Compare building vs. buying AI through a security lens.

Learn how each approach impacts data control, compliance, risk, and long-term protection

Imagine your team is eager to infuse AI into your business – perhaps to automate support or glean insights from data. You have two roads before you: build a custom AI solution from scratch or buy an off-the-shelf AI product. It’s a bit like deciding between constructing a secure house tailored to you, or renting one that’s ready-made. Both paths can get a roof over your head, but which keeps you safer and compliant with the rules?

In this post, we’ll explore the build vs. buy dilemma through a security lens, speaking to business leaders, CTOs, small business owners, and compliance teams alike. We’ll start conversationally and then dig into the technical nitty-gritty of security standards and data protection regulations that must guide this decision. By the end, you’ll see how each approach stacks up on security, from data control and vendor risk to alignment with frameworks like SOC 2, ISO 27001, GDPR, CCPA/CPRA, and NIST CSF, and how you can make a confident choice.

Oh, and one thing is clear: with the average data breach costing about $4.9 million in 2024 - netguru.com, this decision is about more than just tech or cost. Security can make or break your AI initiative, so let’s dive in.

Build or Buy? Why Security Matters More Than Ever

Adopting AI is no longer a bold experiment reserved for tech giants – it’s quickly becoming mainstream across industries. Yet, as excitement grows, so do risks. Whether you develop AI in-house or rely on a third-party vendor, security and compliance must lead the conversation. A wrong choice could expose sensitive data or lead to costly regulatory penalties, especially with global privacy laws tightening.

Choosing to build means you’ll create and host the AI solution within your organization. You’ll wield more control over how it’s built and secured, but you also bear the full responsibility for protecting it. Opting to buy (or outsource) means using an external AI product or platform. This can accelerate deployment and leverage a vendor’s expertise, but it introduces a third-party into your data ecosystem, essentially extending your security perimeter to include a partner.

So how do these options compare when viewed through a security microscope? Let’s break down the key security considerations that should be on your radar in any build vs. buy discussion.

Key Security Considerations in Building vs. Buying AI

When evaluating building vs. buying an AI solution, keep these core security questions in mind. Each factor below highlights what’s at stake and how the two approaches differ:

Control Over Data and Privacy

• Building in-house: You retain full control over data. All sensitive information stays on your systems, reducing exposure to outsiders - ceo-review.com. This makes it easier to enforce strict data governance – you decide who accesses data and how it’s stored or encrypted. In-house development enables “privacy by design,” meaning you can build features to meet laws like GDPR or CCPA from the ground up. For highly sensitive data (think healthcare or finance), this control is golden – it minimizes the risk of unauthorized exposure and can simplify compliance reporting since no external parties are involved - ceo-review.com.

• Buying from a vendor: Your data will likely be shared with or stored by the provider, which means trusting a third party’s security measures. You must vet the vendor’s privacy policies and service terms carefully to ensure they won’t misuse or share your data - optiv.com. Many third-party AI tools include clauses about data sharing or using your data to improve their models – which could be a compliance red flag if not managed. There’s also the matter of data residency: if regulations require data to stay in a certain region, you need to verify the vendor can guarantee that. In short, outsourcing means less direct control – you rely on contractual promises and the vendor’s goodwill to protect your information. Even if their terms look good on paper, breaches can still happen on their side - optiv.com, so you’re one step removed in responding. (Under regulations like GDPR, if a vendor acting as a data processor suffers a breach, they are legally obligated to inform you without undue delay - ico.org.uk, but you’re still dependent on them to notify you promptly and accurately.)

  
  

Vendor Risk and Trust

• Building in-house: If you build your AI solution internally, you avoid many third-party risks by default. There’s no external vendor that could be breached, go out of business, or change their terms on you. You still need to manage risk (open-source components used in your AI could have vulnerabilities, for example), but you’re not exposed to vendor lock-in or a supplier’s weak security practices. For organizations in highly regulated sectors, this is a major plus – in fact, many such companies prefer in-house AI to ensure nothing falls through the cracks with an outside vendor - ceo-review.com. However, remember that building doesn’t eliminate risk; it simply keeps it within your walls. You must still enforce strong internal controls and audits on your AI systems as you would any critical IT system.

