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Secure AI Adoption for Enterprise Data Protection: Are You Prepared?

June 11, 2025
5
Min Read
AI and ML

In today’s fast-moving digital landscape, enterprise AI adoption presents a fascinating paradox for leaders: AI isn’t just a tool for innovation; it’s also a gateway to new security challenges. Organizations are walking a tightrope: Adopt AI to remain competitive, or hold back to protect sensitive data.
With nearly two-thirds of security leaders even considering a ban on AI-generated code due to potential security concerns, it’s clear that this tension is creating real barriers to AI adoption.

A data-first security approach provides solid guarantees for enterprises to innovate with AI safely. Since AI thrives on data - absorbing it, transforming it, and creating new insights - the key is to secure the data at its very source.

Let’s explore how data security for AI can build robust guardrails throughout the AI lifecycle, allowing enterprises to pursue AI innovation confidently.

Data Security Concerns with AI

Every AI system is only as strong as its weakest data link. Modern AI models rely on enormous data sets for both training and inference, expanding the attack surface and creating new vulnerabilities. Without tight data governance, even the most advanced AI models can become entry points for cyber threats.

How Does AI Store And Process Data?

The AI lifecycle includes multiple steps, each introducing unique vulnerabilities. Let’s consider the three main high-level stages in the AI lifecycle:

  • Training: AI models extract and learn patterns from data, sometimes memorizing sensitive information that could later be exposed through various attack vectors.
  • Storage: Security gaps can appear in model weights, vector databases, and document repositories containing valuable enterprise data.
  • Inference: This prediction phase introduces significant leakage risks, particularly with retrieval-augmented generation (RAG) systems that dynamically access external data sources.

Data is everywhere in AI. And if sensitive data is accessible at any point in the AI lifecycle, ensuring complete data protection becomes significantly harder.

AI Adoption Challenges

Reactive measures just won’t cut it in the rapidly evolving world of AI. Proactive security is now a must. Here’s why:

  1. AI systems evolve faster than traditional security models can adapt.

New AI models (like DeepSeek and Qwen) are popping up constantly, each introducing novel attack surfaces and vulnerabilities that can change with every model update..

Legacy security approaches that merely react to known threats simply can't keep pace, as AI demands forward-thinking safeguards.

  1. Reactive approaches usually try to remediate at the last second.

Reactive approaches usually rely on low-latency inline AI output monitoring, which is the last step in a chain of failures that lead to data loss and exfiltration, and the most challenging position to prevent data-related incidents. 

Instead, data security posture management (DSPM) for AI addresses the issue at its source, mitigating and remediating sensitive data exposure and enforcing a least-privilege, multi-layered approach from the outset.

  1. AI adoption is highly interoperable, expanding risk surfaces.

Most enterprises now integrate multiple AI models, frameworks, and environments (on-premise AI platforms, cloud services, external APIs) into their operations. These AI systems dynamically ingest and generate data across organizational boundaries, challenging consistent security enforcement without a unified approach.

Traditional security strategies, which only respond to known threats, can’t keep pace. Instead, a proactive, data-first security strategy is essential. By protecting information before it reaches AI systems, organizations can ensure AI applications process only properly secured data throughout the entire lifecycle and prevent data leaks before they materialize into costly breaches.

Of course, you should not stop there: You should also extend the data-first security layer to support multiple AI-specific controls (e.g., model security, endpoint threat detection, access governance).

What Are the Security Concerns with AI for Enterprises?

Unlike conventional software, AI systems continuously learn, adapt, and generate outputs, which means new security risks emerge at every stage of AI adoption. Without strong security controls, AI can expose sensitive data, be manipulated by attackers, or violate compliance regulations.

