COMMENTARY: Security teams have never had more data at their disposal – or more difficulty using it with confidence.Every system claims it’s the “source of truth,” yet each tool captures only a slice of reality. One dashboard says a device complies, while another flags it as exposed. Orphaned identities linger, SaaS apps multiply, and governance can’t keep pace. The result: plenty of telemetry, but very little clarity.[SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Read more Perspectives here.]This tension has become more urgent in the era of AI-driven security operations. Machine reasoning can accelerate investigations, automate repetitive decisions, and surface patterns that humans would overlook. But AI engines behave like perfectly obedient, yet dangerously literal, analysts. They accept whatever inputs they are given and act instantly, whether those inputs are accurate, outdated, or contradictory. If a model receives flawed context, it will confidently deliver flawed actions.In the AI era today, security teams need a parallel discipline: context engineering – the practice of transforming scattered, inconsistent asset data into coherent, trustworthy, decision-ready intelligence. AI researchers have long recognized that model quality depends on carefully curated inputs. Cybersecurity teams now face that same challenge at enterprise scale.
Why context engineering matters now
Security operations have been shaped by constraints with limited time, fragmented tools, inconsistent naming conventions, and a constantly shifting attack surface. Analysts learn to compensate by validating assumptions manually. AI, by contrast, does not pause to question whether a dataset is stale or whether an asset’s owner is missing. As organizations apply AI across workflows, from triage to identity governance to vulnerability prioritization, they must ensure the underlying context is complete, aligned, and continuously refreshed.The question isn’t which system represents the authoritative source, but how to unify the right context across all of them. Identity providers can’t represent the full environment alone. Context engineering requires synthesizing security, business, and threat data at the aggregate rather than elevating one domain over another.While every enterprise environment looks different, five practices consistently determine whether teams can convert raw asset signals into reliable intelligence:- Comprehensive discovery across control planes: Every asset leaves traces across multiple systems. Discovery today means more than perimeter scanning: it requires maintaining persistent, API-level connections across device management tools, identity systems, cloud platforms, and SaaS providers. Don’t think of visibility as the finish line, but as the foundation for every downstream action. The difficulty lies not in whether the data exists, but in continuously collecting it across dozens of rapidly evolving tools.
- Conflict resolution through correlation: Raw telemetry delivers inherently inconsistent reads. Different tools assign different identifiers, capture timestamps differently, or represent the same asset in multiple ways. Context engineering requires a methodical correlation process that determines which signals refer to the same entity. Over-correlation merges distinct assets; under-correlation multiplies duplicates. At scale, neither a single field nor a single system can serve as a “golden record.” A confidence-based reconciliation process becomes essential.
- Normalization for reliable interoperability: Even when two tools capture the same information, their schemas rarely match. Normalization transforms these mismatched fields into a unified language so that queries and workflows behave predictably. Because schemas drift constantly, we must treat normalization as a continuous engineering practice, not a one-time mapping exercise.
- Timely enrichment with external intelligence: Internal asset data becomes outdated quickly. New vulnerabilities emerge, software versions reach end-of-life, and threat actors shift tactics. Enrichment injects third-party intelligence so that asset context reflects the current risk landscape, not the environment as it looked last week. Effective enrichment requires careful layering of threat intel, vulnerability data, and software lifecycle information to illuminate decisions without overwhelming teams with noise.
- Relationship modeling to understand exposure paths: Modern attack surfaces are defined less by individual assets and more by the relationships between them. Devices authenticate users, services depend on identities, workloads call external APIs. Mapping these relationships creates a living graph of how attackers could move through the environment and how defenders can stop them. These models must scale to millions of relationships and adapt as environments change. When done well, a single action can disrupt multiple potential attack paths at once.