• Buying from a vendor: Using a third-party AI solution introduces vendor risk into the equation. You now have to trust that the provider’s security posture is solid – which means doing thorough vendor due diligence. For instance, you should verify that the vendor follows industry security best practices and holds certifications like SOC 2 or ISO 27001 (more on those shortly) - ceo-review.com. You’ll want to review their breach history and incident response plans, and ensure their contract includes provisions for security (e.g. encryption standards, vulnerability management) and audit rights so you can verify compliance. One challenge with AI services is the “black box” problem: the vendor might not reveal much about how their models work or how your data is processed inside the system - ceo-review.com. This lack of transparency can complicate your risk assessments, there could be hidden biases or vulnerabilities you can’t easily evaluate. In short, outsourcing requires a leap of trust: you’re trusting the vendor to uphold your security standards and often to notify you if something goes wrong. Rigorous vetting and ongoing monitoring of the vendor are crucial to manage this risk - ceo-review.com.

  
  

Infrastructure and Architecture Requirements

• Building in-house: Creating your own AI solution means you’re also responsible for the infrastructure it runs on. This could involve provisioning secure cloud environments or on-premise servers, setting up networks, databases, and GPUs for model training – all hardened against attack. The benefit is you can architect the system to your exact specifications and security requirements. Need all data encrypted at rest with your own keys? You can do that. Require isolated networks or zero-trust architecture for the AI environment? It’s in your control. The downside is the cost and complexity: robust infrastructure doesn’t come cheap. You’ll incur upfront costs for hardware or cloud services and spend time configuring security (firewalls, identity management, monitoring tools, etc.). Scaling can also be tricky – supporting a growing AI workload securely requires expertise in cloud security and devops. In fact, building a scalable, secure architecture is often harder than it appears - gooddata.com, and getting it wrong could expose you to breaches or reliability issues. You essentially become your own cloud provider for this solution, so be prepared to invest in resilient architecture and ongoing maintenance.

• Buying from a vendor: When you purchase an AI platform or service, much of the heavy lifting on infrastructure is handled by the provider. Infrastructure security, scalability, and maintenance become the vendor’s responsibility (especially if it’s a SaaS or cloud-hosted product). This can be a huge relief for smaller teams without cloud architects – you don’t need to buy expensive servers or manage 24/7 operations - ceo-review.com. A well-established AI vendor will have a professionally managed environment, possibly with high availability, DDoS protection, and physical data center security baked in. However, there are still a few things to watch: you should confirm the vendor’s infrastructure meets your standards (e.g. what encryption do they use? do they isolate your data from other customers?). Also, consider data residency and compliance – does their infrastructure reside in a region compliant with your regulatory needs (for example, EU data centers for EU user data)? Another factor is integration: connecting the vendor’s solution with your existing systems (APIs, data pipelines) can introduce security challenges if not done carefully. Always follow best practices like using strong API keys or OAuth, principle of least privilege for any accounts, and rigorous testing of the integration to ensure it doesn’t become a new weak link.

  
  

Incident Response and Accountability

• Building in-house: If you build your own AI solution, you own the incident response (IR) plan end-to-end. This means you’ll need internal procedures to detect and respond to security incidents involving the AI system – whether it’s a data breach, a misuse of the AI (like someone abusing an AI feature maliciously), or a failure that exposes data. The good news is that with in-house solutions, you have direct visibility: your security operations team can monitor logs and metrics in real time, set up custom alerts, and immediately jump on any anomalies. You won’t lose precious hours waiting for a third party to inform you of an issue. Your IR plan can be tailored to the system, and you can drill your team on exactly what to do if, say, an API is exploited or unauthorized data access is detected. Just remember that owning IR means investing in it, ensure you have the necessary tools (intrusion detection systems, AI model monitoring) and people assigned to this duty. Also, consider creating a playbook for how to handle AI-specific incidents (for example, if your AI model starts outputting sensitive data it was trained on – a privacy breach known as a data leakage attack).

• Buying from a vendor: When using a third-party AI product, incident response becomes a shared responsibility, but potentially a tricky one. You’ll rely on the vendor to have robust security monitoring and to notify you promptly if they experience a breach or any incident that affects your data. Under laws like GDPR, for instance, a vendor (data processor) must notify you (the data controller) “without undue delay” if they become aware of a personal data breach - ico.org.uk. But you should formalize this expectation: your contract should require timely breach notification and cooperation from the vendor in incident investigations. It’s wise to inquire about the vendor’s IR capabilities upfront: Do they have a 24/7 security team? What is their average response time to incidents? Will they assist you in forensic analysis if needed? Internally, you’ll also need to adapt your own IR plans to include vendor incidents. For example, if the AI provider alerts you to a breach on their side, how will you respond? You might need to notify customers or regulators within tight timelines (GDPR gives 72 hours for notifying authorities in many cases). Essentially, buying means trust but verify – trust the vendor’s processes, but verify that your organization is prepared to react in coordination. Do joint incident response testing with the vendor if possible, or at least ensure you have up-to-date contact information and escalation paths. The last thing you want is to discover during an incident that you don’t know who to call or what steps to take when the issue originates at your vendor.

