From Conceptual to Operational for Enterprise Cybersecurity
Ten Steps to Secure AI with DASF
Imagine this: a large financial services company unveils what is touted as a “game-changer” for customer service: Betty, an AI-powered virtual agent that can handle most inbound customer inquiries without human intervention. The AI was trained on a blend of publicly available FAQ material and a large corpus of internal knowledge base articles, policy documents, procedural notes, and anonymized examples of customer interactions. At first, Betty had nothing but success. Customers received instant answers, call center queues shrank, and call center costs were way down. Betty became the subject of upbeat boardroom presentations and glowing internal newsletters.
Then, barely two months after launch, a security researcher demonstrated something that took the leadership team from elation to dread. By asking a carefully crafted series of questions, the researcher was able to get Betty to reveal confidential loan underwriting criteria, a competitive crown jewel that had always been closely guarded. These criteria had been embedded deep in the retrieval-augmented generation (RAG) pipeline’s indexed documents. With the right prompting, Betty helpfully responded to the researcher’s queries, presenting the highly confidential company IP (intellectual property) in a neatly formatted answer.
The company’s firewalls were untouched. No one had broken into its servers. There was no malware. The attack surface wasn’t in the traditional network perimeter. The attack surface was the AI itself.
This is the reality confronting every organization adopting AI today. Artificial intelligence doesn’t just process information, it ingests vast amounts of it, learns from it, and produces outputs in ways that can be manipulated in ways traditional software never could be. Attackers no longer need to breach a database to steal sensitive information; they can trick the AI into handing it over. They can poison the data that trains the model, causing subtle and damaging shifts in its behavior. They can craft inputs that appear harmless but cause catastrophic output errors.
Traditional security frameworks, robust as they are for infrastructure, applications, and data stores, were not designed to address these classes of threats. That gap is precisely why the Databricks AI Security Framework (DASF) exists. The DASF breaks down AI systems into twelve key components, identifies sixty-two distinct risks and associated controls across the AI lifecycle, and prescribes targeted, actionable controls for each one.
This guide outlines ten steps to secure AI and takes the DASF out of the realm of theory and into the trenches of real-world security operations and shows how a comprehensive platform like Noma Security can be used to easily implement the recommended control across all AI attack surfaces, from AI models to AI agents. It is written for security engineers to help them understand AI security risks and how to implement technical controls and processes to methodically and sustainably address them.
The Four Core Principles for Secure AI and Bringing DASF to Life
Before jumping into the how-to of the ten steps to secure AI, it’s important to understand the principles that underpin every successful DASF operation. Without these principles embedded into the culture and workflow, any framework can become a dusty shelf document.
1. Perspective on the Full AI Attack Surface
Artificial intelligence is not static. A traditional application is deterministic, the outcome is predictable. Once deployed, the app only changes when developers release an update. AI is “non-deterministic”, the same input can trigger very different output. AI apps learn from new data, respond differently over time due to drift, and in some cases, change behavior entirely when fine-tuned or retrained. This dynamic nature means that a model that is secure and aligned to purpose today may be misaligned tomorrow.
Consider a healthcare AI agent, James, that begins its life answering patient inquiries with information drawn from publicly available medical literature. The security team performs an initial review, ensures no personally identifiable information is in the training data, and approves it for launch. Six months later, James is quietly fine-tuned on internal care guidelines, some of which reference patients undergoing rare treatments. These references are anonymized but specific enough that, with the right prompts, an attacker could infer details about patient cases. No one re-evaluated privacy concerns with James before redeployment and that’s when the risk crept in.
Consideration of the full AI attack surface means treating AI like a living organism in your security ecosystem. You assess it not once, but continuously, throughout the entire arc of its existence: at design, at training, at deployment, during operation, after updates, and at retirement.
2. Shared Responsibility
In traditional IT security, there is often a central infosec team that can, in theory, enforce security across the stack. In AI, no single group has all the expertise required to keep a system secure. Data scientists understand model training and evaluation but may not know the intricacies of API authentication. MLOps engineers are masters of deployment automation but may not be trained in threat modeling. Security engineers are adept at network defense but may have never crafted a prompt injection attack. Governance teams understand compliance obligations but may not recognize the technical implications of a particular model architecture.