For organizations pursuing AI for organization-wide transformation, understanding AI-specific risks is essential:

  • Data loss and exfiltration: AI systems essentially share information contained in their training data and RAG knowledge sources and can act as a “tunnel” through existing data access governance (DAG) controls, with the ability to find and output sensitive data that the user is not authorized to access.
    In addition, Sentra’s rich best-of-breed sensitive data detection and classification empower AI to perform DLP (data loss prevention) measures autonomously by using sensitivity labels.
  • Compliance & privacy risks: AI systems that process regulated information without appropriate controls create substantial regulatory exposure. This is particularly true in heavily regulated sectors like healthcare and financial services, where penalties for AI-related data breaches can reach millions of dollars.
  • Data poisoning: Attackers can subtly manipulate training and RAG data to compromise AI model performance or introduce hidden backdoors, gradually eroding system reliability and integrity.
  • Model theft: Proprietary AI models represent significant intellectual property investments. Inadequate security can leave such valuable assets vulnerable to extraction, potentially erasing years of AI investment advantage.
  • Adversarial attacks: These increasingly prevalent threats involve strategic manipulations of AI model inputs designed to hijack predictions or extract confidential information. Adequate machine learning endpoint security has become non-negotiable.

All these risks stem from a common denominator: a weak data security foundation allowing for unsecured, exposed, or manipulated data.

The solution? A strong data security posture management (DSPM) coupled with comprehensive visibility into the AI assets in the system and the data they can access and expose. This will ensure AI models only train on and access trusted data, interact with authorized users and safe inputs, and prevent unintended exposure.

AI Endpoint Security Risks

Organizations seeking to balance innovation with security must implement strategic approaches that protect data throughout the AI lifecycle without impeding development.

Choosing an AI security solution: ‘DSPM for AI’ vs. AI-SPM

When evaluating security solutions for AI implementation, organizations typically consider two primary approaches:

  • Data security posture management (DSPM) for AI implements data-related AI security features while extending capabilities to encompass broader data governance requirements. ‘DSPM for AI’ focuses on securing data before it enters any AI pipeline and the identities that are exposed to it through Data Access Governance. It also evaluates the security posture of the AI in terms of data (e.g., a CoPilot with access to sensitive data, that has public access enabled).
  • AI security posture management (AI-SPM) focuses on securing the entire AI pipeline, encompassing models and MLOps workflows. AI-SPM features include AI training infrastructure posture (e.g., the configuration of the machine on which training runs) and AI endpoint security.

While both have merits, ‘DSPM for AI’ offers a more focused safety net earlier in the failure chain by protecting the very foundation on which AI operatesーdata. Its key functionalities include data discovery and classification, data access governance, real-time leakage and anomalous “data behavior” detection, and policy enforcement across both AI and non-AI environments.

Best Practices for AI Security Across Environments

AI security frameworks must protect various deployment environments—on-premise, cloud-based, and third-party AI services. Each environment presents unique security challenges that require specialized controls.

On-Premise AI Security

On-premise AI platforms handle proprietary or regulated data, making them attractive for sensitive use cases. However, they require stronger internal security measures to prevent insider threats and unauthorized access to model weights or training data that could expose business-critical information.

Best practices:

  • Encrypt AI data at multiple stages—training data, model weights, and inference data. This prevents exposure even if storage is compromised.
  • Set up role-based access control (RBAC) to ensure only authorized parties can gain access to or modify AI models.
  • Perform AI model integrity checks to detect any unauthorized modifications to training data or model parameters (protecting against data poisoning).

Cloud-Based AI Security

While home-grown cloud AI services offer enhanced abilities to leverage proprietary data, they also expand the threat landscape. Since AI services interact with multiple data sources and often rely on external integrations, they can lead to risks such as unauthorized access, API vulnerabilities, and potential data leakage.  

Best practices:

  • Follow a zero-trust security model that enforces continuous authentication for AI interactions, ensuring only verified entities can query or fine-tune models.
  • Monitor for suspicious activity via audit logs and endpoint threat detection to prevent data exfiltration attempts.
  • Establish robust data access governance (DAG) to track which users, applications, and AI models access what data.

Third-Party AI & API Security

Third-party AI models (like OpenAI's GPT, DeepSeek, or Anthropic's Claude) offer quick wins for various use cases. Unfortunately, they also introduce shadow AI and supply chain risks that must be managed due to a lack of visibility.

Best practices:

  • Restrict sensitive data input to third-party AI models using automated data classification tools.
  • Monitor external AI API interactions to detect if proprietary data is being unintentionally shared.
  • Implement AI-specific DSPM controls to ensure that third-party AI integrations comply with enterprise security policies.