  
  

Internal Security Expertise and Resources

• Building in-house: One of the most significant requirements for a secure in-house AI build is having the right expertise on your team. You’ll need skilled software engineers, data scientists, and ML engineers to create the AI – and equally skilled security professionals to secure it. This might include specialists in cloud security, data encryption, compliance, and even AI ethics. If your organization already has a mature security team or can hire new experts, building can be feasible. You maintain direct oversight and can embed security throughout the development lifecycle (for example, doing threat modeling for your AI system, code reviews, and rigorous testing). The flip side is the resource cost: not every business has a bench of AI engineers and security architects waiting for a project. Hiring or upskilling staff for this purpose is expensive and time-consuming - trustpath.ai. Additionally, once the system is built, you have ongoing maintenance – applying security patches, updating libraries, adjusting to new threats or regulatory changes. Keeping an AI solution compliant with the latest regulations can itself be a major challenge, as laws and standards evolve - trustpath.ai. In summary, an in-house approach demands a serious, long-term commitment of people and budget. It offers unparalleled control if you have the talent, but if you’re short on expertise, you could be biting off more than you can chew.

• Buying from a vendor: By purchasing an AI solution, you’re essentially outsourcing a portion of the expertise. The vendor’s team (ideally) provides the technical know-how to develop and maintain the AI product, including its security. This can level the playing field for a smaller company that couldn’t afford a full AI research team – you benefit from the vendor’s R&D and updates. Many providers also advertise that they handle security compliance (some boast built-in encryption, regular audits, etc., often guided by standards) so that you “don’t need to hire more cybersecurity specialists” to oversee that tool - gooddata.com. However, it’s dangerous to completely offload security thinking. You will still need some internal expertise: at least folks who understand how to securely integrate and use the AI service, and who can manage the relationship with the vendor. There’s also the risk of becoming too dependent on the vendor’s capabilities. If their security falters, do you have the knowledge to quickly implement mitigations on your side? Additionally, while the vendor may have a bigger team than you, they are catering to many customers – you must ensure your needs (e.g., a required compliance report or a security feature) won’t get de-prioritized. In short, buying can reduce the burden on your internal team, but it doesn’t eliminate the need for oversight. Make sure your security and compliance staff are involved in vendor selection and continue to monitor the vendor’s performance (e.g. reviewing their compliance attestations annually, staying alert to any news of vulnerabilities or breaches affecting the product).

  
  

Now that we’ve covered these key considerations, let’s examine how building vs. buying lines up with major security standards and regulations. Compliance is often the elephant in the room, it can dictate whether you must keep things in-house or can leverage an external service. Below, we’ll discuss each framework or law and the implications for your AI strategy.

Compliance and Regulatory Alignment

Modern businesses operate under a web of security frameworks and data protection laws. Here we focus on some of the most relevant ones – SOC 2, ISO 27001, GDPR, CCPA/CPRA, and the NIST Cybersecurity Framework (CSF) – and compare how in-house vs. third-party AI solutions measure up against each.

SOC 2: Customer Trust and Internal Controls

What it is: SOC 2 is a security compliance standard (developed by the AICPA) that evaluates an organization’s controls in categories like security, availability, confidentiality, processing integrity, and privacy. It’s not a law, but many companies – especially SaaS providers – pursue a SOC 2 Type II report to prove to customers that they have sound security practices in place - tiny.cloud. If you deal with customer data in the cloud, enterprise clients may ask for your SOC 2 compliance as a due diligence step.

• Building in-house: If you develop your AI solution internally and it becomes part of a product or service you offer to customers, you may need to undergo SOC 2 compliance for your own organization. That means implementing stringent internal controls around how data is handled in your AI system – think access controls, monitoring, encryption, vendor management, and so on – and then having an independent auditor assess them. Achieving SOC 2 compliance can be a substantial effort (often taking months of preparation), but it demonstrates trustworthiness. The good news is that because you control the whole stack when building in-house, you can design your system to meet SOC 2 requirements from the start. The challenge is you’ll shoulder the full burden: writing policies, maintaining evidence of control activities, and continuously monitoring compliance. In short, building means if SOC 2 is required, you’re signing up for a significant compliance project. On the flip side, if the AI is purely for internal use and not customer-facing, you might not need a formal SOC 2 report – but aligning with its best practices is still wise for security maturity.

