical infrastructure, it provides a way to map dependencies, model resilience and design for long-term adaptability. The common theme being MBSE provides the clarity needed to make confident decisions. 

What good MBSE delivery looks like in practice 

Successful MBSE programmes have less to do with tools and more to do with delivery behaviours. The organisations that get the most value tend to share a few consistent patterns: 

• Models are treated as living artefacts. They evolve as understanding deepens, rather than being produced once and filed away. 
• Iteration is normal. Teams model early, test assumptions quickly and refine as they learn, instead of waiting for a single ‘big reveal’. 
• Commercial and governance frameworks allow change. MBSE only works when contracts, schedules and decision gates accept that things will evolve. 
• Practitioners lead the work. Systems engineers, architects and domain specialists shape the model, ensuring it reflects real world behaviour rather than abstract theory. 
• Collaboration is built in. Modelling becomes a shared activity across disciplines, not something done in isolation by a single specialist. 

These principles also shape how CACI deliver MBSE.  

Our teams work iteratively, use models to drive shared understanding and keep architectures traceable as requirements evolve. We focus on the behaviours that make MBSE effective, clarity, adaptability and practitioner led modelling – because these consistently help programmes navigate complexity and make better decisions. 

Why MBSE is becoming essential 

 Recent research finds the number and intensity of system level dependencies is rising across every major engineering domain, increasing the likelihood that local failures propagate far beyond their point of origin. The PanIberian blackout in April 2025 made this clear: the energy disturbance cascaded across two national grids, disrupting transport, healthcare and communications within minutes.  

In this context, MBSE becomes a core competency rather than a niche specialism. But its value depends on how it is delivered, and by who.  

A strong MBSE approach provides clarity, traceability and better decisions. It reduces risk. It helps engineering systems evolve with the environment. And in sectors where the stakes are high like defence, automotive, aerospace and critical infrastructure, that combination is not optional, it’s foundational — and increasingly essential if organisations are to stay ahead of the rising fragility built into the systems they depend on. 

To find out how CACI can help your organisation build the resilience needed to operate effectively in an increasingly volatile, interconnected engineering environment, get in touch with our experts today. 

FAQs about Model Based Systems Engineering (MBSE)

What does “model-based” actually mean in Model Based Systems Engineering (MBSE)?

In Model Based Systems Engineering (MBSE), “model-based” means that system information is stored in a structured, machine-readable model rather than free-text documents. This allows relationships, dependencies and constraints to be queried, analysed and validated automatically instead of being inferred manually.

Is Model Based Systems Engineering only suitable for large or complex systems?

No. While MBSE is most visible in large, complex programmes, it can also be valuable for smaller systems where change is frequent or assurance requirements are high. Even lightweight models can reduce ambiguity, improve communication and prevent rework as designs evolve.

How does MBSE support verification and validation activities?

MBSE enables verification and validation by explicitly linking system behaviours and constraints to verification criteria within the model. This allows teams to assess test coverage, identify gaps early and maintain alignment between design intent and evidence as the system changes.

What skills are required to work effectively with Model Based Systems Engineering?

Effective MBSE requires a combination of systems thinking, domain expertise and modelling literacy. While familiarity with languages such as SysML is useful, the most important skills are the ability to reason about system behaviour, understand trade-offs and communicate across disciplines using models as a shared reference.

How does Model Based Systems Engineering improve decision-making?

MBSE improves decision-making by making assumptions, dependencies and impacts explicit. Engineers and stakeholders can explore “what-if” scenarios, assess trade-offs and understand consequences before changes are committed, reducing the risk of late-stage surprises.

Can Model Based Systems Engineering be applied to legacy systems?

Yes. MBSE can be introduced incrementally to legacy environments by modelling critical parts of an existing system rather than attempting a full re-engineering effort. This approach helps organisations gain insight into dependencies, constraints and risks without disrupting ongoing operations.

How does MBSE fit with safety, regulatory and assurance frameworks?

