Our AI strategy for preemptive security
Nation-state cyber activity has expanded beyond government networks and traditional critical infrastructure to target the commercial sector. Geopolitical operations are increasingly compromising organizations based on their position in supply chains, the data they manage, the identities they broker, the regions they operate in, and the customers they enable.
In many cases, the business isn’t the final objective—it’s the access path. For boards, this reframes “Why us?” from an industry-only view to one based on strategic relevance; a mid-market software vendor, an MSP, a logistics provider, or an identity platform can become a high-value stepping stone into more sensitive environments.
This reality changes how security teams should prioritize defense. Attackers can now probe at scale, move faster, and use automation to identify and exploit weak links across business ecosystems. A security program built around vulnerability lists, a handful of assumed adversaries, and compliance cycles won’t keep pace. The practical prioritization question becomes “Which attacks are both achievable and worth the effort against our environment, given our technology stack, people, and operating model?”
Effective response starts with a clear premise: Sophisticated attackers act strategically, not opportunistically. They evaluate expected return against cost, timing, operational risk, and—especially for state-aligned groups—deniability and attribution risk. Their goals range from intellectual property theft and revenue generation (including cryptocurrency theft and fraud) to disruption, influence, and prepositioning for future conflict. A purely technical view of “Can they hack us?” misses the point. The better question is “What do they gain by acting now, and how does our organization help them achieve that?” With that lens, security investment shifts away from generic maturity checklists and toward shaping attacker economics: adding friction, increasing the likelihood of detection, and reducing the payoff of targeting your environment.
Strategic relevance often shows up indirectly. Organizations are attractive because they sit close to areas that create leverage like infrastructure, capital, communications, or identity. History offers a useful parallel; groups were monitored or infiltrated not because every member was individually important but because the group created proximity to influence and access. The cyber equivalent is a vendor with privileged connectivity, a SaaS platform embedded across enterprises, a contractor with access to regulated environments, or a company operating in a politically sensitive geography. High-profile supply-chain compromises show that attackers will invest in “routes” that unlock downstream access. For leadership, the takeaway is straightforward: Third-party relationships, integration patterns, and privileged access pathways are strategic exposure decisions, not just IT architecture choices.
Automation and AI are also changing the cost curve of offense. The near-term risk is not fully autonomous, end-to-end intrusions without humans; it’s automation that scales key parts of the attack life cycle—reconnaissance, asset discovery, technology fingerprinting, credential stuffing using leaked data, and rapid testing of exposed paths. These capabilities are boring but dangerous because they are repeatable, persistent, and cheap. As vulnerability discovery and exploit development accelerate, attackers can test more of your environment more often than most organizations can defend, especially when assurance still relies on annual audits, periodic penetration tests, and slow remediation cycles. Speed and scale compress the defender’s window; the time between exposure and exploitation is shrinking while the number of simultaneous probes is increasing.
A practical shift is moving from indicator-led thinking to behavior-led detection and response. Indicators (hashes, domains, and signatures) are brittle and often arrive late. Behaviors (identity misuse, unusual enumeration, privilege escalation patterns, lateral movement paths, and unexpected access to sensitive systems) are more durable and map directly to attacker progress. Behavior-led detection forces clarity on what “normal” looks like across endpoints, cloud control planes, SaaS, and identity providers, since attackers increasingly win by abusing legitimate access rather than deploying obvious malware. Designing detections around behaviors also drives response design around decision points: what triggers escalation, which containment actions are acceptable, and which business processes must continue during disruption.
This connects to a second critical distinction: Vulnerability is not the same as targetability. A system can be severely vulnerable and still be strategically irrelevant. Another system can have mundane weaknesses yet sit directly on the path to something that matters—customer data, intellectual property, privileged identities, payment flows, or operational control. Targetability asks why an attacker would invest effort and what they can achieve from a given foothold; can they pivot to a crown-jewel database, surveil or impersonate a key employee, or reach a control plane that governs many other assets? This framing improves prioritization by tying remediation, segmentation, and monitoring to attacker outcomes rather than generic severity scores.
Keeping pace requires assurance to be continuous in practice, not just in language. Point-in-time assessments and annual certifications can create a false sense of safety in an environment where attackers validate your controls every day. Continuous validation is a program, not a single tool. Vulnerability disclosure programs and bug bounties provide ongoing external pressure testing, red and purple teaming validate detection and response paths, and automated exposure testing repeatedly checks whether controls still work after changes in infrastructure and code. The operating principle is simple: A control that hasn’t been tested is a belief, a detection that hasn’t been exercised is a hypothesis, and a threat model that hasn’t been validated is documentation—not resilience.
A practical response starts by updating how risk is defined and operationalized. Likelihood-and-impact models should incorporate geopolitical context and attacker objectives, since external events can quickly change who is targeted and why. Organizations also need to separate vulnerability from targetability; the issue is not just where weaknesses exist but whether they enable a credible path to an attacker’s goals—data theft, access, disruption, or downstream compromise. This means that organizations must reduce machine-exploitable exposure by default by shrinking internet-facing attack surface, hardening identity and privileged access, constraining lateral movement, and designing environments so that compromise is noisy and detectable. Shift from episodic assurance to continuous validation by testing whether controls and assumptions hold under real conditions, measuring detection and response speed, and prioritizing remediation based on the pathways that matter most.
Resilience also depends on executive readiness. In an incident, leaders need decision-grade information—business impact, what must stop, what must continue, notification obligations, evidence preservation, and a communications plan for regulators, customers, and the press. These decisions should be rehearsed through drills that mirror real pressure: geopolitical escalation, supplier compromise, being used as a stepping stone in a customer incident, IP theft, or false attribution designed to create confusion. This preparation reduces cost, shortens decision cycles, and limits reputational damage, turning security into a tested capability aligned with business operations.
A few experts from Bugcrowd recently tackled this topic in-depth on a webinar. You can watch the recording to learn more.