When these roles work in silos, AI security falls through the cracks. Imagine a scenario where an AI model starts returning biased outputs. The data science team sees it as a dataset problem. The MLOps team suspects a drift issue. The security team wonders if it’s an injection attack. Without shared responsibility and a comprehensive view across the entire lifecycle, everyone assumes someone else will fix it. And no one does.
Operationalizing DASF with Noma Security and ten steps to secure AI provides a single-pane of glass security platform for bringing these stakeholders together, giving them a common language for risks and controls, and ensuring that AI security is co-owned from data to agent.
3. Prioritization
The DASF catalog is comprehensive. It enumerates 62 unique risks covering every phase of the AI lifecycle starting with DataOps. But trying to tackle them all at once is not just unrealistic, it is counterproductive. The goal is not to eliminate all risk in one grand project. The goal is to reduce the most dangerous risks quickly and then iterate.
Consider a manufacturing firm that was tasked with hardening their AI. The MLOps teams spent months implementing sophisticated adversarial robustness measures for their computer vision system but because they were not working with SecOps they missed that the inference API was running without HTTPS. Meaning a basic man-in-the-middle attack could intercept and alter every request and response. The lesson: combine cross-functional shared responsibility principles to prioritize and address high-impact, high-likelihood risks. A full spectrum platform like Noma Security enables teams to work together on prioritization so they can implement the most effective controls first.
4. Standards Alignment
AI security standards are still emerging, but security governance is not. There is immense value in mapping DASF controls to established standards like the NIST AI Risk Management Framework, MITRE ATLAS, OWASP Top 10 for LLMs, ISO 27001, and relevant data privacy laws. To support DASF mapping and compliance, the Noma Security Platform includes mapping to the standards mentioned above and also to the EU AI Act and ISO 42001. The power of compliance mapping makes AI security measures more comprehensible to stakeholders already familiar with those standards and strengthens your position in audits and compliance reviews. An auditor might not yet have a checklist for AI model theft, but they understand ISO clauses on asset protection. When a team can show how DASF controls map to key compliance frameworks using the Noma Security Platform, you’ve made their job, and yours, easier.
From Framework to Action: Ten Steps to Secure AI
The heart of operationalizing DASF with Noma Security is in taking the framework from paper to production. What follows walks you through the ten steps to secure AI with each step using examples of successful implementation patterns and cautionary tales or anti-patterns to help you steer clear of implementation pitfalls.
Step 1: Define Your AI Deployment Model
Every security plan begins with scope. In AI, the shape of your deployment needs to take into account a number of factors: what models will you run, how and who will train the models, where will the models be hosted, and how will they be accessed, traditionally or as part of a modern agentic system? The answers to these questions determine which risks are relevant to the threat model and which surfaces and components require protection.
An example anti-pattern involves a retail company that was launching an AI agent, Augustine, to help customers put together fun, fresh ‘fits using the retailer’s new fashion line. Before launch a security engineer was tasked with making Augustine “safe.” Without a clear end-to-end view of the agentic architecture, the engineer assumed the agent was fetching answers from a general-purpose LLM that contained no sensitive back-end data. The engineer focused security efforts on API protections and did not worry about the LLM layer. Weeks later, a customer posted a TikTok about the retailer’s as yet announced upcoming collaboration with a well known designer. Upon investigation, it turned out that Augustine did have access to confidential data via a RAG integration to the LLM.
Defining the deployment model is about creating an inventory and classification of your AI estate. Using the Noma Security Platform, companies can maintain a living catalog of every AI model in use, with versioning information, connected APIs, and data access pathways. And directly mitigate risks such as insufficient access controls (DASF 1.1), missing data classification (1.2), lack of data versioning (1.5), and insufficient data lineage (1.6). It also reduces systemic threats like lack of traceability and transparency of model assets (4.1), lack of end-to-end ML lifecycle management (4.2), and lack of MLOps repeatable enforced standards (11.1).
Step 2: Map the Twelve DASF Components to Your Environment
The DASF identifies the 12 canonical components of an end-to-end AI system, starting with Raw Data and continuing through Operations and Platform. At a high level, the list is straightforward but when implementation teams actually sit down to map their systems, it can be a revelation. Why? Because mapping often exposes risks like missing data classification (1.2), insufficient data lineage (1.6), lack of data access logs (1.10), lack of traceability and transparency of model assets (4.1), and lack of end-to-end ML lifecycle management (4.2). It also frequently uncovers operational blind spots such as model lifecycle without human-in-the-loop oversight (8.3).