Common AI implementation challenges arise when organizations attempt to maintain consistent security standards across these diverse environments. For enterprises navigating a complex AI adoption, a cloud-native DSPM solution with AI security controls offers a solid AI security strategy.

The Sentra platform is adaptable, consistent across environments, and compliant with frameworks like GDPR, CCPA, and industry-specific regulations.

Use Case: Securing GenAI at Scale with Sentra

Consider a marketing platform using generative AI to create branded content for multiple enterprise clients—a common scenario facing organizations today.

Challenges:

  • AI models processing proprietary brand data require robust enterprise data protection.
  • Prompt injections could potentially leak confidential company messaging.
  • Scalable security that doesn't impede creative workflows is a must. 

Sentra’s data-first security approach tackles these issues head-on via:

  • Data discovery & classification: Specialized AI models identify and safeguard sensitive information.
AI-powered Classification
Figure 1: A view of the specialized AI models that power data classification at Sentra
  • Data access governance (DAG): The platform tracks who accesses training and RAG data, and when, establishing accountability and controlling permissions at a granular level.  In addition, access to the AI agent (and its underlying information) is controlled and minimized.
  • Real-time leakage detection: Sentra’s best-of-breed data labeling engine feeds internal DLP mechanisms that are part of the AI agents (as well as external 3rd-party DLP and DDR tools).  In addition, Sentra monitors the interaction between the users and the AI agent, allowing for the detection of sensitive outputs, malicious inputs, or anomalous behavior.
  • Scalable endpoint threat detection: The solution protects API interactions from adversarial attacks, securing both proprietary and third-party AI services.
  • Automated security alerts: Sentra integrates with ServiceNow and Jira for rapid incident response, streamlining security operations.

The outcome: Sentra provides a scalable DSPM solution for AI that secures enterprise data while enabling AI-powered innovation, helping organizations address the complex challenges of enterprise AI adoption.

Takeaways

AI security starts at the data layer - without securing enterprise data, even the most sophisticated AI implementations remain vulnerable to attacks and data exposure. As organizations develop their data security strategies for AI, prioritizing data observability, governance, and protection creates the foundation for responsible innovation.

Sentra's DSPM provides cutting-edge AI security solutions at the scale required for enterprise adoption, helping organizations implement AI security best practices while maintaining compliance with evolving regulations.

Learn more about how Sentra has built a data security platform designed for the AI era.

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Yogev Wallach is a Physicist and Electrical Engineer with a strong background in ML and AI (in both research and development), and Product Leadership. Yogev is leading the development of Sentra's offerings for securing AI, as well as Sentra's use of AI for security purposes. He is also passionate about art and creativity, as a music producer and visual artist, as well as SCUBA diving and traveling.

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Gilad Golani
Gilad Golani
November 6, 2025
4
Min Read

How SLMs (Small Language Models) Make Sentra’s AI Faster and More Accurate

How SLMs (Small Language Models) Make Sentra’s AI Faster and More Accurate

The LLM Hype, and What’s Missing

Over the past few years, large language models (LLMs) have dominated the AI conversation. From writing essays to generating code, LLMs like GPT-4 and Claude have proven that massive models can produce human-like language and reasoning at scale.

But here's the catch: not every task needs a 70-billion-parameter model. Parameters are computationally expensive - they require both memory and processing time.

At Sentra, we discovered early on that the work our customers rely on for accurate, scalable classification of massive data flows - isn’t about writing essays or generating text. It’s about making decisions fast, reliably, and cost-effectively across dynamic, real-world data environments. While large language models (LLMs) are excellent at solving general problems, it creates a lot of unnecessary computational overhead.

That’s why we’ve shifted our focus toward Small Language Models (SLMs) - compact, specialized models purpose-built for a single task - understanding and classifying data efficiently. By running hundreds of SLMs in parallel on regular CPUs, Sentra can deliver faster insights, stronger data privacy, and a dramatically lower total cost of AI-based classification that scales with their business, not their cloud bill.

What Is an SLM?

