Avoid Issues in Operations: Be More Secure by Design

Tom Burton


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Would you feel comfortable flying in an aeroplane designed by engineers who only considered what might go wrong after they had built it?



‘Secure by Design’ (SbD) is not a technology, it is a set of principles to be adopted to improve business risk and resilience. It has strong similarity to conventional engineering practices, and it will save money by reducing wasteful rework. 


The critical first step is to understand the risks that the solution will be exposed to. Like Failure Mode Analysis in conventional engineering, these inherent risks form an essential part of the solution requirements. The design can then be a collaborative and iterative exercise of review and enhancement to meet the security requirements. 


Effort spent defining requirements before design and implementation is widely recognised to save time and money. The situation is no different with security requirements, but there are wider benefits as well, compared to addressing security late in the lifecycle:


  • Security controls applied after design and implementation are more likely to restrict functionality, undermining overall user satisfaction and the return on investment


  • Early engagement reduces the risk of budgets overruns, or having to accept inadequate security if you can’t secure the budget


  • A well-documented set of risks, security controls and design decisions can then follow the solution through implementation and into operations, enabling future change to understand past rationale

  • Above all else, late identification of risk and security requirements causes wasteful rework of the solution, which will cost time and money


The key to success is defining the system scope correctly. If the scope is too great and encompasses a number of separate systems, then the benefits are eroded and the exercise becomes more akin to a homogenous enterprise risk assessment. If the scope is too small, the number of systems becomes unwieldy and unsustainable to assess and manage.


It is not a Technology, and it is not New


Despite what you might believe from some of the cyber tech product sheets, SbD is not a technology (for that matter, Zero Trust, which we see as a valuable component of SbD practice, is not a technology either). It is a philosophy or strategy, a set of principles that bring efficiency, consistency, and discipline to cyber risk management. You may find tools that help you to adopt these principles, and the practice requires a sound understanding of technology, but above all SbD is a human endeavour.


Like many other buzzwords in the security community, SbD is frequently presented as something rather mystical, requiring specialist knowledge and attracting a new set of standards and vocabulary. We don’t hold with this concept; in our view, it ‘does exactly what it says on the tin’. It is about ensuring the system’s very design enforces security and mitigates risk rather than relying on sticking plasters applied after implementation. Whether those design features are preventative controls, controls to detect and respond to issues, or any other category, they will have been defined and tuned to the specific risks and characteristics of the solution in advance (and managed through life).


The concept is not new. The benefits of early security engagement have been known for some time. But sadly, this has been frequently ignored. As the cyber security industry matures, and the frequency and impact of cyber attacks on businesses increases, the call for this discipline has been increasing. Governments are starting to mandate it in the standards and security governance of technology programmes. 


The Similarities between Digital and Conventional Engineering


Most engineering lifecycles, not just those related to digital solutions, recognise the importance of spending adequate time defining the requirements. At the start of the programme, the level of uncertainty will be at its greatest. The purpose of Requirements Engineering is to reduce that uncertainty so that design and implementation can proceed with direction and to minimise the number of ‘wrong turns’ that have to be unwound. If you do not reduce uncertainty as early as possible, the problems grow as they move downstream, and solving them then becomes a disheartening exercise in ‘pushing water uphill’.


Let us imagine that we want someone to build us a house. We would go to our local house building company and commission the job; if they get started immediately, the chances of the end result being anything like what we originally wanted would be almost zero. Where do we want our home located? How many bedrooms, bathrooms, and living rooms? What architectural style? What about the fixtures and fittings? We will identify everything wrong once the sub-optimal, ill-thought-out building is completed for our inspection. Putting those right at this stage will cost orders of magnitude more than they would have with an effective design phase. Worse, there will be many issues that we cannot put right without starting again, and, therefore, we will be left operating in a flawed and compromised solution. 


Where do we Start?


So, how do we identify the security requirements for the design? What is Requirements Engineering in a security context? The security requirements are defined by the risks that the solution will be exposed to. One of the most important SbD principles emphases this by stating that you must ‘adopt a risk-driven approach’. These risks and your organisation’s appetite to accept risk determine the requirements for controls; or, to put it another way, the controls are required to mitigate the risk to a level that it is within your organisation’s appetite. Again, there are similarities with conventional engineering. Understanding the risks that the design must treat is similar to identifying the Failure Modes of an aircraft or other system.