MBSE supports safety and regulatory assurance by providing a structured way to demonstrate traceability from requirements through design to verification evidence. This can simplify audits, improve confidence in compliance claims and reduce the effort required to respond to regulatory change.

What are common misconceptions about Model Based Systems Engineering?

A common misconception is that MBSE is primarily a tooling or documentation exercise. In practice, its effectiveness depends on how models are used to support collaboration, learning and decision-making — not on the level of detail or the sophistication of the tools alone. 

When it comes to strengthening your network security posture, doing so is no longer a nice-to-have, but a strategic necessity. The notion of strengthening your network may sound time-intensive and lengthy, however, there are some immediate changes that can lead to quick wins. In this blog, we uncover four key steps IT leaders can take to strengthen network security posture and immediate quick wins that can be achieved upon doing so.  

Four steps to strengthen your network security posture

Security is no longer optional. These four foundational actions will help you reduce risk and build resilience: 

1. Adopt zero trust principles

Zero trust means “never trust, always verify.” Every user and device inside or outside the network must be authenticated and authorised. This approach limits the impact of breaches and is now recommended by the NCSC and leading global providers.  

• Implement strong authentication for all users and devices.  
• Segment networks to limit lateral movement.  
• Continuously monitor for unusual behaviour.  

2. Automate detection and response

Manual processes cannot keep pace with modern threats. Automation can reduce response times by up to 40%, demonstrating its ability to help defenders stay ahead. 

• Use AI-driven tools for threat detection and alert triage.  
• Automate patching, backup, and incident response workflows.
• Regularly test and updated automated playbooks.

3. Operational load

With many IT teams stretched thin, managed network services allow organisations to focus on strategy while experts handle day-to-day operations, monitoring and compliance. 

• Consider managed firewall, detection and response and vulnerability management services.  
• Ensure providers offer transparent reporting and clear SLAs.

4. Secure hybrid work

With two-thirds of UK employees working remotely at least part-time, endpoint protection and secure remote access are essential.  

• Enforce multi-factor authentication for all remote access.  
• Protect endpoints with up-to-date security software and policies.
• Educate staff on secure working practices. 

Quick wins: Immediate actions UK IT leaders should take 

Not every improvement requires a major investment or a long-term project. The following actions can quickly reduce risk and strengthen your security posture:  

Enable multi-factor authentication (MFA) 

Multi-factor authentication (MFA) is one of the most effective ways to prevent account compromise, blocking the majority of phishing and credential stuffing attacks.  

• Enforce MFA for all users, not just administrators.  
• Use app-based or hardware tokens for stronger protection. 
• Regularly review and test MFA coverage.  

Read NCSC guidance on MFA  

Patch the basics consistently and quickly

Most breaches exploit known vulnerabilities. Even delays in patching of a few days can be costly.  

• Maintain an up-to-date inventory of all assets, including cloud workloads and remote endpoints. 
• Apply critical patches within 14 days, as recommended by the NCSC.  
•  Automate patch deployment and monitor for failures.  

Back up critical data securely and test your restores

Ransomware is only effective if you cannot recover your data. Secure, tested backups are essential.  

• Use immutable, offsite or cloud-based backups.  
• Regularly test restores to ensure data integrity.  
• Protect backup credentials with MFA and restrict access.

Review firewall rules and access controls

Firewall policies can become cluttered over time with unused or overly permissive rules, creating hidden vulnerabilities.  

• Schedule regular firewall reviews to remove unused or risky rules.  
• Align policies with current business needs.  
• Use automated tools to analyse policies for overlaps and compliance gaps.   

Run a tabletop incident response exercise 

Plans are only effective if teams can execute them under pressure. Tabletop exercises simulate real-world incidents, allowing teams to rehearse roles and identify gaps.  

• Involve both technical and business stakeholders.  
• Use realistic scenarios tailored to your organisation.
• Capture lessons learned and update your incident response plan.  