Take the example of a fictional biotech company that completed an AI mapping exercise. They discovered that although they had strict governance over their official training datasets, half the data used to fine-tune certain models came from ad hoc uploads to a shared storage bucket. There were no access controls, no audit trail, no validation, in other words, several of the above DASF risks in action. Until they drew the map, this had gone entirely unnoticed.
Mapping is not a “check the box for compliance” diagram that sits in a drawer. It’s a living, collaborative process involving engineers, data scientists, and security. The Noma Security Platform makes this work significantly easier by automatically discovering and inventorying all components in the system, and presenting relevant details such as model provenance, model safety, datasets in use, and supply chain dependencies. When done properly, with a tool like Noma Security, mapping transforms these risks from hidden vulnerabilities into manageable, monitored components.
Step 3: Identify and Score Applicable Risks
The DASF risk catalog is comprehensive. It covers threats from legality of data (1.8) and stale data (1.9) to data poisoning (3.1), model drift (5.2), ML supply chain vulnerabilities (7.3), and lack of compliance (12.6), but no single AI deployment will face them all. The key is to narrow the field so you can focus on the risks that actually matter to your architecture.
This is where the Noma Security dashboard can help. Instead of wading through dozens of risk descriptions, teams see a dynamic, real-time dashboard that lists identified issues and mapped DASF risks, filtered by severity, environment, and affected components. The dashboard serves as an interactive command center. With one glance, you can see that a training pipeline is exposed to data poisoning (3.1), a model endpoint is at risk of model drift (5.2), and a storage bucket may violate compliance requirements (12.6). You can drill down into each flagged risk to see exactly which system component it’s tied to and recommended remediation or controls.
Consider an example organization that dismissed model theft (8.2) as a risk because their models were hosted externally, only to discover that intermediate checkpoints were being stored in a publicly accessible cloud bucket. The Noma dashboard would have surfaced that risk immediately, potentially preventing an expensive oversight.
The dashboard’s built-in risk scoring engine helps teams evaluate impact, from financial loss and reputational harm to compliance violations, alongside likelihood scores based on architecture and current threat intelligence. These scores automatically update as your environment changes, ensuring that a new integration, dataset, or model update doesn’t introduce hidden high-priority risks.
Step 4: Select Mitigation Controls
Identifying risks is like spotting leaks on a ship, but spotting them is only half the battle. The DASF provides recommended controls for each risk, helping you “plug the holes,” but not every control fits every architecture. Selecting the right controls means aligning them to your real-world environment, resources, and priorities.
The Noma Security dashboard helps make this selection process easier by mapping risks directly to recommended controls, ranked by severity and potential impact. It’s not just a static list. The Noma Security platform shows where each control will have the highest risk-reduction value in your system and flags redundancies so teams don’t waste time securing what’s already protected.
Consider a fictional healthcare startup that learned this lesson the hard way. Their security team, acting on an early threat assessment, prioritized adding an additional encryption layer to their model storage to prevent model theft (8.2). But the models were already hosted in a fully managed, encrypted vendor registry so the risk was already mitigated. Meanwhile, their ingestion pipeline accepted unvalidated CSV files from outside research partners, which left them open to data poisoning (3.1) and could corrupt model behavior and compromise patient safety.
The Noma Security Dashboard helps companies identify and apply best fit controls based on issue severity. In the example above, model theft (8.2) was low-likelihood given the current architecture, while data poisoning (3.1) was a high-severity, unmitigated risk. Instead of overinvesting in redundant encryption measures, they could have focused first on implementing more impactful for their actual threat profile.
Step 5: Integrate Controls into AI Workflows
Controls only protect if they are applied where the work actually happens. If a critical process, like dataset validation, exists in a separate, manual workflow (e.g., a spreadsheet), it’s only a matter of time before it gets bypassed in the name of speed. To be effective, controls must live inside the same CI/CD pipelines, model serving environments, and orchestration layers that your teams already use. Once all controls are in monitored workflows, tools like the Noma Security Dashboard can continuously report on the validity, health, and enforcement of those controls.
Take the example of a fictional fintech. They had a clear policy: all new models entering the AI workflow must complete a security review. But the review process was manual and existed outside the deployment pipeline. Under pressure to fix a misaligned model suffering from model drift (5.2) right before a major holiday weekend, a developer fine-tuned the model and deployed it directly to production, bypassing the review entirely. The result: the new model began exposing customer financial data in responses (sensitive data output from a model- 10.6, accidental exposure of unauthorized data to models- 9.10). Had the review been embedded as a pre-deployment gate, the deployment would have been blocked until security checks passed.