An SLM is a smaller, domain-specific version of a language model. Instead of trying to understand and generate any kind of text, an SLM is trained to excel at a particular task, such as identifying the topic of a document (what the document is about or what type of document it is), or detecting sensitive entities within documents, such as passwords, social security numbers, or other forms of PII.

In other words: If an LLM is a generalist, an SLM is a specialist. At Sentra, we use SLMs that are tuned and optimized for security data classification, allowing them to process high volumes of content with remarkable speed, consistency, and precision. These SLMs are based on standard open source models, but trained with data that was curated by Sentra, to achieve the level of accuracy that only Sentra can guarantee.

From LLMs to SLMs: A Strategic Evolution

Like many in the industry, we started by testing LLMs to see how well they could classify and label data. They were powerful, but also slow, expensive, and difficult to scale. Over time, it became clear: LLMs are too big and too expensive to run on customer data for Sentra to be a viable, cost effective solution for data classification.

Each SLM handles a focused part of the process: initial categorization, text extraction from documents and images, and sensitive entity classification. The SLMs are not only accurate (even more accurate than LLMs classifying using prompts) - they can run on standard CPUs efficiently, and they run inside the customer’s environment, as part of Sentra’s scanners.

The Benefits of SLMs for Customers

a. Speed and Efficiency

SLMs process data faster because they’re lean by design. They don’t waste cycles generating full sentences or reasoning across irrelevant contexts. This means real-time or near-real-time classification, even across millions of data points.

b. Accuracy and Adaptability

SLMs are pre-trained “zero-shot” language models that can categorize and classify generically, without the need to pre-train on a specific task in advance. This is the meaning of “zero shot” - it means that regardless of the data it was trained on, the model can classify an arbitrary set of entities and document labels without training on each one specifically. This is possible due to the fact that language models are very advanced, and they are able to capture deep natural language understanding at the training stage.

Regardless of that, Sentra fine tunes these models to further increase the accuracy of the classification, by curating a very large set of tagged data that resembles the type of data that our customers usually run into.

Our feedback loops ensure that model performance only gets better over time - a direct reflection of our customers’ evolving environments.

c. Cost and Sustainability

Because SLMs are compact, they require less compute power, which means lower operational costs and a smaller carbon footprint. This efficiency allows us to deliver powerful AI capabilities to customers without passing on the heavy infrastructure costs of running massive models.

d. Security and Control

Unlike LLMs hosted on external APIs, SLMs can be run within Sentra’s secure environment, preserving data privacy and regulatory compliance. Customers maintain full control over their sensitive information - a critical requirement in enterprise data security.

A Quick Comparison: SLMs vs. LLMs

The difference between SLMs and LLMs becomes clear when you look at their performance across key dimensions:

Factor SLMs LLMs
Speed Fast, optimized for classification throughput Slower and more compute-intensive for large-scale inference
Cost Cost-efficient Expensive to run at scale
Accuracy (for simple tasks) Optimized for classification Comparable but unnecessary overhead
Deployment Lightweight, easy to integrate Complex and resource-heavy
Adaptability (with feedback) Continuously fine-tuned, ability to fine tune per customer Harder to customize, fine-tuning costly
Best Use Case Classification, tagging, filtering Reasoning and analysis, generation, synthesis

Continuous Learning: How Sentra’s SLMs Grow

One of the most powerful aspects of our SLM approach is continuous learning. Each Sentra customer project contributes valuable insights, from new data patterns to evolving classification needs. These learnings feed back into our training workflows, helping us refine and expand our models over time.

While not every model retrains automatically, the system is built to support iterative optimization: as our team analyzes feedback and performance, models can be fine-tuned or extended to handle new categories and contexts.

The result is an adaptive ecosystem of SLMs that becomes more effective as our customer base and data diversity grow, ensuring Sentra’s AI remains aligned with real-world use cases.

Sentra’s Multi-SLM Architecture

Sentra’s scanning technology doesn’t rely on a single model. We run many SLMs in parallel, each specializing in a distinct layer of classification:

  1. Embedding models that convert data into meaningful vector representations
  2. Entity Classification models that label sensitive entities
  3. Document Classification models that label documents by type
  4. Image-to-text and speech-to-text models that are able to process non-textual data into textual data

This layered approach allows us to operate at scale - quickly, cheaply, and with great results. In practice, that means faster insights, fewer errors, and a more responsive platform for every customer.