See NCSC’s guidance on incident response exercises 

How CACI can help enhance your network security

CACI has helped UK businesses protect their networks for decades. From network security to data centre solutions and IT consulting, our expertise delivers secure-by-design architectures, automation, and incident readiness for robust network security.  

Download our 2026 Network Security Survival Guide today to learn more about how your organisation can set its network environments up for success. 

The demand for cloud-based offerings and cloud adoption has accelerated, with the importance of flexibility and agility now being realised. Without adapting, businesses risk being left behind. What are the benefits, however, and how do you know if it’s the right solution for you? 

We shared the key advantages of cloud adoption in our previous blog. This time around, we identify the biggest challenges of cloud security. 

Cloud adoption has become increasingly important in recent years, with 64% of all enterprises now regarding cloud security as a pressing security discipline. Despite its integral role, more than half of all enterprises find securing cloud environments to be more complex than securing on-premises venues. 

As cybercriminals increasingly target cloud environments, the pressure is on for IT leaders to protect their businesses. Here, we explore the most pressing threats to cloud security you should take note of. 

Limited visibility

The traditionally used tools for gaining complete network visibility are ineffective for cloud environments as cloud-based resources are located outside the corporate network and run on infrastructure the company doesn’t own. Furthermore, most organisations lack a complete view of their cloud footprint. You can’t protect what you can’t see, so having a handle on the entirety of your cloud estate is crucial. 

Lack of cloud security architecture and strategy

The rush to migrate data and systems to the cloud meant that organisations were operational before thoroughly assessing and mitigating the new threats they’d been exposed to. The result is that robust security systems and strategies are not in place to protect infrastructure. 

Unclear accountability

Pre-cloud, security was firmly in the hands of security teams. In public and hybrid cloud settings, however, responsibility for cloud security is split between cloud service providers and users, with responsibility for security tasks differing depending on the cloud service model and provider. Without a standard shared responsibility model, addressing vulnerabilities effectively is challenging as businesses struggle to grapple with their responsibilities. This not only obfuscates incident response, but increases the likelihood of risks and misconfigurations. 

Misconfigured cloud services

Misconfiguration of cloud services can cause data to be publicly exposed, manipulated or even deleted. It occurs when a user or admin fails to set up a cloud platform’s security setting properly. For example, keeping default security and access management settings for sensitive data, giving unauthorised individuals access or leaving confidential data accessible without authorisation are all common misconfigurations. Human error is always a risk, but it can be easily mitigated with the right processes. 

Data loss

Data loss is one of the most complex risks to predict, so taking steps to protect against it is vital. The most common types of data loss are: 

• Data alteration – when data is changed and cannot be reverted to the previous state. 
• Storage outage – access to data is lost due to issues with your cloud service provider. 
• Loss of authorisation – when information is inaccessible due to a lack of encryption keys or other credentials. 
• Data deletion – data is accidentally or purposefully erased, and no backups are available to restore information. 

While regular back-ups will help avoid data loss, backing up large amounts of company data can be costly and complicated. Nonetheless, ransomware attacks swelled by 126% earlier this year, reiterating the necessity for businesses to conduct regular data backups.  

Malware

Malware can take many forms, including DoS (denial of service) attacks, hyperjacking, hypervisor infections and exploiting live migration. Left undetected, malware can rapidly spread through your system and open doors to even more serious threats. That’s why multiple security layers are required to protect your environment. 

Insider threats

While images of disgruntled employees may spring to mind, malicious intent is not the most common cause of insider threat security incidents. Worryingly, the frequency of insider-led incidents is on the rise. According to a report published this year, nearly half of the organisations surveyed noticed an increase in the frequency of their insider threats. The financial repercussions of this increase have led to costs increasing by 109% between 2018 to 2024, posing serious financial risks to affected organisations. 

Compliance concerns

While some industries are more regulated, you’ll likely need to know where your data is stored, who has access to it, how it’s being processed and what you’re doing to protect it. This can become more complicated in the cloud. Furthermore, your cloud provider may be required to hold specific compliance credentials. 