The Noma Security Dashboard makes this type of integration simple and enforceable. Every model in the pipeline is scored automatically. Models that fail are flagged for immediate review or removal. Other embedded controls monitored by Noma Security include: checks for excessive agent agency (9.13), preventing autonomous agents from taking unapproved actions and identification of insecure MCP (model context protocol) servers, mitigating risks tied to unauthorized privileged access (12.4) or initial access (12.7).
Embedding controls directly into workflows and monitoring them continuously helps make security seamless and when it’s harder to bypass security than to follow it, controls become self-enforcing.
Step 6: Establish Continuous Monitoring
Security isn’t a one-time event. Controls and processes exist in environments that shift daily. In AI and agentic workloads, new data arrives, models drift (5.2), MCP tool integrations are added (introducing potential unauthorized privileged access – 12.4 or initial access- 12.7), and attacker techniques evolve- from prompt injection (9.1) to model inversion (9.2). Continuous monitoring is what separates a security posture that merely looks solid in a quarterly report from one that actually holds up during a real incident.
But monitoring can be tedious and time-consuming, which is why many teams tail off. For example, a company deploying an AI-powered analytics tool began with diligent logging and review of every model inference API request (9.11), tuning alerts and investigating anomalies for the first three months. Over time, when no significant incidents were detected, the team scaled back. Alerts still fired, but no one looked closely unless they spiked dramatically. That’s when a low-and-slow attacker could step in, using carefully crafted prompt injections (9.1) to extract embeddings from the company’s vector store (model assets leak- 7.2). Spread over weeks, each query looked harmless in isolation but damaging in aggregate. Before anyone noticed, a significant portion of the embedding index had been leaked.
Continuous monitoring must go beyond capturing raw logs. It means:
- Setting realistic thresholds to detect stealthy attacks without drowning in noise
- Rotating human reviewers to avoid alert fatigue
- Auditing alerting logic to ensure rules are still catching emerging threats
- Using a unified view like the Noma Security Dashboard to tie together logs, anomalies, and control status in one place so issues are flagged before damage occurs.
In AI and agentic workflows, it also means monitoring the model itself. Behavioral drift (model drift- 5.2) can indicate output manipulation (10.2) or sensitive data leakage (10.6). If an LLM that once answered in formal policy language suddenly adopts slang, or a classifier starts misclassifying benign items as malicious, that’s a signal. Drift detection tools can flag these shifts, but someone must decide whether it’s harmless evolution or the result of data poisoning (3.1) or backdoor ML/Trojaned models (7.1).
Not all risks are accidental. If a malicious model trained on sensitive data enters your environment, will you know? Noma Security continuously monitors for malicious model artifacts (7.1), unauthorized model context protocol servers (12.4), and pipeline changes that could introduce data poisoning (3.1) or bypass CI/CD controls.
Most critically, monitoring must extend to the entire pipeline. If a new, unverified data source starts feeding your training process, you need to know before it poisons your model. If a model artifact changes in your registry outside the approved CI/CD process, that’s a signal worth investigating. With Noma Security, these changes are detected and flagged in real time, so your team can act before attackers exploit them.
Step 7: Test and Validate AI Security
Controls must be tested and validated regularly to confirm they are working as intended. In AI, this means going beyond one-time pre-deployment checks and repeatedly stress-testing guardrails both before launch and during runtime. Because AI is non-deterministic, even well-tested guardrails can break under new prompt conditions. That’s why runtime monitoring is essential to ensure that those controls continue to hold.
In this context, testing cannot be limited to the functional “does it work” checks used to test traditional software. AI must be tested for all kinds of failure both intentional and unintentional. A fictional financial services firm believed it had solved prompt injection (9.1) with a sophisticated output sanitization layer. Functional testing suggested success. But during a red team exercise, multi-turn conversation chains were used to embed harmful instructions that only triggered after a specific conversational context was built. The LLM followed these instructions, bypassing the sanitization entirely and resulting in sensitive data output from a model (10.6). Noma Security provides extensive red-team testing before launch and then leverages the red-team findings to ensure that the run-time monitoring is tuned to catch and alert on suspicious prompts and responses that could indicate guardrail failure.