The risks need to be articulated so that all stakeholders can understand them, including by the non-technical and non-security communities. Getting all stakeholders to sign off on these inherent risks is crucial to ensure that everyone recognises the constraints the solution will be confined by. If you do not have a sound understanding of the risks before work starts on the design, let alone the implementation, then you are lacking an essential part of the solution requirements.


Review, Collaborate, and Iterate


Once you have the security requirements, you can feed them into the design process similar to functional requirements. Selecting appropriate controls to meet the requirements will undoubtedly require some specialist expertise. However, this is similar to the requirement for technical architects to be familiar with the technologies employed in the solution stack.


This design process should be iterative. Requirements change, frequently due to learning in one iteration providing feedback into the next. The security requirements may influence the architectural approach to fulfil the functional requirements. Occasionally, a complete rethink may be required to adjust the functional requirements to meet the security constraints while also meeting the business needs.


However, like the house-building analogy above, this time spent optimising the design will be significantly less than the time, cost, and disruption caused if security is addressed later in the lifecycle.


Each iteration takes the proposed design, reviews the inherent risks to identify any that can be retired or if new ones have been created, assesses the residual risk given the existing security controls, and identifies additional security controls to reduce the residual risk to an acceptable level. Done collaboratively, this can introduce fast feedback into the design process, and, over time, the technical architects will become more familiar with security issues and their resolutions.


Zero Trust’s Role in the Exercise, and Scope Definition


Zero Trust is another trending buzzword frequently camouflaged with mystique, or hijacked as a ‘feature’ on product sheets. My view on Zero Trust is similar to my view on SbD: it should be easy to understand, and ‘does exactly what it says on the tin’. In design and in operations, we start from the baseline that nothing is trusted. Whether it is digital identities, devices, applications, or services, we can only trust them once we have an objective and explicit reason to trust them.


We use the principle of Zero Trust extensively when applying SbD. By having no implicit trust in any identity, device, or service, we can decide on the minimum level of trust we need to enforce and the maximum level of trust that the entity can offer. If the maximum trust on offer is less than the minimum trust we need, then there is a design decision to be made about how we close the gap. It may be necessary to reduce functionality in order to reduce the required minimum. Or, we may need to put in place other compensatory controls to reduce the risk in other ways. 


Defining an appropriate scope of the system is key to success. If you set the scope too large, then everything is inside the ‘circle of trust’, and SbD becomes a homogenous exercise in enterprise security. If you set the scope too small then you will drown under the sheer quantity of projects to manage.


The World is not a Greenfield Site, and Security is not a Fire-and-Forget Weapon


The world is not a greenfield site, and there will be challenges retrofitting a SbD approach to the broad portfolio of legacy solutions. There is no simple or quick solution to this, it will be a case of progressively revisiting each project’s architecture and identifying the changes that will make it secure by design.


But, risk can help us here too. Some projects or services will be sufficiently low-risk so that they can be tolerated until they are retired (so long as they are not trusted by any other more important system).


The SbD approach lends itself well to a progressive rollout. SbD will limit the negative impact that a legacy system can have on a target system, because nothing outside of a project’s scope is implicitly trusted. You can only aim for a perfect world by progressively taking steps to make it a better world.


In this article, we explain why risk management needs to be addressed at the design phase of projects. This does not mean that we believe this is the end of the journey. Security and risk management still needs to be managed in operations as new threats change the risk profile, or change is applied to a system. But with the foundations laid early in the lifecycle, the task of management through life becomes easier. The documentation generated by SbD should provide clear traceability between risks and controls. When a project is reviewed in life, the rationale behind previous decisions can be clearly understood, enabling change to be an informed process.


Summary


This article outlines why I believe applying the principles of Secure by Design avoids issues getting into operations, and saves time and money. If what I have described already seems obvious, then that is positive. However, from my experience, too many projects do not consider security to be an essential component of design. I believe that this is a missed opportunity, and, when applied correctly, it delivers solutions that are more secure and easier to manage.