Failure to follow the regulations can result in substantial legal, financial and reputational repercussions. Therefore, it’s critical to handle your regulatory requirements, ensure good governance is in place and keep your business compliant. 

API vulnerabilities

Cloud applications typically interact via APIs (application programming interfaces). However, insecure external APIs can provide a gateway, allowing threat actors to launch DoS attacks and code injections to access company data. 

In 2020, Gartner predicted API attacks would become the most frequent attack vector by 2022. With over half of all enterprises reporting an increase in direct attacks to compromise infrastructure as of 2025, this prediction has become a reality. Addressing API vulnerabilities will therefore be a chief priority for IT leaders in 2025 and beyond. 

Check out our comprehensive guide to cloud security for more insights on overcoming these challenges and safeguarding your business against evolving threats.

Cloud innovation trends: Why optimisation must come first

In the race to modernise, many businesses make a critical mistake: innovating before optimising their cloud infrastructure. It’s an easy trap to fall into – new technologies promise speed, agility and competitive advantage. However, without a solid foundation, those promises can quickly unravel.

So, what difference will optimisation make to cloud innovation? How do complex hybrid environments affect optimisation and what are the repercussions of innovating too soon?

Why optimisation should come first

Cloud optimisation isn’t just a technical exercise – it’s a strategic imperative. Before you invest in AI-driven tools, advanced analytics or multi-cloud deployments, you need to ensure your existing environment is efficient, secure and cost-effective. Otherwise, innovation becomes a gamble rather than a growth driver.

How the complexity of hybrid environments affects optimisation

Modern IT landscapes are rarely simple. Most organisations operate in hybrid environments, combining:

• Cloud-native workloads
• Semi-native applications
• Containerised services
• Legacy systems migrated via IaaS.

This mix introduces complexity that can quietly erode ROI and performance. Without optimisation, you risk inefficiencies that undermine every future initiative.

Common pitfalls of innovating too soon

When businesses rush to innovate without first optimising, they often encounter:

Duplicated workloads

Hybrid setups frequently lead to duplication of environments or services, especially when containerised and legacy systems overlap with cloud-native tools. This consumes bandwidth and burdens IT and DevOps teams with managing multiple versions of the same workload.

Latency issues

Poor workload distribution across cloud environments increases latency, slowing response times and masking compliance or security issues. For customer-facing applications, this can directly impact user experience and brand reputation.

Security saps

Unoptimised containerised and legacy workloads are vulnerable to governance and compliance risks. Differences in data storage and flow between environments complicate tracking, while unresolved legacy issues can carry over post-migration.

Mounting costs

With up to 30% of cloud spend wasted, inefficiencies inflate monitoring and security costs, draining budgets that could fund innovation.

Why this matters now

Cloud strategies are under pressure to deliver more – faster, cheaper and greener. Without optimisation, businesses risk inefficiency, higher costs and vulnerabilities that stall progress. In an industry where every second counts, building on shaky ground isn’t just risky, it’s expensive.

How to get started

Before chasing the next big trend in cloud innovation, take time to:

• Audit your current architecture: Maintain visibility by understand what’s running, where and why.
• Identify duplicated workloads and inefficiencies: Determine whether any services or resources are the cause behind draining budgets.
• Align resources with business priorities: Ensure any spending on cloud innovation drives value for the business.
• Implement governance and security best practices: Establishing best practices early on will ensure that innovation is scaled effectively.

This foundation ensures innovation is sustainable, not just a short-term fix.

The CACI approach: Building a cloud that enables innovation

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Don’t leave your cloud strategy to chance. Our specialist cloud architects and optimisation experts have helped leading organisations modernise, streamline and unlock innovation without compromise. Contact us today to start your cloud optimisation journey.

Cloud adoption is no longer seen as a means for storage, but a foundation for intelligent business capabilities. Businesses that have adopted the cloud are able to reap benefits far beyond cost savings, enhancing operational flexibility, enabling faster disaster recovery and much more. In the first blog of our cloud security series, we explore the key advantages of cloud adoption. 

