During the U.S. and Israeli strikes against Iranian military, security, and nuclear sites, a debate emerged among Iranian nationals and Iran watchers—both inside and outside the country—about the possibility of regime change and, more pointedly, whether such military action could trigger mass protests akin to those that helped topple the Shah in 1979. While the notion that civilians under bombardment—despite Israeli pre-warnings—might pour into the streets in defiance of a repressive regime may seem implausible, the question of protest remains ever-present in conversations about Iran’s political future.
Indeed, the Islamic Republic has witnessed a steady intensification of public unrest in recent years. From the Green Movement to the waves of protest in 2017, 2019, and the nationwide uprisings sparked by the killing of Mahsa Amini at the hands of Iranian security forces in 2022, Iranians have repeatedly shown that the thirst for change has not waned. What has remained constant, however, is the element of surprise. Despite mounting social, economic, and political pressure—and increasingly bold public expressions aimed directly at the regime’s leadership—outside observers continue to be caught off guard. This is not a new phenomenon: the Intelligence Community famously underestimated the revolutionary momentum of 1979, despite ample warning signs. A similar pattern of misreading or dismissing early indicators persists today.
This recurring failure points to a deeper systemic issue: the inability to harness open-source intelligence (OSINT) in a way that allows for early detection and assessment of protest movements. The data is there—public media content and social media posts, unencrypted communications, economic indicators, cultural signals, and even the conversations of regime elites and independent experts. What’s missing is a mechanism to process these signals into coherent, forward-looking analysis—not to predict unrest with precision, but to offer policymakers timely, contextual warnings so they can prepare for its possibility and scope.
In a world where information is abundant but insight remains scarce, OSINT should be at the forefront of efforts to track the internal dynamics of authoritarian states. Yet it remains sidelined—fragmented across institutions, reactive rather than anticipatory, and too often deployed only after a crisis has begun. Reports tend to be published once protests are already underway or have subsided, offering little value to policymakers who need to be prepared in their responses and strategies. The fragmentation is such that these analyses routinely fail to prepare decision-makers for the next wave. This is not a failure of data collection or digital tools—it’s a failure of integration, prioritization, and institutional imagination.
Iran is not an isolated case. The broader challenge of forecasting unrest in authoritarian settings lies in decoding signals that are deliberately obscured. These regimes excel at narrative control, internet blackouts, and disinformation campaigns. Citizens, in turn, adapt by using coded language, symbolism, satire, and silence. Analyzing such environments requires more than algorithms; it demands deep cultural literacy, interdisciplinary frameworks, and a continuous process of interpretation.
To shift from reaction to prediction, the Intelligence Community—and even external think tanks—must move away from one-off responses and toward sustainable systems. That means developing always-on collection strategies centered around key structural questions: Are economic grievances deepening? Are regime-affiliated media outlets shifting tone? Are generational or ethnic divisions intensifying? These questions can be grounded in well-established social theories that link protest potential to the accumulation of grievances.
At the same time, we need centralized, AI-supported platforms capable of aggregating and modeling sentiment trends across multiple domains—from open public discourse to semi-closed platforms like Telegram or domestic networks that may go dark during unrest. The goal is not to monitor everything at once but to focus on early indicators of mobilization, especially those that tend to precede flashpoints.
Access remains a major hurdle. Too many analysts focus only on what is publicly visible, while the most crucial conversations often take place behind firewalls, on encrypted channels, or in spaces that vanish during state-imposed blackouts. While it is essential to equip collection teams with tools to ethically access these spaces, the true value lies in sustained monitoring—establishing a baseline of normal discourse and then identifying meaningful deviations before protests erupt.
Equally important is investing in native-language and culturally fluent analysts—experts who not only understand the idioms, metaphors, sarcasm, and memes used in these societies but are also attuned to the constraints under which local journalists and commentators operate. Analysts must be able to infer meaning from absence, understand which narratives are suppressed, and distinguish between self-censorship, state pressure, and organic discourse. They must also be familiar with the political leanings of domestic media outlets in order to interpret elite conversations with greater accuracy. In such environments, what isn’t said often matters as much as what is.
Credibility presents another challenge. Disinformation campaigns, regime-linked bots, and propaganda outlets flood the information space with distortions. Without strong signal validation and filtering capabilities, OSINT analysis risks being skewed by noise. Analysts need better training and more sophisticated tools to verify what they are seeing—and to distinguish genuine grassroots sentiment from coordinated state messaging.
Ultimately, OSINT must be embedded in the daily rhythm of strategic planning. It cannot remain a reactive tool used only during crises. Instead, it should be institutionalized as a source of continuous early warning and policy insight. This shift requires a cultural transformation within intelligence and diplomatic circles—one that values open-source information not as a secondary input, but as a core strategic asset.
Iran has shown, time and again, that protest is not an anomaly—it is a recurring feature of its political life. Each cycle arrives faster, with greater intensity, and broader demands. Waiting to analyze these moments after they occur is no longer viable. The same dynamic holds true across other authoritarian states where pressure is building beneath the surface. The goal isn’t to predict the exact moment when protests will break out. The goal is to develop enough situational awareness to detect when conditions for unrest are taking shape—and to be ready, not surprised, when they do.