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by Darren Sheppard 4 December 2025
What is the Contract Lifecycle Management and Why does it Matter? The future success of your business depends on realising the value that’s captured in its contracts. From vendor agreements to employee documents, everywhere you look are commitments that need to be met for your business to succeed. The type of contract and the nature of goods or services it covers will determine what sort of management activities might be needed at each stage. How your company is organised will also determine which departments or individuals are responsible for what activities at each stage. Contract Lifecycle Management, from a buyer's perspective, is the process of defining and designing the actual activities needed in each stage for any specific contract, allocating ownership of the activities to individuals or groups, and monitoring the performance of those activities as the contract progresses through its lifecycle. The ultimate aim is to minimise surprises, ensure the contracted goods or services are delivered by the vendor in accordance with the contract, and realise the expected business benefits and value for money. The Problem of Redundant Spend in Contracts Despite the built-in imbalance of information favoring suppliers, companies still choose to oversee these vendors internally. However, many adopt a reactive, unstructured approach to supplier management and struggle to bridge the gap between contractual expectations and actual performance. Currently, where governance exists, it is often understaffed, with weak, missing, or poorly enforced processes. The focus is primarily on manual data collection, validation, and basic retrospective reporting of supplier performance, rather than on proactively managing risk, relationships, and overall performance. The amount of redundant spend in contracts can vary widely depending on the industry, the complexity of the contracts, and how rigorously they are managed. For further information on this, Cambridge MC’s case studies provide insights into typical ranges and common sources of redundant spend. As a general estimate, industry analysts often state that redundant spend can account for as much as 20% of total contract value. In some cases, especially in poorly managed contracts, this can be much higher. What is AI-driven Contract Management? Artificial Intelligence (AI) is redefining contract management, transforming a historically time-consuming and manual process into a streamlined, efficient, and intelligent operation. Traditionally, managing contracts required legal teams to navigate through extensive paperwork, drafting, reviewing, and monitoring agreements — a process prone to inefficiencies and human error. With the emergence of artificial intelligence, particularly generative AI and natural language processing (NLP), this area of operations is undergoing a paradigm shift. This step change is not without concerns however, as there are the inevitable risks of AI hallucinations, training data biases and the threat to jobs. AI-driven contract management solutions not only automate repetitive tasks but also uncover valuable insights locked up in contract data, improving compliance and reducing the risks that are often lost in reams paperwork and contract clauses. Put simply, AI can automate, analyse, and optimise every aspect of your contract lifecycle. From drafting and negotiation to approval, storage, and tracking, AI-powered platforms enhance precision and speed across these processes; in some cases reducing work that might take several days to minutes or hours. By discerning patterns and identifying key terms, conditions, and concepts within agreements, AI enables businesses to parse complex contracts with ease and efficiency. In theory, this empowers your legal and contract teams (rather than reducing them), allowing personnel to focus on high-level tasks such as strategy rather than minutiae. However, it is important to recognise that none of the solutions available in the marketplace today offer companies an integrated supplier management solution, combining a comprehensive software platform, capable of advanced analytics, with a managed service. Cambridge Management Consulting is one of only a few consultancies that offers fully integrated Contract Management as a Service (CMaaS). Benefits of Integrating AI into your Contract Lifecycle Management Cambridge MC’s Contract Management as a Service (CMaaS) 360-degree Visibility: Enable your business to gain 360-degree visibility into contracts and streamline the change management process. Real-time Data: Gain real-time performance data and granularly compare it against contractually obligated outcomes. More Control: Take control of your contracts and associated relationships with an integrated, centralised platform. Advanced meta data searches provide specific information on external risk elements, and qualitative and quantitative insights into performance. Reduces Costs: By automating manual processes, businesses can significantly reduce administrative costs associated with contract management. AI-based solutions eliminate inefficiencies in the contract lifecycle while minimising reliance on external legal counsel for routine tasks. Supplier Collaboration: Proactively drive supplier collaboration and take a data-driven approach towards managing relationships and governance process health. Enhanced Compliance: AI tools ensure