While the world rapidly adapts to the demands of AI development in 2026, one of the main concerns – following its potential impact on jobs – is how the technology handles sensitive information and the risks of exposure.
Thomson Reuters’ recent Future of Professionals report found that nearly 42% of respondents are concerned about insufficient data security, which hampers safe investment due to the risk of client data being shared, stolen, or irreversibly damaged.
Meanwhile, Bloomberg stresses AI systems are vulnerable to misuse, including malicious prompts or AI-generated code designed to bypass guardrails and access sensitive information.
The deployment gap is equally telling. According to Jarek Kutylowski, CEO and founder of language AI platform DeepL, “AI is everywhere, but efficiency is not. Most companies have deployed AI in some form, yet few achieve real productivity at scale because core workflows remain designed around people, not systems.”
In response, the AI software market is evolving: new companies are developing systems that not only protect data but also analyze and understand its content contextually, providing sustainable and proactive security.
Companies turning to an AI context-aware approach
McKinsey’s 2025 State of AI report showed that nearly two-thirds of companies are already experimenting with AI agents; 23% of organizations are scaling agentic AI systems, and 39% are actively implementing them into full operations.
One of them, Concentric AI was founded in 2018 in San Jose, California by Chief Data Scientist Dr. Madhu Shashanka, and offers solutions designed to ensure that company data is properly managed and protected.
The business case for this shift is becoming increasingly clear. “The organizations seeing the greatest return from AI are not the ones moving fastest – they’re the ones that have built contextual intelligence into their security architecture from the ground up,” said Martin Lewit, SVP Corporate Development at global tech consulting firm Nisum.
“When AI understands not just what data exists, but why it matters and who should access it, governance stops being a cost center and starts becoming a competitive advantage.”
Unlike traditional data security tools that rely on static data-at-rest scanning and rule-based approaches, Concentric AI offers context-aware classification of sensitive data, continuous monitoring of access and exposure and comprehensive risk assessment across environments.
Similarly, platforms like Britive are applying context-aware principles to cloud access management, enabling just-in-time elevation of privileges based on a user role, and real-time conditions, reducing the risk of exposing sensitive data.
Beyond proactive security, many organisations rely on data protection and recovery solutions. For instance, Stellar Data Recovery provides tools for retrieving lost and corrupted information. This ensures that, should sensitive data be compromised, companies can restore this quickly and effectively,.
Such an approach from AI agent companies is becoming increasingly handy across the wider technology sector, including software engineering, IT, product development and human resource. Those organizations that decide to adapt to context awareness and invest heavily have proven to see the most benefit, as opposed to their previous reliance on autopilots without personalization – which carry a high risk of DLP (Data Loss Prevention).
Lane Sullivan, Chief Information Security and Strategy Officer at Concentric AI, emphasized the contextual intelligence practical benefits while in conversation with AI Journal during the February CIO UK & I Event in London.
“Traditional DLP focuses on preventing data loss in motion, whereas a context-aware approach starts with understanding what data exists, how to protect it, who has access, and applying controls before data moves,” he noted.
According to Sullivan, this method enables business stakeholders to implement robust data governance without requiring deep technical expertise.
How contextual AI improves data classification and control
In the context of modern data complexity, diversity, and the rapid pace of change, controlling how data moves across AI systems remains a major challenge.
Context-aware AI was originally designed to protect companies from data leaks while also helping manage chaotic or mismanaged data locations. Its basic tools, including natural language processors and large language models, can quickly and accurately identify and classify data based on content and context, enabling scalable and effective data governance and security.
According to the Fortinet 2025 Data Security Report, 77% of organizations experienced insider-related data loss within just 18 months; most of these leaks originated from ordinary user behavior rather than the anticipated hacking attacks, highlighting the limitations of static rule-based tools in distinguishing normal from risky activity.
This shift is already delivering measurable results in adjacent security fields. For one, Baran Ozkan, co-founder and CEO of AI-native compliance platform Flagright, noted that context-aware intelligence has enabled their systems to reduce false positives by 93% and decrease alert investigation time by 80%, demonstrating the tangible business value of moving beyond static, rule-based detection.
Contextual synergy automates data classification, remediation, and enforces access controls, thus enabling granular policies – allowing legal documents to be shared only with approved parties, for example – while supporting data lifecycle management, Sullivan explained.
Data classification helps identify the type of data, such as legal, HR or finance documents, which in turn provides a helping hand to business owners who want to make informed decisions about retention, access, and protection.
Unlike traditional systems – that mostly rely on outdated approaches that simply classify sensitive data while leaving its further handling unknown – adaptive intelligence provides contextual data insights that SOC teams can use to accelerate investigation and response across their security stack. Particularly semantic Intelligence platforms enhance SOC operations by enabling context-rich alert triage and prioritization, helping teams focus on the most critical threats first.
“Context enables more precise control over data, allowing teams to set tailored sharing permissions that go beyond what traditional classification labels can manage,” noted Sullivan.
Enabling secure adoption of generative AI
While traditional classification labels apply broad rules that may restrict business activities, contextual understanding and processing allow for more granular and flexible data management. This, Sullivan observed, enables the creation of specific rules for different data owners or departments, while businesses can prioritize alerts more effectively by understanding the context of accessed files, reducing false positives, and focusing on incidents that truly matter.
Context-aware intelligence, then, acts here as a sophisticated filter that distinguishes routine data transfers from potential breaches by analyzing user intent and data sensitivity in real-time. By applying these ‘just-in-time’ protections, organizations can reduce false-positive security alerts by up to 30%, ensuring sensitive information remains shielded without disrupting high-speed development workflows, according to another 2025 report.
By monitoring data flowing through generative AI tools in real time, context-aware AI agents provide automated oversight of sensitive financial data and enable safe AI adoption rather than banning these tools.
Conversely, via automated data oversight, security teams are given visibility into AI-driven data access – which allows firms like Concentric AI to embrace generative AI securely rather than restricting it.
As Ajay Agrawal, founder and CEO of contract lifecycle management platform Sirion noted, this evolution is already tangible across enterprise data functions: “especially as intelligence becomes a part of the system itself, where data, context, and intent come together to guide every decision, every agreement, every action.”
This approach mirrors the broader shift toward adaptive, intelligent security that scales across cloud and generative AI environments, rapidly evolving in contrast to traditional systems that merely process data at a superficial level without developing organizational data literacy.
Article Co-Authored by Liubov Romanova