• Buying from a vendor: When buying an AI solution, the vendor’s SOC 2 status becomes critical. If the vendor is SOC 2 certified (has a recent Type II audit report), it’s a strong indicator they follow industry-standard security controlsceo-review.com. You should request their SOC 2 report and review it – check the scope (does it cover the parts of their service you’ll use?), the trust principles included (Security is mandatory, but did they also cover Availability, Confidentiality, etc. that matter to you?), and any noted exceptions (issues) in the report. A SOC 2 report can greatly simplify your vendor risk assessment since an independent auditor has evaluated them. However, don’t get lulled into complacency: you are still responsible for using the product securely. Also note, your company doesn’t automatically become SOC 2 compliant by proxy of using a compliant vendor. If your customers demand your SOC 2, you’ll still need to incorporate the vendor into your own compliance scope (for example, showing you evaluated them, and perhaps that you configure their product securely). Essentially, buying an AI service with SOC 2 compliance can give you a shortcut to trust – and might help you satisfy your own customers’ requirements more quickly – but it doesn’t eliminate accountability. If the vendor’s certification lapses or has gaps, those become your problem to manage - ceo-review.com.

  
  

ISO/IEC 27001: Information Security Management

What it is: ISO 27001 is an internationally recognized standard for Information Security Management Systems (ISMS). An organization that is ISO 27001 certified has demonstrated a systematic approach to managing sensitive information, including risk assessments and a comprehensive set of security controls. Many global businesses value this certification as a hallmark of a mature security program.

• Building in-house: If you build your AI solution internally, you might treat it as part of your organization’s overall IT landscape that could fall under an ISO 27001 program. Achieving ISO 27001 certification for your company means you have to establish an ISMS – effectively, a holistic set of policies and processes covering all aspects of security in scope. This is a broad effort (covering not just IT systems, but also human resources security, physical security, supplier security, etc.). From the AI perspective, building in-house gives you the ability to ensure that your AI systems adhere to your ISMS controls at every step. For example, you can enforce that all model training data is classified and handled per your policies, or that any changes to the AI code go through proper change management and security review – because you control those processes. In highly regulated industries, companies often prefer in-house development specifically to more easily meet strict internal security standards and demonstrate compliance - ceo-review.comceo-review.com. However, pursuing ISO 27001 certification solely for a new AI system would be a heavy lift if you don’t already have an ISMS in place. If you do have one, you’ll want to include your AI project in its risk assessments and scope of controls. Bottom line: building allows alignment with ISO 27001 on your terms, but it requires a significant organizational commitment to maintain certification and continuously improve your security processes.

• Buying from a vendor: If you opt to buy an AI solution, it’s wise to look for vendors that are ISO 27001 certified (or similarly, have a robust security framework). An ISO-certified vendor has been audited for a comprehensive security program, which should cover areas like access control, operations security, encryption, and supplier security – all relevant to how they will handle your data. Using an ISO 27001 certified provider can give you confidence that the vendor follows globally accepted practices for protecting information. In fact, vendor vetting for ISO 27001 compliance is a must when outsourcing critical AI in many cases - ceo-review.com. You should obtain the vendor’s certification scope statement to see what services/locations it covers, and possibly even the Statement of Applicability (which lists the controls they implement). However, much like SOC 2, remember that having a certified vendor doesn’t automatically certify your company. You’ll still need to ensure your own usage of the AI service fits into your security management processes. Additionally, one nuance: ISO 27001 also requires you to manage supplier risk. If you are ISO-certified or aiming to be, you’ll need to show that you’ve assessed the vendor, have them under contract with necessary security clauses, and you monitor their performance - isms.onlinebitsight.com. Fortunately, a certified vendor makes this easier. To summarize, buying from an ISO 27001 certified vendor can greatly reduce the compliance legwork on your side and is a positive sign of security maturity – but it doesn’t mean you can completely ignore the vendor’s operations. Continue to include them in your risk management (e.g., get annual updated certs, discuss any incidents, etc.).

  
  

GDPR: Data Protection by Design and Default (EU)

What it is: The General Data Protection Regulation (GDPR) is the European Union’s stringent data protection law, governing how personal data of EU residents must be handled. GDPR is known for its strict requirements on obtaining consent, honoring data subject rights (access, deletion, correction, etc.), ensuring privacy by design, and for its hefty fines for non-compliance. If you deal with personal data from EU individuals, GDPR compliance is not optional – it’s mandatory and far-reaching.