that contracts adhere to internal policies and external regulations by flagging non-compliant clauses during the drafting or review stage. This proactive approach reduces the risk of costly disputes or penalties. Reduces Human Errors: In traditional contract management processes, human errors can lead to missed deadlines and hidden risks. AI-powered systems use natural language processing to identify inconsistencies or inaccuracies in contracts before they escalate into larger issues. Automates Repetitive Tasks: AI-powered tools automate time-consuming tasks such as drafting contracts, reviewing documents for errors, and extracting key terms. This frees up legal teams to focus on higher-value activities like strategic negotiations and risk assessment. We can accurately model and connect commercial information across end-to-end processes and execution systems. AI capabilities then derive and apply automated commercial intelligence (from thousands of commercial experts using those systems) to error-proof complex tasks such as searching for hidden contract risks, determining SLA calculations and performing invoice matching/approvals directly against best-in-class criteria. Contract management teams using AI tools reported an annual savings rate that is 37% higher than peers. Spending and tracking rebates, delivery terms and volume discounts can ensure that all of the savings negotiated in a sourcing cycle are based on our experience of managing complex contracts for a wide variety of customers. Our Contract Management as a Service, underpinned by AI software tooling, has already delivered tangible benefits and proven success. 8 Steps to Transition Your Organisation to AI Contract Management Implementing AI-driven contract management requires a thoughtful and structured approach to ensure seamless integration and long-term success. By following these key steps your organisation can avoid delays and costly setbacks. Step 1 Digitise Contracts and Centralise in the Cloud: Begin by converting all existing contracts into a digital format and storing them in a secure, centralised, cloud-based repository. This ensures contracts are accessible, organised, and easier to manage. A cloud-based system also facilitates real-time collaboration and allows AI to extract data from various file formats, such as PDFs and OCR-scanned images, with ease. Search for and retrieve contracts using a variety of advanced search features such as full text search, Boolean, regex, fuzzy, and more. Monitor upcoming renewal and expiration events with configurable alerts, notifications, and calendar entries. Streamline contract change management with robust version control and automatically refresh updated metadata and affected obligations. Step 2 Choose the Right AI-Powered Contract Management Software: Selecting the right software is a critical step in setting up your management system. Evaluate platforms based on their ability to meet your organisation’s unique contracting needs. Consider key factors such as data privacy and security, integration with existing systems, ease of implementation, and the accuracy of AI-generated outputs. A well-chosen platform will streamline workflows while ensuring compliance and scalability. Step 3 Understand How AI Analyses Contracts: To make the most of AI, it’s essential to understand how it processes contract data. AI systems use Natural Language Processing (NLP) to interpret and extract meaning from human-readable contract terms, while Machine Learning (ML) enables the system to continuously improve its accuracy through experience. These combined technologies allow AI to identify key clauses, conditions, and obligations, as well as extract critical data like dates, parties, and legal provisions. Training your team on these capabilities will help them to understand the system and diagnose inconsistencies. Step 4 Maintain Oversight and Validate AI Outputs: While AI can automate repetitive tasks and significantly reduce manual effort, human oversight is indispensable. Implement a thorough process for spot-checking AI-generated outputs to ensure accuracy, compliance, and alignment with organisational standards. Legal teams should review contracts processed by AI to verify the integrity of agreements and minimise risks. This collaborative approach between AI and human contract management expertise ensures confidence in the system. Step 5 Refine the Data Pool for Better Results: The quality of AI’s analysis depends heavily on the data it is trained on. Regularly refine and update your data pool by incorporating industry-relevant contract examples and removing errors or inconsistencies. A well-maintained data set enhances the precision of AI outputs, enabling the system to adapt to evolving business needs and legal standards. Step 6 Establish Frameworks for Ongoing AI Management: To ensure long-term success, set clear objectives and measurable goals for your AI contract management system. Define key performance indicators (KPIs) to track progress and prioritise features that align with your organisation’s specific requirements. Establish workflows and governance frameworks to guide the use of AI tools, ensuring consistency and accountability in contract management processes. Step 7 Train and Empower Your Teams: Equip your teams with the skills and knowledge they need to use AI tools effectively. Conduct hands-on training sessions to familiarise users with the platform’s features and functionalities. Create