Additionally, testing AI security means evaluating the entire surrounding system, not just the model. A perfectly hardened model offers little protection if the model inference API (9.11) accepts unauthenticated requests, if monitoring logs can be tampered with to hide output manipulation (10.2) or model inversion attempts (9.2), or if a Trojaned model (7.1) slips into production unnoticed. Testing must also consider risks such as model theft (8.2), data poisoning (3.1), evaluation data poisoning (6.1), and ML supply chain vulnerabilities (7.3), as well as operational weaknesses like unauthorized privileged access (12.4) or unmonitored denial of service (9.7) conditions.
With the Noma Security platform, it’s easy to test and re-test as needed. Tests can run automatically on a schedule, and tracked over time, ensuring that both the models and their supporting infrastructure remain resilient.
Step 8: Map to Compliance Frameworks
Today’s security programs operate within complex organizational and regulatory ecosystems. While the DASF is a fantastic tool, simply stating “we use the DASF” may not satisfy auditors, customers, or partners. In most cases, organizations will also need to demonstrate how your implemented controls align to common assessment standards.
A fictional European medtech company learned this lesson when an EU AI Act assessor asked how its AI agent, Marjorie, protected sensitive health information. The security team had encryption, access controls, and logging in place to ensure that Marjorie didn’t give our patient data and to mitigate DASF risks such as insufficient access controls (1.1), ineffective storage and encryption (1.4), and lack of data access logs (1.10).
Using the Noma Security Dashboard, the team quickly generated a control mapping that showed how each DASF-aligned safeguard, from preventing accidental exposure of unauthorized data to models (9.10), to restricting unauthorized privileged access (12.4), to guarding against sensitive data output from a model (10.6), mapped directly to the EU AI Act’s data governance requirements. Once the mapping was in place, the audit process became far smoother, as both parties were speaking the same compliance language.
Mapping controls in this way means taking every implemented measure and linking it to the corresponding clause, section, or principle in the frameworks relevant to your organization. This could be sector-specific standards like PCI-DSS or HITRUST; broad security frameworks such as ISO 27001; or AI-focused regulations like the NIST AI Risk Management Framework, ISO 42001, and the EU AI Act. With Noma, control-to-framework mapping is built in, making it easy to maintain a living record of compliance alignment that evolves alongside both your security posture and the regulatory landscape.
Step 9: Train Teams
Even the most well-engineered security controls can be undone in seconds by a well-meaning employee who clicks the wrong link, uploads the wrong dataset, or bypasses a safeguard to “get the job done.” To truly operationalize DASF, every person interacting with AI systems must understand how to use them securely.
AI security is still a new and unfamiliar domain for many. Even seasoned ML professionals like from prompt engineers and data scientists may underestimate risks like prompt injection (9.1), data poisoning (3.1, 6.1), model drift (5.2), excessive agency (9.13), or accidental exposure of unauthorized data to models (9.10). Without targeted training, these risks can be amplified when well-intentioned employees bypass controls.
The democratization of AI through low-code/no-code agent creation and “vibe coding” has put powerful capabilities into the hands of many non-technical users. This is exciting but it also expands the attack surface. A highly motivated business development intern could, without realizing the risk, connect the company’s customer database to a public LLM, introducing vulnerabilities tied to unauthorized privileged access (12.4), sensitive data output from a model (10.6), and model inference API misuse (9.11).
Role-specific training is critical. Security engineers need deep knowledge of AI-specific threats and their mitigations. Data scientists must be fluent in secure dataset handling, safe model development practices, and detecting drift or poisoning attempts. Business users need awareness of the dangers of sharing sensitive data with public models or integrating proprietary repositories into agentic AI workflows. Governance teams must understand AI architectures well enough to interpret regulatory requirements against the applicable DASF risk set.
Tabletop exercises can help bring this training to life. Simulating an AI-specific incident such as a model that starts leaking sensitive data, or an indirect prompt injection attack on an AI agent, allows the team to walk through their response in a safe but realistic environment. Training content should be reviewed and updated at least annually, and more often in fast-moving organizations that rely heavily on AI innovation.
Step 10: Review and Update Regularly
The final step is the one that keeps your AI security program in a constantly improving optimization loop. Models change, deployment architectures evolve, datasets shift, MCP servers are swapped out, new agentic tools appear and criminal tactics adapt just as quickly. Without regular review and update, your DASF implementation will inevitably drift out of alignment with your true risk profile.