Flexibility

Cloud infrastructure is the key to operational agility, allowing you to scale up or down to suit your bandwidth needs. The pay-as-you-go model offered by most cloud service providers (CSPs) also means that you pay for usage rather than a set monthly fee, making IT spending a more manageable operational expense. The ability to scale resources according to demand also ensures performance will be optimal during peak times and eliminate waste during downtime. 

Reduced cost

Kind to your cash flow, cloud computing cuts out the high hardware cost. The availability of the aforementioned pay-as-you-go models can significantly cut costs. Not to mention the cost-savings of reduced resources, lower energy consumption and fewer delays.  

Disaster recovery

From natural disasters to power outages and software bugs, if your data is backed up in the cloud, it is at a reduced risk of system failure as the servers are typically far from your office locations. You can recover data anywhere to minimise downtime by logging into the internet’s cloud storage portal. 

Accessibility

We’ve all heard that the office is dead. Workers want the ability to work anytime, anywhere. With cloud (and an internet connection), they can. The cloud enables workforces to be distributed through secure access to data and applications from any location, which is critical in today’s hybrid working world. 

Greater collaboration

Cloud infrastructure makes collaboration a simple process, changing the parameters of how and where teams can work. The cloud can drastically improve workplace productivity, from online video calls to sharing files and co-authoring documents in real-time. It offers a centralised, secure and real-time working environment that bolsters communication and helps streamline workflows. These cloud-native applications are designed to make our lives more efficient through greater collaboration.  

Strategic value

Ultimately, businesses that have adopted the cloud typically experience greater cost efficiencies, faster speed to market and enhanced service levels. Adopting the cloud not only reimagines business models and builds resilience but also enables organisations to be agile and innovative. For example, adopting DevOps methodologies can be an essential element for businesses looking to get ahead of their competitors. 

But what about security? Earlier this year, a reported 61% of organisations felt security and compliance were their primary barriers to cloud adoption. Rushed application and the resulting lacklustre security have only intensified security concerns as cybercriminals increasingly target cloud environments. 

Download our comprehensive guide to cloud security and start securing your cloud today.

Benefits of marketing mix modelling (MMM)

For any marketing activities to be successful, understanding consumers’ behaviours and whether a channel is oversaturated is essential. While data and analysis play undeniably important roles in this, marketing mix modelling (MMM) plays an even greater one, representing the merging point of data and analysis with the psychology of consumer understanding.  

Marketing mix modelling (MMM) is a statistical tool that enables an understanding of how each part of an organisation’s marketing activity impacts consumers’ behaviours, sales, return on investment (ROI) and more. Through MMM, an organisation’s performance can be broken down by channel and various types of data can be incorporated to evaluate the effectiveness of marketing activities and determine which are making the most substantial differences to the organisation’s overall performance. 

• Enables organisations to quantify and measure marketing channels effectively to assess which drive the most sales and return on investment 
• Equips organisations with long-term insights that will bolster planning through effective forecasting and marketing campaign generation based on previous performance  
• Helps organisations allocate budgets according to the best performing channels due to measuring growth based on investments
• Instils confidence due to its statistical reliability and being privacy-safe, both of which are particularly important in a post-cookie world
• Offers organisations a holistic view of the impacts that various factors will have on achieving specific KPIs, ensuring marketers can make more informed decisions based on how and when marketing activities will impact KPIs. 

How do marketing mix modelling (MMM) & commercial mix modelling (CMM) work?

Marketing mix modelling (MMM)

Marketing mix modelling (MMM) is used by organisations aiming to understand how marketing activities impact KPIs being measured. Its ability to measure the impact that certain pricing choices, promotional offers, product launches or advertising campaigns may have on sales makes it a game-changer for organisations. 

In MMM, the dependent variable used to assess the relationship between sales and marketing activities is usually:  

• Sales volume: to assess the impact of different marketing activities on sales 
• Revenue: to track the amount of money generated by sales 
• Competitor analysis: to understand how your organisation’s marketing activities are affecting your position in the market. 