The Future of AI Is Specialized

We believe the next frontier of AI isn’t about who can build the biggest model, it’s about who can build the most efficient, adaptive, and secure ones.

By embracing SLMs, Sentra is pioneering a future where AI systems are purpose-built, transparent, and sustainable. Our approach aligns with a broader industry shift toward task-optimized intelligence - models that do one thing extremely well and can learn continuously over time.

Conclusion: The Power of Small

At Sentra, we’ve learned that in AI, bigger isn’t always better. Our commitment to SLMs reflects our belief that efficiency, adaptability, and precision matter most for customers. By running thousands of small, smart models rather than a single massive one, we’re able to classify data faster, cheaper, and with greater accuracy - all while ensuring customer privacy and control.

In short: Sentra’s SLMs represent the power of small, and the future of intelligent classification.

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Aarti Gadhia
Aarti Gadhia
October 27, 2025
3
Min Read
Data Security

My Journey to Empower Women in Cybersecurity

My Journey to Empower Women in Cybersecurity

Finding My Voice: From Kenya to the Global Stage

I was born and raised in Kenya, the youngest of three and the only daughter. My parents, who never had the chance to finish their education, sacrificed everything to give me opportunities they never had. Their courage became my foundation.

At sixteen, my mother signed me up to speak at a community event, without telling me first! I stood before 500 people and spoke about something that had long bothered me: there were no women on our community board. That same year, two women were appointed for the first time in our community’s history. This year, I was given the recognition for being a Community Leader at the Global Gujrati Gaurav Awards in BC for my work in educating seniors on cyber safety and helping many immigrants secure jobs.

I didn’t realize it then, but that moment would define my purpose: to speak up for those whose voices aren’t always heard.

From Isolation to Empowerment

When I moved to the UK to study Financial Economics, I faced a different kind of challenge - isolation. My accent made me stand out, and not always in a good way. There were times I felt invisible, even rejected. But I made a promise to myself in those lonely moments that no one else should feel the same way.

Years later, as a founding member of WiCyS Western Affiliate, I helped redesign how networking happens at cybersecurity events. Instead of leaving it to chance, we introduced structured networking that ensured everyone left with at least one new connection. It was a small change, but it made a big difference. Today, that format has been adopted by organizations like ISC2 and ISACA, creating spaces where every person feels they belong. 

Breaking Barriers and Building SHE

When I pivoted into cybersecurity sales after moving to Canada, I encountered another wall. I applied for a senior role and failed a personality test, one that unfairly filtered out many talented women. I refused to accept that. I focused on listening, solving real customer challenges, and eventually became the top seller. That success helped eliminate the test altogether, opening doors for many more women who came after me. That experience planted a seed that would grow into one of my proudest initiatives: SHE (Sharing Her Empowerment).

It started as a simple fireside chat on diversity and inclusion - just 40 seats over lunch. Within minutes of sending the invite, we had 90 people signed up. Executives moved us into a larger room, and that event changed everything. SHE became our first employee resource group focused on empowering women, increasing representation in leadership, and amplifying women’s voices within the organization. Even with just 19% women, we created a ripple effect that reached the boardroom and beyond.

SHE showed me that when women stand together, transformation happens.

Creating Pathways for the Next Generation

Mentorship has always been close to my heart. During the pandemic, I met incredible women, who were trying to break into cybersecurity but kept facing barriers. I challenged hiring norms, advocated for fair opportunities, and helped launch internship programs that gave women hands-on experience. Today, many of them are thriving in their cyber careers, a true reflection of what’s possible when we lift as we climb.

Through Standout to Lead, I partnered with Women Get On Board to help women in cybersecurity gain board seats. Watching more women step into decision-making roles reminds me that leadership isn’t about titles, it’s about creating pathways for others.

Women in Cybersecurity: Our Collective Story

This year, I’m deeply honored to be named among the Top 20 Cybersecurity Women of the World by the United Cybersecurity Alliance. Their mission - to empower women, elevate diverse voices, and drive equity in our field, mirrors everything I believe in.