• Building in-house: If you build an AI solution that processes personal data, you have the advantage of being able to bake in privacy by design principles from the outset. This means you can design your data flows and databases to minimize personal data usage, implement pseudonymization or encryption for data at rest and in transit, and ensure that individuals’ data can be deleted or modified across your AI pipeline when requested. All these measures help fulfill GDPR obligations. Keeping the solution in-house also means you remain the sole data controller for the processing – you’re not sending personal data to an external party, which simplifies the data protection picture. For example, if users invoke their right to erasure, you only need to purge the data from your systems, not chase a vendor to do the same. You also reduce the risk of unlawful international data transfers since you control where the data is stored (e.g., keeping it on EU servers if required). However, building in-house doesn’t magically make GDPR compliance easy. You must still maintain documentation (like a Record of Processing Activities for what your AI is doing with personal data), conduct Data Protection Impact Assessments (DPIAs) for potentially high-risk processing (many AI uses might qualify), and secure the data properly. GDPR’s security principle (Article 32) means you should implement appropriate technical and organizational measures – if a breach happens and you lacked proper encryption or access control, you could be found in violation. One more thing: AI-specific concerns under GDPR include the requirement to avoid automated decisions that have legal or similarly significant effects without human intervention, unless certain conditions are met. If your in-house AI does automated decision-making (say, lending decisions or hiring screenings), you’ll need to provide notice and possibly human review options. In short, building gives you maximum ability to comply on your own terms, but you have to know the law well and engineer your system to meet it.

• Buying from a vendor: Using a third-party AI product that involves personal data means you’ll likely be sharing that data with the vendor, who will act as a data processor under GDPR. This arrangement introduces several compliance obligations: you need a Data Processing Agreement (DPA) in place with the vendor, as required by GDPR Article 28. This contract should stipulate how the vendor handles the data, that they only process it on your instructions, that they use adequate security, help you fulfill data subject requests, and so forth. Essentially, GDPR forces you to ensure the vendor will uphold GDPR standards. You should vet whether the vendor is GDPR-compliant – do they have EU-based data centers or legal data transfer mechanisms (Standard Contractual Clauses, etc.) if data will leave the EU? Also, consider that by involving a vendor, you’ve added complexity to data subject rights fulfillment: if someone asks to delete their data, you’ll need to request the vendor to delete records in their systems as well. Similarly, if there’s a breach at the vendor, they must notify you, and then you as the controller have to handle notifying regulators and possibly users within 72 hours, as required by GDPR. So your incident clock could be ticking based on when the vendor tells you (another reason to demand prompt reporting)ico.org.uk. Another factor is that some vendors might use sub-processors (other third parties) to deliver their service – under GDPR, they need your approval for those sub-processors and must flow down the same data protection obligations to them. On the positive side, many established AI vendors are very aware of GDPR and will have compliance baked in: they might offer data residency choices, built-in tools to help with data exports or deletions, and will often tout certifications or codes of conduct. Using such a vendor can accelerate your GDPR compliance efforts if you choose wisely. Just be cautious of any vendor who asks for broad rights to use your data (e.g., to improve their AI model) – ensure this is compatible with GDPR (usually it isn’t, unless proper consent or anonymization is in place). To sum up, buying means managing GDPR compliance in partnership with the vendor. It can be perfectly safe and compliant, but it requires due diligence (get those legal agreements right!) and ongoing oversight to ensure the vendor continues to meet GDPR obligations on your behalf.

  
  

CCPA/CPRA: Safeguarding Consumer Privacy (California)

What it is: The California Consumer Privacy Act (CCPA) – amended by the California Privacy Rights Act (CPRA) – is California’s consumer privacy law, which grants residents rights over their personal information. It’s often considered the US counterpart to GDPR, though with some differences. CCPA/CPRA give Californians rights to know what data is collected about them, to delete data, to opt out of the sale or sharing of their data, and to sue over certain data breaches. They also impose obligations on businesses around transparency and data minimization.