In contrast, the independent variables in MMM are the marketing activities or factors that might drive those results, such as: 

• Advertising spend: the amount invested in promotion across various channels. 
• Price: to explore the impact of price adjustments on sales 
• Promotions: discounts, coupons, or offers that could increase sales 
• Distribution: the potential impact of product availability across various locations on sales. 

Commercial mix modelling (CMM)

Commercial mix modelling (CMM) is an analytical approach that examines a variety of commercial factors that drive an organisation’s performance. It begins with collecting data from across the organisation on pricing, promotions, distribution channels, products and more, combining the resulting data into a cohesive dataset.

The insights presented within the dataset help organisations gauge which factors contribute most to performance and where investments result in the highest returns. It also enables organisations to test various scenarios— price changes, promotional adjustments, changes within distribution channels— to assess the potential impact on performance. Through this, organisations can optimise their overall commercial mix to grow and become more profitable.  

How does commercial mix modelling (CMM) differ from marketing mix modelling (MMM)?

While both commercial mix modelling (CMM) and marketing mix modelling (MMM) are granular approaches that help organisations analyse the impact of marketing activities, their scope, methodology and applications differ.  

Scope

CMM offers a broader approach when it comes to evaluating the marketing activities that would impact an organisation’s performance, integrating various functions to optimise revenue and profitability. It encompasses external, non-marketing data sources such as weather, seasonality, competitor pricing, interest rates, etc.  

MMM, on the other hand, is more partial, purely marketing data that offers a more detailed and expansive result. As a statistical analysis method, it quantifies the impact that marketing activities— campaigns, paid advertisements, promotions, etc.— have on specific KPIs. Focusing more on media and investments rather than a wider marketing strategy, its granularity is what marks its stark contrast to CMM.  

Despite the broad scope of CMM, it is just as granular and technical as MMM. 

Methodology

CMM blends analytics, business intelligence and strategic insights, considering both internal and external factors that can affect an organisation’s growth. The approach entails: 

• Scoping & data auditing:
  • Understanding the KPIs and defining whether the model should target revenue, acquisitions, renewals or some combination form the scoping basis. Data auditing includes tech and journey mapping to determine the stages comprising the funnel for lead gen and closing, as well as the tools and tech used at each stage. 
• Data collation & cleaning:
  • This includes a data request to outline the full scope of what can be used in the model, with cleansing, organising and playback taken into consideration to check for completeness and broad accuracy. During this stage, data is also combined and reaggregated for ingestion into the model. 
• Exploratory analysis & feature configuration:
  • Plotting all the raw data to understand distribution and periodicity and exploring this raw data to identify gaps and anomalies is conducted during this stage. Correlation analysis helps find feature relationships and possible collinearity, feature types are configured for use in the model and decay is applied (AdStock) to channel features to simulate the memory effect of advertising.
  • Diminishing returns to channel features simulate channel saturation and other transformations such as smoothing or feature combination.  
• Pre-processing & feature engineering:
  • Calendar and dummy variables can be included to represent milestones and seasonality, with each variable transforming across a range of parameters to find the most realistic behaviour. 
• Commercial mix modelling (an iterative process with pre-processing & feature engineering):
  • Once the model for the approach is scoped (e.g. logistic vs. linear, pooled, nested, hierarchical) and fit for processed features to optimise accuracy and generalising power, it is then checked against existing commercial knowledge and external priors and returned to feature processing to refine variables and tune parameters accordingly.
  • All candidate variables are imported and tested from the pre-processing stage. Finally, the model is refined continually by adjusting variables to optimise statistical measures of accuracy. 
• Optimisation & simulations:
  • The present channel saturation is analysed, the optimal channel mix is delegated for specific budgets and results are presented from scenario simulations to understand which channels have headroom and which are oversaturated.
  • A budget guide is provided for optimising revenue and the ability to plan for different scenarios: mitigating headwinds, capitalising on opportunity and planning contingencies. 
• Next steps & recommendations:
  • Recommendations are given based on budget optimisations and added value. 