I’m also thrilled to be part of the upcoming documentary premiere, “The WOMEN IN SECURITY Documentary,” proudly sponsored by Sentra, Amazon WWOS, and Pinkerton among others. This film shines a light on the fearless women redefining what leadership looks like in our industry.

As a member of Sentra’s community, I see the same commitment to visibility, inclusion, and impact that has guided my journey. Together, we’re not just securing data, we’re securing the future of those who will lead next.

Asante Sana – Thank You

My story, my safari, is still being written. I’ve learned that impact doesn’t come from perfection, but from purpose. Whether it’s advocating for fairness, mentoring the next generation, or sharing our stories, every step we take matters.

To every woman, every underrepresented voice in STEM, and everyone who’s ever felt unseen - stay authentic, speak up, and don’t be afraid of the outcome. You might just change the world.

Join me and the Sentra team at The WOMEN IN SECURITY Documentary Premiere, a celebration of leadership, resilience, and the voices shaping the future of our industry.

Save your seat at The Women in Security premiere here (spots are limited).

Follow Sentra on LinkedIn and YouTube for more updates on the event and stories that inspire change.

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Ward Balcerzak
Ward Balcerzak
October 20, 2025
3
Min Read
Data Security

2026 Cybersecurity Budget Planning: Make Data Visibility a Priority

2026 Cybersecurity Budget Planning: Make Data Visibility a Priority

Why Data Visibility Belongs in Your 2026 Cybersecurity Budget

As the fiscal year winds down and security leaders tackle cybersecurity budget planning for 2026, you need to decide how to use every remaining 2025 dollar wisely and how to plan smarter for next year. The question isn’t just what to cut or keep, it’s what creates measurable impact. Across programs, data visibility and DSPM deliver provable risk reduction, faster audits, and clearer ROI,making them priority line items whether you’re spending down this year or shaping next year’s plan. Some teams discover unspent funds after project delays, postponed renewals, or slower-than-expected hiring. Others are already deep in planning mode, mapping next year’s security priorities across people, tools, and processes. Either way, one question looms large: where can a limited security budget make the biggest impact - right now and next year?

Across the industry, one theme is clear: data visibility is no longer a “nice-to-have” line item, it’s a foundational control. Whether you’re allocating leftover funds before year-end or shaping your 2026 strategy, investing in Data Security Posture Management (DSPM) should be part of the plan.

As Bitsight notes, many organizations look for smart ways to use remaining funds that don’t roll over. The goal isn’t simply to spend, it’s to invest in initiatives that improve posture and provide measurable, lasting value. And according to Applied Tech, “using remaining IT funds strategically can strengthen your position for the next budget cycle.”

That same principle applies in cybersecurity. Whether you’re closing out this year or planning for 2026, the focus should be on spending that improves security maturity and tells a story leadership understands. Few areas achieve that more effectively than data-centric visibility.

(For additional background, see Sentra’s article on why DSPM should take a slice of your cybersecurity budget.)

Where to Allocate Remaining Year-End Funds (Without Hurting Next Year’s Budget)

It’s important to utilize all of your 2025 budget allocations because finance departments frequently view underspending as a sign of overfunding, leading to smaller allocations next year. Instead, strategic security teams look for ways to convert every remaining dollar into evidence of progress.

That means focusing on investments that:

  • Produce measurable results you can show to leadership.
  • Strengthen core program foundations: people, visibility, and process.
  • Avoid new recurring costs that stretch future budgets.

Top Investments That Pay Off

1. Invest in Your People

One of the strongest points echoed by security professionals across industry communities: the best investment is almost always your people. Security programs are built on human capability. Certifications, practical training, and professional growth not only expand your team’s skills but also build morale and retention, two things that can’t be bought with tooling alone.

High-impact options include:

  • Hands-on training platforms like Hack The Box, INE Skill Dive, or Security Blue Team, which develop real-world skills through simulated environments.
  • Professional certifications (SANS GIAC, OSCP, or cloud security credentials) that validate expertise and strengthen your team’s credibility.
  • Conference attendance for exposure to new threat perspectives and networking with peers.
  • Cross-functional training between SOC, GRC, and AppSec to create operational cohesion.