• Building in-house: If you build your AI solution internally and it handles personal information of consumers (including Californians), you’ll need to ensure your use of data aligns with CCPA/CPRA. The advantage of in-house processing is that you avoid “selling” data to third parties by default – under CCPA, sale is defined broadly as transferring personal info to another business for benefit. If all processing stays in-house, you’re not exposing yourself to that “sale” classification (which triggers the need for an opt-out mechanism). You still have to honor requests: if a California resident asks, “Do not sell my data,” your in-house AI shouldn’t be sharing data externally anyway. You also must be able to delete or correct a person’s data if they request it. Building in-house can make this simpler since you know exactly where the data lives (in your databases, your training sets, etc.) and you can directly erase or update it. Compliance measures like limiting data collection to what is necessary (data minimization) and implementing reasonable security procedures to protect data are fully under your control. CPRA (which took effect in 2023) introduced even tighter requirements, like the concept of “sensitive personal information” and offering an opt-out for its use. If you’re building your solution, you can engineer it to categorize and handle such sensitive data appropriately (for example, maybe your AI doesn’t use certain sensitive attributes at all, to avoid bias and privacy issues). Overall, building gives you the opportunity to deeply ingrain California privacy compliance into your design – but, as with GDPR, you must stay on top of the law’s requirements, update your privacy notices to cover the AI’s data use, and be ready to respond to consumer rights promptly. You’ll also want an internal process for handling any Do Not Sell or Share requests and ensure your AI doesn’t inadvertently count as “sharing” data with an ad network or analytics provider without proper controls.

• Buying from a vendor: If you use a third-party AI service and that service involves personal data about consumers, you need to manage CCPA/CPRA compliance in your vendor relationship. Under these laws, when you disclose personal information to a service provider for a business purpose, it’s not considered a “sale” as long as certain conditions are met. The key is to have a service provider agreement with the vendor that restricts their use of the data strictly to providing the service to you (and not for their own purposes like marketing or building unrelated products). The contract should also require them to assist you in complying with consumer requests (e.g., if you get a deletion request, the service provider must delete the person’s data from their systems too). Essentially, you need to make sure your AI vendor qualifies as a “service provider” or “contractor” under CPRA definitions – this shields you from the data being considered sold or shared. If the vendor were to use your data for other purposes, that could be deemed a sale or share, and you’d have to disclose it and provide opt-outs, which gets messy. So, choose vendors who explicitly state they do not use client data beyond delivering the service (many will even sign additional privacy pledges on that). Additionally, CPRA established the California Privacy Protection Agency, which can audit service providers. This means if your vendor has a major lapse, it could implicate your compliance as well. As a business, you’re expected to do some due diligence on your service providers. One practical step is to evaluate if the vendor is CPRA-ready: Do they allow you to forward deletion requests? Can they flag and not use data that a consumer opted out of “selling”? Are they prepared to be audited for privacy compliance? On the security side, CCPA/CPRA mandate “reasonable security” – if a vendor suffers a breach due to lack of such security, both you and the vendor could face legal consequences (including private lawsuits). Therefore, ensure the vendor follows strong security practices (which ties back to looking at SOC 2/ISO, etc.). In summary, buying can be done in a CCPA-compliant way, but it requires tight contracts and vigilant oversight of the vendor’s data practices. You essentially extend your privacy program to include the vendor. The benefit is many reputable AI vendors are already familiar with these requirements and may even have certifications or audits to prove their compliance, making your job easier. Just remember that regulators will ultimately hold your company responsible for what your vendors do with consumer data, so choose and manage them wisely.

  
  

NIST Cybersecurity Framework (CSF): Integrating AI into a Secure Posture

What it is: The NIST Cybersecurity Framework is a voluntary framework (from the U.S. National Institute of Standards and Technology) consisting of best practices and guidelines for managing cybersecurity risk. It’s organized into core functions – Identify, Protect, Detect, Respond, Recover (and the new version 2.0 adds Govern). Many organizations use NIST CSF as a baseline to build their security programs because it’s comprehensive yet flexible. It’s not a certification, but aligning with NIST CSF is often seen as a mark of due diligence in cybersecurity.

• Building in-house: Embracing NIST CSF for your internally built AI means you’ll be systematically addressing security at each stage of the framework. For instance, in the “Identify” function, you’d classify your AI assets (data, models, servers) and assess risks to them. In “Protect,” you’d implement access controls, encryption, and training for staff on AI security. “Detect” might involve setting up anomaly detection on AI inputs/outputs or monitoring for unusual activity around the AI environment. “Respond” and “Recover” would cover your incident response and backup plans specifically tuned to an AI breach or outage. Essentially, building in-house gives you the freedom to apply NIST CSF controls as deeply as needed. If your organization already aligns with NIST CSF, you should include the new AI system in that governance – e.g., update your asset inventory and risk register to include the AI solution, incorporate AI threats into your risk assessments, and ensure your security operations team is prepared to detect/respond to incidents involving the AI. One thing to note: NIST CSF 2.0 (the latest update) explicitly calls out Cybersecurity Supply Chain Risk Management (C-SCRM) as part of the framework - upguard.com. If you’re building internally, you have fewer supply chain worries (besides maybe third-party libraries or platforms you use), which can simplify this aspect. That said, you should still vet any open-source tools or pretrained models you incorporate as part of “supply chain” risk. Overall, using NIST CSF internally ensures you don’t overlook any major area of security – it can guide your team to fortify the AI system comprehensively. The challenge is purely on you to implement those practices and continuously mature them. Many companies find NIST CSF valuable because it’s like a security health checklist – if you build to it, you likely are covering your bases.