This is not the same as continuous monitoring. Monitoring is operational and near real-time; review and update operates at a strategic altitude. A quarterly cadence works well for many organizations. In these reviews, you update the risk register, re-score threats such as model drift (5.2), unauthorized privileged access (12.4), prompt injection (9.1), model theft (8.2), and evaluation data poisoning (6.1), assess incidents and near-misses, and adjust controls to keep pace. It’s also the time to retire measures that no longer work and replace them with more effective controls, preventing stagnation.
Reviews should also be triggered by significant events such as changing a foundation model, deploying a new ingestion pipeline, or conducting an AAR (after-action report) after an incident. These inflection points often bring new risks, such as accidental exposure of unauthorized data to models (9.10), sensitive data output from a model (10.6), or trojaned models (7.1) introduced through the supply chain. Your security posture must adapt in step with each of these changes.
The Noma Security Dashboard makes this discipline achievable by continuously mapping your environment, surfacing drift in both models and controls, and providing prioritized remediation recommendations. This ensures that reviews aren’t an abstract conversation, they’re backed by current and actionable intelligence. Organizations that turn review and update into a habitual, high-value process will stay ahead of attackers.
Additional Lessons Learned
Operationalizing a framework DASF can be tricky without the right planning and tools. Throughout this paper, we’ve shared many of the anti-patterns we’ve seen companies struggle with that have slowed or negatively impacted their AI rollouts, putting systems and customer data at risk. In this section we’ll provide a few more so you can focus on positive and successful implementation patterns and not fall into the anti-pattern pit.
Working in silos is perhaps the most damaging pitfall. AI security is inherently cross-disciplinary. When data scientists, engineers, and security teams operate in isolation, they each optimize for their own priorities, leaving gaps in coverage. A model might be perfectly trained for fairness and accuracy but deployed on an insecure endpoint, or locked down in deployment but poisoned in training.
Another recurring issue is ignoring operational drift. Controls that are perfectly tuned at launch can degrade over time as data changes, models evolve, and integrations shift. Without continuous monitoring and regular reviews, these controls can become ineffective long before anyone realizes it.
Finally, there is the risk of “paper compliance”, implementing controls that satisfy an auditor’s checklist but do little to address real threats. Passing an audit is not the same as being secure. The ultimate measure of success is resilience in the face of an actual attack.
Noma Security Can Help Shift DASF from Conceptual to Operational
The Databricks AI Security Framework (DASF) is a structured way to understand, map, and mitigate the unique risks of AI systems. Translating the DASF into day-to-day security practice requires tools, processes, and training.
This is where Noma Security can help. The Noma Security Platform supports operationalization of security across the entire AI lifecycle, from raw data to deployed agents:
- Define & Map – Noma Security automatically discovers AI models, datasets, APIs, and integrations, surfacing shadow systems, agents and tool integrations, ungoverned data flows, and undocumented endpoints.
- Identify & Prioritize Risks – Risks automatically discovered and are scored by likelihood and impact, and organized by environment and severity so teams know exactly where to focus first.
- Select & Integrate Controls – For each identified risk, Noma shows where they apply in your actual workflows and integrates them into CI/CD, model serving environments, and orchestration layers.
- Monitor Continuously – Noma Security provides real-time visibility into model drift, malicious artifacts, prompt injection attempts, insecure MCP integrations, and data poisoning risks, ensuring controls remain effective as your environment changes.
- Test & Validate – Built-in support for adversarial testing, red teaming, and run-time monitoring ensures your guardrails hold up against evolving attack techniques.
- Align with Standards – Noma Security maps implemented controls to industry and regulatory frameworks such as NIST AI RMF, MITRE ATLAS, OWASP LLM Top 10, ISO 42001, and the EU AI Act, streamlining compliance and audits.
DASF mapped to the Noma Security platform turns what would otherwise be a static framework into an operating security practice. It ensures AI systems are treated not as opaque black boxes, but as fully discovered, transparent, and accountable business assets that are continuously inventoried, risk-assessed, protected, and monitored throughout their lifecycle.
The organizations that thrive in the AI-driven future will be those that embed this kind of adaptive, full-spectrum security into everything they build. By bringing DASF to life with Noma Security risk management companies can confidently deploy AI everywhere.
To see how we do it, request a demo here.