a feedback loop to gather insights from your team, allowing for continuous improvement of the system. Avoid change resistance by using change management methodologies, as this will foster trust in the technology and drive successful adoption. Step 8 Ensure Ethical and Secure Use of AI: Tools Promote transparency and integrity in the use of AI-driven contract management. Legal teams should have the ability to filter sensitive information, secure data within private cloud environments, and trace data back to its source when needed. By prioritising data security and ethical AI practices, organisations can build trust and mitigate potential risks. With the right tools, training, and oversight, AI can become a powerful ally in achieving operational excellence as well as reducing costs and risk. Overcoming the Technical & Human Challenges While the benefits are compelling, implementing AI in contract management comes with some unique challenges which need to be managed by your leadership and contract teams: Data Security Concerns: Uploading sensitive contracts to cloud-based platforms risks data breaches and phishing attacks. Integration Complexities: Incorporating AI tools into existing systems requires careful planning to avoid disruptions and downtime. Change Fatigue & Resistance: Training employees to use new technologies can be time-intensive and costly. There is a natural resistance to change, the dynamics of which are often overlooked and ignored, even though these risks are often a major cause of project failure. Reliance on Generic Models: Off-the-shelf AI models may not fully align with your needs without detailed customisation. To address these challenges, businesses should partner with experienced providers who specialise in delivering tailored AI-driven solutions for contract lifecycle management. Case Study 1: The CRM That Nobody Used A mid-sized company invests £50,000 in a cutting-edge Customer Relationship Management (CRM) system, hoping to streamline customer interactions, automate follow-ups, and boost sales performance. The leadership expects this software to increase efficiency and revenue. However, after six months: Sales teams continue using spreadsheets because they find the CRM complicated. Managers struggle to generate reports because the system wasn’t set up properly. Customer data is inconsistent, leading to missed opportunities. The Result: The software becomes an expensive shelf-ware — a wasted investment that adds no value because the employees never fully adopted it. Case Study 2: Using Contract Management Experts to Set Up, Customise and Provide Training If the previous company had invested in professional services alongside the software, the outcome would have been very different. A team of CMaaS experts would: Train employees to ensure adoption and confidence in using the system. Customise the software to fit business needs, eliminating frustrations. Provide ongoing support, so issues don’t lead to abandonment. Generate workflows and governance for upward communication and visibility of adherence. The Result: A fully customised CRM that significantly improves the Contract Management lifecycle, leading to: more efficient workflows, more time for the contract team to spend on higher value work, automated tasks and event notifications, and real-time analytics. With full utilisation and efficiency, the software delivers real ROI, making it a strategic investment instead of a sunk cost. Summary AI is reshaping the way organisations approach contract lifecycle management by automating processes, enhancing compliance, reducing risks, and improving visibility into contractual obligations. From data extraction to risk analysis, AI-powered tools are empowering legal teams with actionable insights while driving operational efficiency. However, successful implementation requires overcoming challenges such as data security concerns and integration complexities. By choosing the right solutions, tailored to their needs — and partnering with experts like Cambridge Management Consulting — businesses can overcome the challenges and unlock the full potential of AI-based contract management. A Summary of Key Benefits Manage the entire lifecycle of supplier management on a single integrated platform Stop value leakage: as much as 20% of Annual Contract Value (ACV) Reduce on-going governance and application support and maintenance expenses by up to 60% Deliver a higher level of service to your end-user community. Speed without compromise: accomplish more in less time with automation capabilities Smarter contracts allow you to leverage analytics while you negotiate Manage and reduce risk at every step of the contract lifecycle Up to 90% reduction in creating first drafts Reduction in CLM costs and extraction costs How we Can Help Cambridge Management Consulting stands at the forefront of delivering innovative AI-powered solutions for contract lifecycle management. With specialised teams in both AI and Contract Management, we are well-placed to design and manage your transition with minimal disruption to operations. We have already worked with many public and private organisations, during due diligence, deal negotiation, TSAs, and exit phases; rescuing millions in contract management issues. Use the contact form below to send your queries to Darren Sheppard , Senior Partner for Contract Management. Go to our Contract Management Service Page
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