• Buying from a vendor: When relying on third-party AI, you’ll want to make sure that your overall security program (perhaps based on NIST CSF) accounts for this arrangement. Under the Identify function, your vendor should be listed in your asset inventory and risk assessments as an entity that could impact your security. NIST CSF’s new guidance on supply chain risk management is especially relevant here: it recommends things like tiering your vendors by criticality and continuously monitoring their security posture - upguard.comupguard.com. In practice, this means if you buy, you should integrate vendor management into your security framework – perform due diligence (as discussed earlier), get assurances of their controls, and maybe use questionnaires or tools to track their risk over time. For the Protect function, consider how the vendor helps protect your assets: do they provide encryption, MFA for dashboard access, etc.? If not, you might need compensating controls on your side. Under Detect, think about what visibility you have (or lack) into the vendor’s operations. You might rely on their SOC 2 report or periodic pen test summaries as a form of detection of issues, since you can’t see their logs. Respond and Recover functions highlight the need for clear communication with the vendor. You should incorporate their contact info and promised actions into your incident response plans – e.g., “If vendor X’s service is compromised or offline, how do we respond and recover our business functions?” Do they have a DR (Disaster Recovery) plan that you know of? Can they fail over to a backup, and does that meet your recovery time needs? Essentially, aligning a buy decision with NIST CSF means treating the vendor as an extension of your environment. NIST CSF encourages continuous improvement, so you should periodically re-evaluate the vendor’s risk (perhaps annually) – for example, check if there are new vulnerabilities or if the vendor’s compliance posture changed. While NIST CSF isn’t a checkbox compliance like SOC or ISO, it’s a framework for best practices. If you’re using it, a third-party AI should be woven into those best practices. The positive side is that many reputable vendors themselves align to frameworks like NIST; some might even map their product features or internal controls to NIST categories to show they cover key areas. If you find a vendor that openly claims alignment to NIST CSF (or similar frameworks like NIST 800-53 or the AI-specific NIST AI Risk Management Framework), it’s a good sign. Still, your organization is responsible for ensuring that vendor risks are governed. The new CSF 2.0 functions on governance and supply chain will remind you to set policies for third-party use and plan for the end of the vendor relationship (including secure data deletion when you discontinue use) - upguard.comupguard.com. In summary, buying means you must extend your security program’s reach to include the vendor – it’s not “out of sight, out of mind.” Use NIST CSF as a guide to cover all angles with your supplier.

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Having examined these standards and regulations, it’s clear that both building and buying can be done securely if handled correctly. Building gives you autonomy to meet these compliance demands internally, while buying means evaluating and trusting a vendor to meet them for you (with verification on your part). In highly regulated scenarios, companies often lean towards in-house for greater assurance - ceo-review.com, whereas a trustworthy vendor can accelerate compliance in less sensitive scenarios by offering pre-built security features - gooddata.com.

Building, Buying, or Both? Making the Right Choice

After weighing the considerations and compliance factors, you might be wondering, “So, what’s the best approach for my business?” The reality is, there’s no one-size-fits-all answer – the right choice depends on your specific context, risk appetite, and capabilities. In many cases, a hybrid strategy (blending building and buying) can offer the best of both worlds.

Start by assessing your data and risk profile:

• If you’re dealing with extremely sensitive data (trade secrets, personal health information, etc.) or operate in a heavily regulated industry (finance, healthcare, government), the in-house route might be the safest bet for core systems - ceo-review.comceo-review.com. The tighter your regulatory requirements, the more you benefit from direct control. For example, a hospital developing an AI diagnostic tool might choose an internal build to ensure patient data never leaves their secure environment, satisfying HIPAA and GDPR obligations in a straightforward way.

• Conversely, if your AI use-case involves less sensitive data or is more of a commodity function (like a generic chatbot for FAQs), using a well-vetted external solution can save you time and still be secure. You’d focus on choosing a vendor with strong security credentials and putting proper contracts in place, as we discussed.