MMM, in comparison, focuses on econometric modelling and regression analysis to determine the contributions made by various marketing channels on an organisation’s outcomes. Econometric modelling is a statistical, mathematical approach that quantifies the relationship between marketing activities and business outcomes, built with historical data. Regression analysis is a technique used within econometric modelling to measure the impact of independent variables (marketing activities) on dependent variables (sales or revenue). 

Application

Senior executives and C-suite employees may use CMM for longer-term strategic planning and decision-making, whereas MMM would be used by marketing teams to optimise spending and budget allocation towards campaigns or advertisements.  

The broader scope of CMM enables senior executives and C-suite employees to gain a complete picture of the various commercial drivers and their impact on marketing rather than isolated results. On the other hand, the granularity of MMM ensures marketing teams strategically plan and forecast how changes in spending across channels might impact sales and plan scenarios accordingly. 

How to build a marketing mix model

The first step in building a marketing mix model will be to collate and prepare your data. This will involve collecting historical data on sales and marketing spend across different channels and should go back far enough in time to effectively capture market conditions and seasonality fluctuations. 

Next, selecting the appropriate model to facilitate this will be crucial. Selecting the model can come from its robustness or flexibility, catering to your organisation’s unique needs. 

Building the model will come after this. This will include defining the relationship between marketing spend and sales or other KPIs and considering carryover effects, saturation or external factors. 

Furthermore, fitting the model will use your historical data to estimate the parameters of the MMM. Once the model is fit, the results can be analysed to precisely determine their contributions towards each marketing channel. 

Finally, the insights gleaned from these results can help you adjust marketing strategies accordingly, increase budgets within the highest-performing channels and reduce it in those underperforming. 

Examples of marketing mix modelling (MMM)

Organisations across a variety of industries can apply marketing mix modelling (MMM) to lead to improved outcomes. A few of such examples include:  

• Consumer Packaged Goods (CPG): Gathering data on sales, advertisements, campaigns and pricing can help CPG organisations understand which channels—digital advertising, TV campaigns, etc.— drive the most overall return on investment. 
• Retailers: From seasonal promotions to discounts and the influence of both in-store and online presence, retailers can leverage MMM to understand peak performance periods, digital sales and foot traffic to allocate budgets accordingly or reassess promotional calendars.  
• Financial Services: Financial institutions can use MMM to evaluate their multi-channel advertising efforts and ensure they are reaching the appropriate audiences, encouraging sign-ups.  

Why businesses should choose CACI to carry out CMM 

CACI supports businesses in their delivery of optimised marketing efficiency by: 

• Determining the value and performance of activity through evolved multi-touch and econometric modelling 
• Producing results to sustain and increase growth through targeted investment and improved marketing performance 
• Delivering improved accuracy, consistency and availability of marketing performance insights 
• Enhancing capability by evolving data, technology and process 
• Supporting the provision of ongoing strategic and delivery resource 
• Helping businesses dig into bespoke segments and utilise in-house data products to unlock insights 
• Offer businesses location-based insights into the effects that marketing has at various levels, from stores to regions.  

Find out more about the impact that marketing mix modelling can have on your business by contacting us today. 

Click here to read our short infographic to learn how CACI’s Commercial Mix Modelling can transform your business strategy.

Sources:

• https://www.ruleranalytics.com/blog/analytics/marketing-mix-modelling/  
• https://www.ruleranalytics.com/blog/analytics/marketing-mix-modelling/ 
• https://kleene.ai/marketing-mix-modelling/ 
• https://www.ruleranalytics.com/blog/analytics/marketing-mix-modelling/ 
• https://gaintheory.com/marketing-mix-modeling/ 
• https://medium.com/@nialloulton/building-a-marketing-mix-model-a-guide-for-marketers-37cc5e41e459