In practitioner discussions, one common sentiment stood out: training isn’t just an expense, it’s proof of leadership maturity.

As one manager put it, “If you want your analysts to go the extra mile during an incident, show you’ll go the extra mile for them when things are calm.”

2. Invest in Data Visibility (DSPM)

While team capability drives execution, data visibility drives confidence. In recent conversations among mid-market and enterprise security teams, Data Security Posture Management (DSPM) repeatedly surfaced as one of the most valuable investments made in the past year, especially for hybrid-cloud environments.

One security leader described it this way:

“After implementing DSPM, we finally had a clear picture of where sensitive data actually lived. It saved our team hours of manual chasing and made the audit season much easier.”

That feedback reflects a growing consensus: without visibility into where sensitive data resides, who can access it, and how it’s secured, every other layer of defense operates partly in the dark.

*Tip: If your remaining 2025 budget won’t suffice for a full DSPM deployment, you can scope an initial implementation with the remaining budget, then expand to full coverage in 2026.

DSPM solutions provide that clarity by helping teams:

  • Map and classify sensitive data across multi-cloud and SaaS environments.
  • Identify access misconfigurations or risky sharing patterns.
  • Detect policy violations or overexposure before they become incidents.

Beyond security operations, DSPM delivers something finance and leadership appreciate, measurable proof. Dashboards and reports make risk tangible, allowing CISOs to demonstrate progress in data protection and compliance.

The takeaway: DSPM isn’t just a good way to use remaining funds, it’s a baseline investment every forward-looking security program should plan for in 2026 and beyond.

3. Invest in Testing

Training builds capability. Visibility builds understanding. Testing builds credibility.

External red team, purple team, or security posture assessments continue to be among the most effective ways to validate your defenses and generate actionable findings.

Security practitioners often point out that testing engagements create outcomes leadership understands:

“Training is great, but it’s hard to quantify. An external assessment gives you findings, metrics, and a roadmap you can point to when defending next year’s budget.”

Well-scoped assessments do more than uncover vulnerabilities—they benchmark performance, expose process gaps, and generate data-backed justification for continued investment.

4. Preserve Flexibility with a Retainer

If your team can’t launch a new project before year-end, a retainer with a trusted partner is an efficient way to preserve funds without waste. Retainers can cover services like penetration testing, incident response, or advisory hours, providing flexibility when unpredictable needs arise. This approach, often recommended by veteran CISOs, allows teams to close their books responsibly while keeping agility for the next fiscal year.

5. Strengthen Your Foundations

Not every valuable investment requires new tools. Several practitioners emphasized the long-term returns from process improvements and collaboration-focused initiatives:

  • Threat modeling workshops that align development and security priorities.
  • Framework assessments (like NIST CSF or ISO 27001) that provide measurable baselines.
  • Automation pilots to eliminate repetitive manual work.
  • Internal tabletop exercises that enhance cross-team coordination.

These lower-cost efforts improve resilience and efficiency, two metrics that always matter in budget conversations.

How to Decide: A Simple, Measurable Framework

When evaluating where to allocate remaining or future funds, apply a simple framework:

  1. Identify what’s lagging. Which pillar - people, visibility, or process most limits your current effectiveness?
  2. Choose something measurable. Prioritize initiatives that produce clear, demonstrable outputs: reports, dashboards, certifications.
  3. Aim for dual impact. Every investment should strengthen both your operations and your ability to justify next year’s funding.

Final Thoughts

A strong security budget isn’t just about defense, it’s about direction. Every spend tells a story about how your organization prioritizes resilience, efficiency, and visibility.

Whether you’re closing out this year’s funds or preparing your 2026 plan, focus on investments that create both operational value and executive clarity. Because while technologies evolve and threats shift, understanding where your data is, who can access it, and how it’s protected remains the cornerstone of a mature security program.

Or, as one practitioner summed it up: “Spend on the things that make next year’s budget conversation easier.”

DSPM fits that description perfectly.

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