  
  

Next, consider your internal resources and expertise:

• Do you have (or can you hire) the talent to build and maintain an AI system securely? If not, a reputable vendor might fill that gap. There’s no shame in leveraging outside expertise to avoid security mistakes that in-house generalists might overlook. Just weigh the cost of hiring and development versus vendor subscription costs – and remember to factor in the cost of compliance in both scenarios (one study noted compliance can add significant annual costs – on the order of tens of thousands for changing standards in regulated sectorsnetguru.com).

• Also evaluate time-to-market needs. If you need a solution up and running quickly, buying can often deploy in days or weeksceo-review.com, whereas building might take months. However, rapid deployment should not come at the expense of due diligence – rushing a vendor integration without proper security vetting is a recipe for trouble.

  
  

A hybrid approach is increasingly common and can be very effective - ceo-review.comceo-review.com. This means building the components that are most critical to your business or most sensitive in terms of data, and buying components that are more standard or where an external provider clearly excels. For instance, you might develop your proprietary machine learning model in-house (to keep the intellectual property and data under tight control), but use a third-party cloud AI service for something like speech-to-text or translation, which isn’t core to your business and would be resource-intensive to build from scratch. Many organizations find this mix gives them competitive advantage on the secret sauce they build, while saving effort on the plumbing they can rent. If you go hybrid, just pay attention to the integration points – ensure that the handoff between your in-house system and the external service is secure (using strong APIs, network safeguards, etc.), so you don’t introduce a weak link.

Throughout the decision process, involve your security and compliance teams early. They can help map out the regulatory implications (maybe your compliance officer knows that using a cloud service will trigger a need for a new vendor risk assessment or an update to your PCI scope, for example). By bringing these teams in at the start, you can avoid nasty surprises like discovering late in the game that a chosen vendor isn’t compliant with a law that applies to you.

To recap practical guidance for making the choice:

1. Assess data sensitivity and regulatory requirements: If high, lean towards building or very carefully selected vendors; if low, more freedom to buy.

2. Evaluate internal capabilities: If you have strong tech and security talent (or budget to acquire it), building may be viable. If not, consider vendors but scrutinize their security.

3. Analyze time and cost constraints: Determine if you can afford the time to build and maintain. Don’t forget to include ongoing security compliance costs in that analysis - netguru.com.

4. Research and vet vendors (if considering buy): Look for certifications (SOC 2, ISO 27001), compliance guarantees (GDPR, CCPA-ready), and client testimonials in your industry. Send out security questionnaires or use trial periods to test their product’s security features.

5. Consider a pilot or phased approach: You might pilot an external solution with non-critical data as a test, while developing long-term plans for an in-house system for the crown jewels. Or vice versa – build a minimal viable model in-house and augment it with vendor tools to see results faster.

6. Plan for the long term: Whichever route you go, plan for the full lifecycle. If building, how will you keep the system updated and secure over time? If buying, what’s your exit strategy if the vendor doesn’t perform or a better option appears (avoiding heavy lock-in)?

   
   

By taking a thoughtful, security-focused approach to these questions, you can arrive at a decision that balances innovation with protection. The goal isn’t to fear AI adoption – it’s to do it smartly and safely.

Conclusion: Securing AI Success with askDato.AI

Making the build vs. buy decision for AI is one of the most pivotal choices businesses face in this AI-driven era. Security should be at the heart of that choice. As we’ve seen, building an AI solution in-house grants unparalleled control – you can tailor security to perfection and directly ensure compliance – but it demands resources, expertise, and diligence. Buying an AI solution can accelerate your journey and bring in expert-built security, but it requires trust and vigilant vendor management to protect your data and uphold your obligations. In many cases, a blended approach offers a strategic sweet spot, leveraging the best of both worlds safely.

Whatever path you choose, the ultimate key is informed decision-making. This is where askDato.AI comes in. We position ourselves as your strategic partner in navigating AI implementation securely and confidently. Our team understands the technical complexities and the regulatory nuances – from SOC 2 controls to GDPR clauses – that underpin a successful AI deployment. We can help you assess your unique situation, ask the right questions, and even audit potential solutions from a security perspective.

At askDato.AI, we don’t push one approach over the other; instead, we help you weave the solution that fits – whether that’s guiding your in-house development team to build with robust security architecture, assisting in vetting and integrating a third-party AI platform, or crafting a hybrid approach that aligns with your business goals and risk tolerance. Our expertise in AI and cybersecurity means you gain a trusted advisor who speaks both languages fluently.

Your next step: Don’t leave the security of your AI initiative to chance. Reach out to askDato.AI for a personalized consultation on your AI strategy. We’ll help ensure that whether you build, buy, or combine the two, you’ll implement AI with confidence – securely, compliantly, and successfully. Let’s turn your AI ambitions into reality, safely.